From 17995c470075e7dd2f4b33b3d7578300d3b2458e Mon Sep 17 00:00:00 2001 From: xiaying Date: Wed, 22 Jan 2025 14:47:50 +0800 Subject: [PATCH] MNN:Sync: Sync Internal 3.0.4 --- CMakeLists.txt | 34 +- docs/compile/cmake.md | 1 - docs/contribute/backend.md | 17 + docs/index.rst | 2 +- docs/inference/module.md | 1 - docs/inference/npu.md | 57 + docs/inference/session.md | 4 +- docs/tools/compress.md | 39 +- docs/tools/convert.md | 5 +- docs/tools/script.md | 70 - docs/transformers/diffusion.md | 36 +- docs/transformers/llm.md | 44 +- express/Executor.cpp | 35 +- express/NeuralNetWorkOp.cpp | 9 +- express/RuntimeAttr.hpp | 14 +- express/module/Module.cpp | 24 +- express/module/ModuleInside.hpp | 35 + express/module/NMSModule.cpp | 2 +- express/module/NMSModule.hpp | 3 +- express/module/PipelineModule.cpp | 28 +- express/module/StaticModule.cpp | 29 +- express/module/StaticModule.hpp | 8 +- express/module/WhileModule.cpp | 2 +- express/module/WhileModule.hpp | 3 +- include/MNN/Interpreter.hpp | 8 +- include/MNN/MNNDefine.h | 3 +- include/MNN/MNNForwardType.h | 34 +- include/MNN/expr/Module.hpp | 19 +- pymnn/pip_package/MNN/expr/__init__.py | 2 +- pymnn/pip_package/MNN/llm/__init__.py | 62 +- pymnn/pip_package/MNN/numpy/__init__.py | 7 +- pymnn/pip_package/README.md | 2 +- pymnn/pip_package/build_manylinux2014.sh | 2 +- pymnn/pip_package/setup.py | 14 +- pymnn/src/llm.h | 40 + source/backend/arm82/Arm82Functions.cpp | 73 +- ...MinMax_ARM82.S => CountMinMaxValue_FP16.S} | 2 +- .../arm64/low_memory/MNNAbsMaxFP16_Pack4.S | 226 + ...{MNNAbsMaxFP16.S => MNNAbsMaxFP16_pack8.S} | 6 +- .../low_memory/MNNDynamicQuantFP16_Pack4.S | 492 + ...uantFP16.S => MNNDynamicQuantFP16_Pack8.S} | 5 +- .../arm64/low_memory/MNNGeneralIm2col_Arm82.S | 260 + .../arm64/low_memory/MNNGeneralIm2col_Arm86.S | 139 + source/backend/cpu/CPUBackend.cpp | 12 +- source/backend/cpu/CPUDeconvolution.cpp | 5 + source/backend/cpu/CPUEltwise.cpp | 1 + source/backend/cpu/CPURaster.cpp | 10 +- source/backend/cpu/CPURuntime.cpp | 2 - source/backend/cpu/CPUStft.cpp | 7 +- source/backend/cpu/arm/arm32/MNNExpC8.S | 16 +- source/backend/cpu/arm/arm64/MNNExpC8.S | 11 +- .../arm/arm64/MNNPackC4Int8ForMatMulA_ARM82.S | 68 +- .../arm/arm64/MNNPackC4Int8ForMatMulA_ARM86.S | 147 +- .../cpu/arm/arm64/low_memory/MNNAbsMaxFP32.S | 6 +- .../arm64/low_memory/MNNDynamicQuantFP32.S | 6 +- .../low_memory/MNNGeneralIm2col_Fp32Arm82.S | 140 + .../low_memory/MNNGeneralIm2col_Fp32Arm86.S | 264 + source/backend/cpu/bf16/BF16Functions.cpp | 2 - .../backend/cpu/compute/CommonOptFunction.cpp | 174 +- .../backend/cpu/compute/CommonOptFunction.h | 17 +- .../cpu/compute/ConvInt8TiledExecutor.cpp | 541 +- .../cpu/compute/ConvInt8TiledExecutor.hpp | 3 +- .../cpu/compute/Convolution1x1Strassen.cpp | 34 +- .../cpu/compute/Convolution1x1Strassen.hpp | 5 +- .../cpu/compute/ConvolutionFloatFactory.cpp | 5 +- .../cpu/compute/ConvolutionTiledExecutor.cpp | 17 +- .../compute/DenseConvolutionTiledExecutor.cpp | 69 +- .../cpu/compute/IdstConvolutionInt8.cpp | 8 +- source/backend/cpu/compute/ResizeFunction.h | 3 - .../cpu/compute/StrassenMatmulComputor.cpp | 27 +- .../cpu/compute/StrassenMatmulComputor.hpp | 7 +- source/backend/cpu/x86_x64/AVX2Functions.cpp | 2 - .../cpu/x86_x64/FunctionDispatcher.cpp | 2 - source/backend/cpu/x86_x64/avx/GemmInt8.cpp | 8 +- .../backend/cpu/x86_x64/avx/MathFunctions.cpp | 11 +- .../cpu/x86_x64/avxfma/MathFunctions.cpp | 11 +- .../backend/cpu/x86_x64/sse/MathFunctions.cpp | 11 +- source/backend/metal/AllShader.cpp | 995 +- source/backend/metal/ConvSimdGroupShader.hpp | 1087 + .../metal/LayerNormSimdGroupShader.hpp | 255 + source/backend/metal/MNNMetalContext.mm | 30 +- source/backend/metal/MetalAttention.mm | 282 +- source/backend/metal/MetalAttentionShader.hpp | 48 + source/backend/metal/MetalBackend.hpp | 2 + source/backend/metal/MetalBackend.mm | 24 +- source/backend/metal/MetalConvolution.mm | 1 + source/backend/metal/MetalConvolution1x1.mm | 110 +- source/backend/metal/MetalLayerNorm.mm | 67 +- source/backend/metal/MetalPReLU.hpp | 5 +- source/backend/metal/MetalPReLU.mm | 27 +- source/backend/metal/MetalRaster.mm | 3 + source/backend/metal/MetalScale.hpp | 7 +- source/backend/metal/MetalScale.mm | 35 +- source/backend/metal/MetalUnary.mm | 4 +- .../metal/shader/MetalConvolution.metal | 14 +- .../metal/shader/MetalConvolution1x1.metal | 784 - source/backend/metal/shader/MetalDefine.metal | 3 - .../backend/metal/shader/MetalLayerNorm.metal | 225 - source/backend/metal/shader/MetalScale.metal | 9 +- .../backend/metal/shader/MetalSoftmax.metal | 36 - source/backend/nnapi/execution/NNAPIUnary.cpp | 2 +- source/backend/opencl/core/OpenCLGemmTune.cpp | 65 +- .../opencl/core/runtime/OpenCLRuntime.cpp | 42 +- .../buffer/AttentionBufExecution.cpp | 100 +- .../buffer/AttentionBufExecution.hpp | 2 +- .../execution/buffer/ConvBufExecution.cpp | 6 +- .../execution/buffer/ConvBufExecution.hpp | 1 + .../buffer/ConvBufLowMemoryExecution.cpp | 97 +- .../execution/buffer/RasterBufExecution.cpp | 198 +- .../execution/buffer/RasterBufExecution.hpp | 13 +- .../opencl/execution/cl/attention_buf.cl | 4 +- .../execution/cl/buffer_convert_quant.cl | 43 + .../opencl/execution/cl/conv_2d_int_buf.cl | 118 +- .../opencl/execution/cl/gemm_conv1x1_buf.cl | 108 +- .../opencl/execution/cl/gemv_conv1x1_buf.cl | 69 +- .../opencl/execution/cl/opencl_program.cc | 340 +- source/backend/vulkan/CMakeLists.txt | 2 +- .../vulkan/buffer/backend/VulkanBackend.cpp | 2 +- .../buffer/execution/VulkanLayernorm.cpp | 13 +- .../backend/vulkan/component/VulkanDevice.cpp | 14 + .../backend/vulkan/component/VulkanDevice.hpp | 5 + .../vulkan/component/VulkanPipeline.cpp | 1 + .../vulkan/component/VulkanPipeline.hpp | 3 + .../vulkan/component/VulkanQueryPool.cpp | 14 +- .../vulkan/image/backend/VulkanBackend.cpp | 113 +- .../vulkan/image/backend/VulkanBackend.hpp | 5 + .../vulkan/image/compiler/AllShader.cpp | 30885 ++++++++++------ .../vulkan/image/compiler/VulkanShaderMap.cpp | 39 +- .../image/execution/VulkanConvolution.cpp | 33 +- .../image/execution/VulkanConvolution.hpp | 7 +- .../image/execution/VulkanConvolution1x1.cpp | 152 + .../image/execution/VulkanConvolutionImpl.cpp | 8 + .../execution/VulkanMatrixMultier4x4.cpp | 7 +- .../execution/VulkanMatrixMultier4x4.hpp | 3 +- .../image/execution/Vulkanconvolution1x1.hpp | 38 + .../image/execution/glsl/convolution1x1.comp | 69 + .../execution/glsl/convolution1x1_c8w4.comp | 163 + .../execution/glsl/convolution1x1_w4.comp | 121 + .../execution/glsl/convolutionDepthwise.comp | 23 +- .../glsl/convolutionDepthwiseMali.comp | 62 - .../glsl/convolutionDepthwise_s1d1_w2.comp | 37 +- .../image/execution/glsl/gemm16x16.comp | 2 +- .../vulkan/image/execution/glsl/macro.json | 44 +- .../backend/vulkan/image/shaders/AllShader.h | 78 +- .../backend/vulkan/runtime/VulkanRuntime.cpp | 75 + .../backend/vulkan/runtime/VulkanRuntime.hpp | 37 + .../backend/vulkan/runtime/vulkan_wrapper.cpp | 3 + source/backend/vulkan/schema/VKCache.fbs | 22 + .../vulkan/schema/current/VKCache_generated.h | 430 + source/backend/vulkan/schema/generate.sh | 19 + source/backend/vulkan/vulkan/vulkan_wrapper.h | 1 + source/core/Backend.hpp | 2 + source/core/BufferAllocator.cpp | 22 +- source/core/BufferAllocator.hpp | 4 +- source/core/ConvolutionCommon.hpp | 3 +- source/core/IDSTEncoder.hpp | 8 + source/core/MNNFileUtils.cpp | 21 +- source/core/MNNFileUtils.h | 11 + source/core/Pipeline.cpp | 43 +- source/core/Pipeline.hpp | 2 + source/core/Session.cpp | 3 + source/geometry/GeometryComputerUtils.cpp | 4 + source/geometry/GeometryComputerUtils.hpp | 3 +- source/geometry/GeometryConv2D.cpp | 20 +- source/geometry/GeometryConvUtils.cpp | 21 + source/geometry/GeometryELU.cpp | 3 +- source/shape/ShapeAttention.cpp | 10 + source/shape/ShapeConvolution3D.cpp | 8 +- test.sh | 20 + test/MNNTestSuite.h | 18 - test/TestUtils.h | 2 +- test/expr/MMapTest.cpp | 58 + test/expr/ModuleTest.cpp | 146 +- test/model/MobileNetTest.cpp | 6 +- test/op/ConvertTest.cpp | 2 + test/op/ConvolutionTest.cpp | 32 +- test/op/TopKV2Test.cpp | 6 +- test/speed/HybridConvSpeedTest.cpp | 12 +- tools/converter/forward.json | 3 +- tools/converter/include/commonKit.hpp | 18 - tools/converter/include/config.hpp | 5 +- tools/converter/include/options.hpp | 30 - tools/converter/include/writeFb.hpp | 2 +- tools/converter/source/common/Common.cpp | 42 - tools/converter/source/common/Common.hpp | 25 - tools/converter/source/common/CommonUtils.cpp | 78 + tools/converter/source/common/CommonUtils.hpp | 19 +- .../source/common/FullQuantAndCoding.cpp | 3 + .../source/common/WeightQuantAndCoding.cpp | 65 +- tools/converter/source/common/cli.cpp | 167 +- .../source/common/convertToStaticModel.cpp | 2 +- tools/converter/source/common/options.cpp | 33 - tools/converter/source/common/writeFb.cpp | 87 +- .../source/compression/MNN_compression.proto | 12 + .../source/compression/PipelineBuilder.cpp | 132 - .../source/compression/PipelineBuilder.hpp | 72 - .../generated/MNN_compression.pb.cc | 782 +- .../generated/MNN_compression.pb.h | 787 +- tools/converter/source/onnx/CastOnnx.cpp | 3 + tools/converter/source/onnx/EluOnnx.cpp | 2 +- .../optimizer/merge/Conv1dSqueezeMove.cpp | 16 +- .../merge/ConvBNReluFuseToConvInt8.cpp | 16 +- .../optimizer/merge/Convolution3DTurn2D.cpp | 145 + .../source/optimizer/merge/MergeHelpers.cpp | 13 +- .../onnxextra/OnnxBatchNormMerge.cpp | 88 +- .../onnxextra/OnnxConvolutionMerge.cpp | 40 +- .../source/tensorflow/TfModelOptimizer.cpp | 689 - tools/converter/source/tensorflow/TfUtils.cpp | 483 - tools/converter/source/tensorflow/TfUtils.hpp | 150 - tools/converter/tools/auto_quant.py | 263 + .../converter/tools/user_quant_modify_demo.py | 54 + .../converter/user_provide_quant_params.json | 132 +- tools/cpp/ModuleBasic.cpp | 4 +- tools/cv/CMakeLists.txt | 140 +- tools/quantization/calibration.cpp | 10 +- tools/quantization/calibration.hpp | 4 +- tools/script/apply_gptq.py | 201 - tools/script/convertTfTest.py | 5 +- tools/script/testMNNFromOnnx.py | 2 + tools/train/source/nn/NN.cpp | 1 + transformers/diffusion/CMakeLists.txt | 14 - transformers/diffusion/engine/CMakeLists.txt | 24 + .../diffusion/engine/diffusion_demo.cpp | 73 + .../include/diffusion/diffusion.hpp} | 36 +- .../{ => engine}/scheduler/alphas.txt | 0 .../src/diffusion.cpp} | 223 +- .../diffusion/{ => engine/src}/tokenizer.cpp | 91 +- .../diffusion/{ => engine/src}/tokenizer.hpp | 33 +- transformers/diffusion/main.cpp | 47 - transformers/llm/engine/CMakeLists.txt | 1 - transformers/llm/engine/audio_demo.cpp | 4 +- transformers/llm/engine/embedding_demo.cpp | 67 +- transformers/llm/engine/include/llm/llm.hpp | 1 + transformers/llm/engine/src/llm.cpp | 61 +- transformers/llm/engine/src/llmconfig.hpp | 32 + transformers/llm/export/llmexport.py | 2598 +- transformers/llm/export/utils/__init__.py | 0 transformers/llm/export/utils/audio.py | 140 + .../llm/export/utils/awq_quantizer.py | 815 + transformers/llm/export/utils/custom_op.py | 59 + transformers/llm/export/utils/gptq.py | 147 + .../llm/export/utils/lora.py | 143 +- .../llm/export/utils/mnn_converter.py | 416 + transformers/llm/export/utils/model_mapper.py | 295 + transformers/llm/export/utils/onnx.py | 70 + .../llm/export/utils/onnx_rebuilder.py | 70 + transformers/llm/export/utils/spinner.py | 31 + transformers/llm/export/utils/transformers.py | 333 + transformers/llm/export/utils/vision.py | 394 + 249 files changed, 32828 insertions(+), 20046 deletions(-) create mode 100644 docs/inference/npu.md delete mode 100644 docs/tools/script.md create mode 100644 express/module/ModuleInside.hpp rename source/backend/arm82/asm/arm64/low_memory/{MNNCountMinMax_ARM82.S => CountMinMaxValue_FP16.S} (99%) create mode 100644 source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_Pack4.S rename source/backend/arm82/asm/arm64/low_memory/{MNNAbsMaxFP16.S => MNNAbsMaxFP16_pack8.S} (96%) create mode 100644 source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack4.S rename source/backend/arm82/asm/arm64/low_memory/{MNNDynamicQuantFP16.S => MNNDynamicQuantFP16_Pack8.S} (99%) create mode 100644 source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm82.S create mode 100644 source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm86.S create mode 100644 source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm82.S create mode 100644 source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm86.S create mode 100644 source/backend/metal/ConvSimdGroupShader.hpp create mode 100644 source/backend/metal/LayerNormSimdGroupShader.hpp create mode 100644 source/backend/vulkan/image/execution/VulkanConvolution1x1.cpp create mode 100644 source/backend/vulkan/image/execution/Vulkanconvolution1x1.hpp create mode 100644 source/backend/vulkan/image/execution/glsl/convolution1x1.comp create mode 100644 source/backend/vulkan/image/execution/glsl/convolution1x1_c8w4.comp create mode 100644 source/backend/vulkan/image/execution/glsl/convolution1x1_w4.comp delete mode 100644 source/backend/vulkan/image/execution/glsl/convolutionDepthwiseMali.comp create mode 100644 source/backend/vulkan/schema/VKCache.fbs create mode 100644 source/backend/vulkan/schema/current/VKCache_generated.h create mode 100755 source/backend/vulkan/schema/generate.sh create mode 100644 test/expr/MMapTest.cpp delete mode 100644 tools/converter/include/commonKit.hpp delete mode 100644 tools/converter/include/options.hpp delete mode 100644 tools/converter/source/common/Common.cpp delete mode 100644 tools/converter/source/common/Common.hpp create mode 100644 tools/converter/source/common/CommonUtils.cpp delete mode 100644 tools/converter/source/common/options.cpp delete mode 100644 tools/converter/source/compression/PipelineBuilder.cpp delete mode 100644 tools/converter/source/compression/PipelineBuilder.hpp create mode 100644 tools/converter/source/optimizer/merge/Convolution3DTurn2D.cpp delete mode 100644 tools/converter/source/tensorflow/TfModelOptimizer.cpp create mode 100644 tools/converter/tools/auto_quant.py create mode 100644 tools/converter/tools/user_quant_modify_demo.py delete mode 100644 tools/script/apply_gptq.py delete mode 100644 transformers/diffusion/CMakeLists.txt create mode 100644 transformers/diffusion/engine/CMakeLists.txt create mode 100644 transformers/diffusion/engine/diffusion_demo.cpp rename transformers/diffusion/{pipeline.hpp => engine/include/diffusion/diffusion.hpp} (55%) rename transformers/diffusion/{ => engine}/scheduler/alphas.txt (100%) rename transformers/diffusion/{pipeline.cpp => engine/src/diffusion.cpp} (64%) rename transformers/diffusion/{ => engine/src}/tokenizer.cpp (91%) rename transformers/diffusion/{ => engine/src}/tokenizer.hpp (70%) delete mode 100644 transformers/diffusion/main.cpp create mode 100644 transformers/llm/export/utils/__init__.py create mode 100644 transformers/llm/export/utils/audio.py create mode 100644 transformers/llm/export/utils/awq_quantizer.py create mode 100644 transformers/llm/export/utils/custom_op.py create mode 100644 transformers/llm/export/utils/gptq.py rename tools/script/apply_lora.py => transformers/llm/export/utils/lora.py (62%) create mode 100644 transformers/llm/export/utils/mnn_converter.py create mode 100644 transformers/llm/export/utils/model_mapper.py create mode 100644 transformers/llm/export/utils/onnx.py create mode 100644 transformers/llm/export/utils/onnx_rebuilder.py create mode 100644 transformers/llm/export/utils/spinner.py create mode 100644 transformers/llm/export/utils/transformers.py create mode 100644 transformers/llm/export/utils/vision.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 6048bf4d3..747fe9a0b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -53,7 +53,6 @@ option(MNN_DEBUG_TENSOR_SIZE "Enable Tensor Size" OFF) option(MNN_GPU_TRACE "Enable MNN Gpu Debug" OFF) option(MNN_SUPPORT_RENDER "Enable MNN Render Ops" OFF) option(MNN_SUPPORT_TRANSFORMER_FUSE "Enable MNN transformer Fuse Ops" OFF) -option(MNN_PORTABLE_BUILD "Link the static version of third party libraries where possible to improve the portability of built executables" OFF) option(MNN_SEP_BUILD "Build MNN Backends and expression separately. Only works with MNN_BUILD_SHARED_LIBS=ON" ON) option(NATIVE_LIBRARY_OUTPUT "Native Library Path" OFF) option(NATIVE_INCLUDE_OUTPUT "Native Include Path" OFF) @@ -174,9 +173,6 @@ ENDIF() IF(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND NOT MNN_BUILD_SHARED_LIBS AND NOT (MSVC OR WIN32)) SET(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS}") SET(MNN_SEP_BUILD OFF CACHE BOOL "" FORCE) - IF(MNN_BUILD_CONVERTER) - SET(MNN_PORTABLE_BUILD ON CACHE BOOL "" FORCE) - ENDIF() ENDIF() if(MNN_FORBID_MULTI_THREAD) @@ -515,6 +511,7 @@ endif() if ((NOT MSVC) AND MNN_HIDDEN) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility-inlines-hidden -fvisibility=hidden") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fvisibility=hidden") + set(CMAKE_ASM_FLAGS "${CMAKE_ASM_FLAGS} -fvisibility=hidden") # Omit frame pointer may cause difficult debug set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fomit-frame-pointer") endif() @@ -701,6 +698,16 @@ IF(MNN_TENSORRT) list(APPEND MNN_EXTRA_DEPENDS ${MNN_TRT_LIBS}) ENDIF() +IF(MNN_BUILD_OPENCV) +add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/tools/cv) +IF(MNN_SEP_BUILD) + list(APPEND MNN_DEPS MNNOpenCV) +ELSE() + list(APPEND MNN_TARGETS MNNOpenCV) + list(APPEND MNN_OBJECTS_TO_LINK $) +ENDIF() +ENDIF() + IF(MNN_BUILD_LLM) # add_definitions(-DMNN_BUILD_LLM) include(${CMAKE_CURRENT_LIST_DIR}/transformers/llm/engine/CMakeLists.txt) @@ -709,6 +716,13 @@ IF(MNN_BUILD_LLM) list(APPEND MNN_OBJECTS_TO_LINK $) ENDIF() ENDIF() +IF(MNN_BUILD_DIFFUSION AND MNN_BUILD_OPENCV AND MNN_IMGCODECS) + include(${CMAKE_CURRENT_LIST_DIR}/transformers/diffusion/engine/CMakeLists.txt) + IF(NOT MNN_SEP_BUILD) + list(APPEND MNN_TARGETS diffusion) + list(APPEND MNN_OBJECTS_TO_LINK $) + ENDIF() +ENDIF() IF(MNN_SEP_BUILD) add_library(MNN SHARED ${CMAKE_CURRENT_LIST_DIR}/cmake/dummy.cpp ${MNN_OBJECTS_TO_LINK} ${MNN_PUB_HDRS} ${MNN_EXPR_PUB_HDRS} ${MNN_EXTRA_HEADERS}) @@ -774,13 +788,7 @@ IF(WIN32 AND MNN_BUILD_CONVERTER AND MNN_BUILD_SHARED_LIBS) target_link_libraries(MNN PUBLIC ${Protobuf_LIBRARIES}) ENDIF() # Merge MNN/MNNExpress/MNNOpenCV and other backends into one .lib/.dll on Windows -add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/tools/cv) -IF(MNN_BUILD_OPENCV AND NOT MNN_SEP_BUILD) - IF(MSVC) - target_compile_definitions(MNNOpenCV PRIVATE "-DBUILDING_MNN_DLL" INTERFACE "-DUSING_MNN_DLL") - ENDIF() - target_sources(MNN PRIVATE $) -ENDIF() + add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/tools/audio) IF(MNN_BUILD_AUDIO AND NOT MNN_SEP_BUILD) IF(MSVC) @@ -820,9 +828,7 @@ list(REMOVE_ITEM MNN_TARGETS MNN) IF(MNN_BUILD_DEMO) include(${CMAKE_CURRENT_LIST_DIR}/demo/exec/CMakeLists.txt) ENDIF() -IF(MNN_BUILD_DIFFUSION AND MNN_BUILD_OPENCV AND MNN_IMGCODECS) -include(${CMAKE_CURRENT_LIST_DIR}/transformers/diffusion/CMakeLists.txt) -ENDIF() + IF(MNN_BUILD_TOOLS) include(${CMAKE_CURRENT_LIST_DIR}/tools/cpp/CMakeLists.txt) ENDIF() diff --git a/docs/compile/cmake.md b/docs/compile/cmake.md index a4d45bca4..f25d4ede7 100644 --- a/docs/compile/cmake.md +++ b/docs/compile/cmake.md @@ -20,7 +20,6 @@ MNN使用CMake构建项目，CMake中的宏定义列表如下： | MNN_DEBUG_MEMORY | 是否开启MNN内存调试，默认为`OFF` | | MNN_DEBUG_TENSOR_SIZE | 是否开启MNN tensor size调试，默认为`OFF` | | MNN_GPU_TRACE | 是否开启MNN GPU调试，默认为`OFF` | -| MNN_PORTABLE_BUILD | 尽可能链接第三方库的静态版本，以提高构建的可执行文件的可移植性，默认为`OFF` | | MNN_SEP_BUILD | 是否构建MNN的后端和表达式分离版本，只在`MNN_BUILD_SHARED_LIBS=ON`时生效，默认为`ON` | | NATIVE_LIBRARY_OUTPUT | 如果构建为动态库，则指定动态库的输出路径，默认为`OFF` | | NATIVE_INCLUDE_OUTPUT | 如果构建为动态库，则指定动态库的头文件路径，默认为`OFF` | diff --git a/docs/contribute/backend.md b/docs/contribute/backend.md index caa10ee2a..c3f436608 100644 --- a/docs/contribute/backend.md +++ b/docs/contribute/backend.md @@ -179,6 +179,23 @@ virtual void onExecuteEnd() const = 0; 对于使用同一种后端，且存在先后顺序，不会同时运行的模型，MNN提供机制使其共享部分计算资源，比如线程池，内存池等等。 这部分计算资源使用Runtime存储。而Backend则由Runtime创建 +### CompileType + +Runtime 可以通过指定 CompileType ，决定 MNN 是否跳过几何计算步骤： + +``` +enum CompilerType { + // 部分执行几何计算，分解形变算子，但不分解 BatchMatMul / Gather 等算子 + Compiler_Geometry = 0, + + // 完全跳过几何计算步骤，直接使用原始算子 + Compiler_Origin = 1, + + // 完全执行几何计算，仅此模式下，可以在算子不支持时自动回退到CPU计算 + Compiler_Loop = 2, +}; +``` + ### 实现Runtime Runtime主要实现如下接口： diff --git a/docs/index.rst b/docs/index.rst index 0e9264da3..74943310d 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -42,6 +42,7 @@ inference/session inference/module inference/python + inference/npu .. toctree:: :maxdepth: 1 @@ -81,7 +82,6 @@ tools/compress tools/visual tools/python - tools/script .. toctree:: :maxdepth: 1 diff --git a/docs/inference/module.md b/docs/inference/module.md index 3fea3589a..6e2d797a6 100644 --- a/docs/inference/module.md +++ b/docs/inference/module.md @@ -92,7 +92,6 @@ rtmgr->setMode(Interpreter::Session_Debug); - Interpreter::HintMode::WINOGRAD_MEMORY_LEVEL ：使用 Winograd 算法优化卷积时，内存占用倾向，默认为 3 ，若希望降低内存占用可设为 0 - Interpreter::HintMode::GEOMETRY_COMPUTE_MASK ：几何计算相关优化开关，1为区域合并，2为复合区域合并，4为使用loop算子，8为支持几何计算重计算，需要多个功能开启时把对应值叠加。默认为功能全开。 -- Interpreter::HintMode::DYNAMIC_QUANT_OPTIONS ：动态量化选项，1为 Per Batch，2为Per Tensor 。默认为2。 - Interpreter::HintMode::CPU_LITTLECORE_DECREASE_RATE ：对于 Android 设备存在大中小核的情况，大核算力到中核算力的衰减比例。默认为50（中核算力为大核的50%） diff --git a/docs/inference/npu.md b/docs/inference/npu.md new file mode 100644 index 000000000..ecf3b9a7d --- /dev/null +++ b/docs/inference/npu.md @@ -0,0 +1,57 @@ +# NPU 及相应后端使用说明 + +目前 MNN 支持通过如下后端调用部分手机上的NPU能力： +- CoreML +- NNAPI +- HIAI + +目前NPU相关后端均不支持可变形状、控制流等动态模型，算子数相比CPU/GPU支持要少，建议根据NPU是否能跑通，反复调整模型结构。 + +## CoreML +适用于 Mac / iOS / iPad + +### CoreML 后端编译 +1. 编译 MNN 时打开编译宏 MNN_COREML ：-DMNN_COREML=ON +2. 编译App / 可执行程序时，增加链接 CoreML.framework + +### CoreML 后端使用 +backend type设置成：MNN_FORWARD_NN + +## NNAPI +适用于 Android 系统，高通/联发科芯片 + +### NNAPI 后端编译 +打开编译宏 MNN_NNAPI 即可 +``` +cd ${MNN} +cd project/android +mkdir build && cd build +../build_64.sh -DMNN_USE_LOGCAT=ON -DMNN_NNAPI=ON +``` + +### NNAPI 后端使用 +backend type设置成：MNN_FORWARD_NN + + +## 华为 HIAI +适用于 Android 系统， Kirlin芯片 + +### HIAI 环境准备 +1. 从如下链接下载 DDK +https://developer.huawei.com/consumer/cn/doc/hiai-Library/ddk-download-0000001053590180 + + + +2. 拷贝相对应的so和include文件到 hiai/3rdParty 目录下，如果没有3rdParty目录，新建一个： + +``` +mkdir ${MNN}/source/backend/hiai/3rdParty +cp -r ${DDK}/lib ${MNN}/source/backend/hiai/3rdParty/armeabi-v7a +cp -r ${DDK}/lib64 ${MNN}/source/backend/hiai/3rdParty/arm64-v8a +cp -r ${DDK}/include ${MNN}/source/backend/hiai/3rdParty/include +``` + +### HIAI 编译执行 +1. cmake 参数打开npu开关： -DMNN_NPU=true +2. backend type设置成：MNN_FORWARD_USER_0 +3. 执行可执行程序（需动态加载：libMNN_NPU.so, libhiai_ir_build.so, libhiai_ir.so, libhiai.so） \ No newline at end of file diff --git a/docs/inference/session.md b/docs/inference/session.md index 2e5e0b6fc..98b92e873 100644 --- a/docs/inference/session.md +++ b/docs/inference/session.md @@ -302,7 +302,7 @@ delete nhwcTensor; 通过这类拷贝数据的方式，用户只需要关注自己创建的tensor的数据布局，`copyFromHostTensor`会负责处理数据布局上的转换（如需）和后端间的数据拷贝（如需）。 -### 【不推荐】直接填充数据 +### 【已废弃】直接填充数据 ```cpp auto inputTensor = interpreter->getSessionInput(session, NULL); inputTensor->host()[0] = 1.f; @@ -594,7 +594,7 @@ delete nhwcTensor; -### 【不推荐】直接读取数据 +### 【已废弃】直接读取数据 **由于绝大多数用户都不熟悉MNN底层数据布局，所以不要使用这种方式！！！** ```cpp auto outputTensor = interpreter->getSessionOutput(session, NULL); diff --git a/docs/tools/compress.md b/docs/tools/compress.md index b80386a23..a66760028 100644 --- a/docs/tools/compress.md +++ b/docs/tools/compress.md @@ -69,7 +69,7 @@ MNN模型压缩工具提供了包括低秩分解、剪枝、量化等模型压 - 动态量化 可以通过如下方式打开MNN运行时的动态量化支持，使权值量化后的模型中卷积等核心算子使用量化计算，降低内存并提升性能 -1. 打开 MNN_LOW_MEMORY 编译宏编译 MNN （支持动态量化功能） +1. 打开 `MNN_LOW_MEMORY` 编译宏编译 MNN （支持动态量化功能） ``` cmake .. -DMNN_LOW_MEMORY=ON ``` @@ -99,6 +99,43 @@ backendConfig.precision = BackendConfig::Precision_Low; config.backendConfig = &backendConfig; ``` +### 自动压缩工具 +可使用脚本 `tools/converter/tools/auto_quant.py` 依据测试误差，自动确定量化方案。使用步骤如下： + +1. 将模型转成MNN格式，示例： +``` +./MNNConvert -f ONNX --modelFile src.onnx --MNNModel float.mnn +``` + +2. 参考[正确性校验](convert.html#id3)，构建测试文件夹 mnntest + +3. 在编译好 MNNConvert 的目录下执行脚本 +``` +# 查看参数 +python ../tools/converter/tools/auto_quant.py -h +# 压缩，产出文件 quant.mnn 及相关参数 quant.mnn.json +python ../tools/converter/tools/auto_quant.py --model float.mnn --quant_model quant.mnn --test_dir mnntest --rate 0.05 +``` + +### 自行定制方案 +若默认压缩方案无法满足精度需求，可以按如下步骤定制压缩方案。一般来说跳过一些重要的算子压缩，可以缓解精度下降的问题。 + +1. 生成模型压缩信息文件: user.json +``` +rm user.json +./MNNConvert -f ONNX --modelFile src.onnx --MNNModel dst.mnn --weightQuantBits 8 --compressionParamsFile user.json +``` + +2. 编辑 user.json ，把不需要量化的算子，bits数设为0 或者调高 bits数（不超过8）。也可以参考脚本 `tools/converter/tools/user_quant_modify_demo.py` ，批量处理压缩信息，然后执行脚本: + +``` +python3 ../tools/converter/tools/user_quant_modify_demo.py user.json user.json +``` + +3. 使用 user.json 重新转换模型 +``` +./MNNConvert -f ONNX --modelFile src.onnx --MNNModel dst.mnn --compressionParamsFile user.json +``` ## 离线量化工具 ### 离线量化工具安装 diff --git a/docs/tools/convert.md b/docs/tools/convert.md index bc869abab..bb301bd74 100644 --- a/docs/tools/convert.md +++ b/docs/tools/convert.md @@ -47,7 +47,7 @@ Usage: --weightQuantAsymmetric 与weightQuantBits结合使用，决定是否用非对称量化，默认为`true` --compressionParamsFile arg - 使用MNN模型压缩工具箱生成的模型压缩信息文件或根据用户提供的量化参数来生成对应的量化模型，量化参数文件可参考tools/converter/user_provide_quant_params.json + 使用MNN模型压缩工具箱生成的模型压缩信息文件或根据用户提供的量化参数来生成对应的量化模型，量化参数文件可参考tools/converter/user_provide_quant_params.json 。如果文件不存在，且开启了weightQuantBits等量化功能，会在相应路径生成模型压缩信息文件(json格式)，可后续编辑 --saveStaticModel 固定输入形状，保存静态模型， default: false @@ -74,8 +74,7 @@ Usage: --alignDenormalizedValue arg 可选值：{0, 1}， 默认为1, 当`float(|x| < 1.18e-38)`会被视为0 - --detectSparseSpeedUp arg - 可选值：{0, 1}， 默认为1, 会检测权重是否使用稀疏化加速 + --detectSparseSpeedUp 检测权重是否使用稀疏化加速/压缩，有可能减少模型大小，但增大模型转换时间 --saveExternalData 将权重，常量等数据存储在额外文件中，默认为0，也就是`false` diff --git a/docs/tools/script.md b/docs/tools/script.md deleted file mode 100644 index 551b8e7b1..000000000 --- a/docs/tools/script.md +++ /dev/null @@ -1,70 +0,0 @@ -# 脚本工具 -一些功能性脚本，提供各种功能。 - -## apply_gptq.py -将GPTQ的权重写入到量化的MNN权重中。 - -### 用法 -``` -usage: apply_gptq.py [-h] --mnn_graph MNN_GRAPH --mnn_weight MNN_WEIGHT --gptq_tensor GPTQ_TENSOR - -apply_gptq - -options: - -h, --help show this help message and exit - --mnn_graph MNN_GRAPH - mnn graph json path. - --mnn_weight MNN_WEIGHT - mnn weight file path. - --gptq_tensor GPTQ_TENSOR - gptq tensor path. -``` - -### 参数 -- MNN_GRAPH: 模型计算图的json文件，获取方法：`./MNNDump2Json model.mnn model.json` -- MNN_WEIGHT: 模型的权重文件，如：`gptq.mnn.weight` -- GPTQ_TENSOR: GPTQ量化后的权重文件，`model.safetensor` - -### 示例 -使用该脚本生成gptq量化的权重`gptq.mnn.weight` -```sh -cd build -./MNNDump2Json model.mnn model.json -cp model.mnn.weight gptq.mnn.weight -python ../tools/script/apply_gptq.py --mnn_graph model.json --mnn_weight gptq.mnn.weight --gptq_tensor model.safetensor -``` - -## apply_lora.py - -合并base模型的计算图和lora模型的权重文件，生成新的计算图。 - -### 用法 -```sh -usage: apply_lora.py [-h] --base BASE --lora LORA [--scale SCALE] [--fuse FUSE] [--out OUT] - -apply_lora - -options: - -h, --help show this help message and exit - --base BASE base model json path. - --lora LORA lora dir path or *.safetensors path. - --scale SCALE lora scale: `alpha/r`. - --fuse FUSE fuse A and B. - --out OUT out file name. -``` - -### 参数 -- BASE: base.json, base模型计算图的json文件，获取方法：`./MNNDump2Json base.mnn base.json` -- LORA: lora权重文件夹或者lora权重的safetensors -- SCALE: lora权重的scale, `lora_alpha / lora_r`, 一般为4.0 -- FUSE: 是否将lora_A与lora_B合并成为一个lora权重，合并后模型较大 -- OUT: 生成新的计算图文件名，默认为`lora.json`，转换为模型：`./MNNRevert2Buffer lora.json lora.mnn` - -### 示例 -使用该脚本生成lora对应的模型`lora.mnn`, 用法: [LoRA](../transformers/llm.html#lora) -```sh -cd build -./MNNDump2Json base.mnn base.json -python ../tools/script/apply_lora.py --base base.json --lora lora_dir -./MNNRevert2Buffer lora.json lora.mnn -``` \ No newline at end of file diff --git a/docs/transformers/diffusion.md b/docs/transformers/diffusion.md index b8367fd23..b2496fe1f 100644 --- a/docs/transformers/diffusion.md +++ b/docs/transformers/diffusion.md @@ -60,23 +60,33 @@ cd mnn_path/project/android/build ``` ## 运行Diffusion Demo ``` -./diffusion_demo +./diffusion_demo ``` 其中，resource_path 就是mnn模型文件的路径，除了mnn文件，还需要: -1. 将MNN目录transformers/diffusion/scheduler/alphas.txt文件拷贝到该文件夹下。 -2. 针对stable-diffusion-v1-5/chilloutmix模型需要将huggingfacetokenizer目录下merges.txt和vocab.json拷贝到该文件夹中。 -3. 针对Taiyi-Stable-Diffusion模型需要将huggingfacetokenizer目录下vocab.txt拷贝到该文件夹中。 -4. model_type是目前支持的两种diffusion模型的类别。如果是stable-diffusion-v1-5/chilloutmix模型设为0，如果是Taiyi-Stable-Diffusion模型设为1。 -5. output_image_name是生成图片的名字，默认图片位置在当前运行目录下。 -6. memory_mode代表设备是否内存足够，设为0表示内存节约模式(demo中每个模型使用前等待初始化，用完释放)，1代表内存足够模式(所有模式启动时全初始化完，用时无需等待初始化)。 -7. backend_type代表选择的运行后端。 -8. input_text是文生图的prompt，如果是stable-diffusion-v1-5/chilloutmix模型建议英文prompt，如果是Taiyi-Stable-Diffusion建议中文prompt。 +### 资源拷贝 +``` +(a) 将MNN目录transformers/diffusion/engine/scheduler/alphas.txt文件拷贝到resource_path文件夹下。 +(b) 针对stable-diffusion-v1-5/chilloutmix模型需要将huggingfacetokenizer目录下merges.txt和vocab.json拷贝到resource_path文件夹中。 +(c) 针对Taiyi-Stable-Diffusion模型需要将huggingfacetokenizer目录下vocab.txt拷贝到resource_path文件夹中。 +``` +### 参数设置 +``` +1. model_type是目前支持的两种diffusion模型的类别。如果是stable-diffusion-v1-5/chilloutmix模型设为0，如果是Taiyi-Stable-Diffusion模型设为1。 +2. memory_mode代表设备是否内存足够，设为0表示内存节约模式(demo中每个模型使用前等待初始化，用完释放)，1代表内存足够模式(所有模式启动时全初始化完，用时无需等待初始化)。 +3. backend_type代表选择的运行后端，如OpenCL/CPU等。 +4. iteration_num代表文生图迭代次数，通常建议设置10到20之间。 -运行指令例如: ``` -./diffusion_demo mnn_sd1.5_path 0 demo.jpg 0 3 "a cute cat" -./diffusion_demo mnn_chilloutmix_path 0 demo.jpg 0 3 "a pure girl" -./diffusion_demo mnn_taiyi_path 1 demo.jpg 0 3 "一只可爱的猫" +### 提示词和图片名称设置 +``` +1. output_image_name是生成图片的名字，默认图片位置在当前运行目录下。 +2. prompt_text是文生图的prompt，如果是stable-diffusion-v1-5/chilloutmix模型建议英文prompt，如果是Taiyi-Stable-Diffusion建议中文prompt。 +``` +### 运行命令示例 +``` +./diffusion_demo mnn_sd1.5_path 0 1 3 20 demo.jpg "a cute cat" +./diffusion_demo mnn_chilloutmix_path 0 0 3 10 demo.jpg "a pure girl" +./diffusion_demo mnn_taiyi_path 1 0 3 10 demo.jpg "一只可爱的猫" ``` ## FAQ 1. Demo运行报错、段错误，怎么解决？ diff --git a/docs/transformers/llm.md b/docs/transformers/llm.md index 109a6b43f..c63eabfa4 100644 --- a/docs/transformers/llm.md +++ b/docs/transformers/llm.md @@ -269,21 +269,41 @@ node llm_demo.js ~/qwen2.0_1.5b/config.json ~/qwen2.0_1.5b/prompt.txt 介绍一下音频里的内容 ``` -#### GPTQ权重加载 -- 使用脚本生成GPTQ模型权重，用法参考: [apply_gptq.py](../tools/script.html#apply-gptq-py) -- 创建`gptq.json`配置文件 - ```json - { - "llm_model": "model.mnn", - "llm_weight": "gptq.mnn.weight", - } - ``` +#### GPTQ权重 +需要使用GPTQ权重，可以在导出[Qwen2.5-0.5B-Instruct]模型时，使用`--gptq_path PATH`来指定[Qwen2.5-0.5B-Instruct-GPTQ-Int4]()的路径，使用如下： +```bash +# 导出GPTQ量化的模型 +python llmexport.py --path /path/to/Qwen2.5-0.5B-Instruct --gptq_path /path/to/Qwen2.5-0.5B-Instruct-GPTQ-Int4 --export mnn +``` + +#### LoRA权重 +LoRA权重有两使用方式：1. 合并LoRA权重到原始模型；2. LoRA模型单独导出。 +第一种模式速度更快，使用更简单但是不支持运行时切换；第二种略微增加一些内存和计算开销，但是更加灵活，支持运行时切换LoRA，适合多LoRA场景。 +##### 融合LoRA + +###### 导出 +将LoRA权重合并到原始模型中导出，在模型导出时指定`--lora_path PATH`参数，默认使用合并方式导出，使用如下： +```bash +# 导出LoRA合并的模型 +python llmexport.py --path /path/to/Qwen2.5-0.5B-Instruct --lora_path /path/to/lora --export mnn +``` + +###### 使用 +融合LoRA模型使用与原始模型使用方法完全一样。 + +##### 分离LoRA + +###### 导出 +将LoRA单独导出为一个模型，支持运行时切换，在模型导出时指定`--lora_path PATH`参数，并指定`--lora_split`，就会将LoRA分离导出，使用如下： +```bash +python llmexport.py --path /path/to/Qwen2.5-0.5B-Instruct --lora_path /path/to/lora --lora_split --export mnn +``` +导出后模型文件夹内除了原始模型外，还会增加`lora.mnn`，这个就是lora模型文件。 -#### LoRA权重加载 -- 使用脚本生成lora模型，用法参考: [apply_lora.py](../tools/script.html#apply-lora-py) +###### 使用 - lora模型使用 - - 直接加载lora模型使用，创建`lora.json`配置文件 + - 直接加载lora模型使用，创建`lora.json`配置文件，这样与直接运行融合LoRA的模型相似。 ```json { "llm_model": "lora.mnn", diff --git a/express/Executor.cpp b/express/Executor.cpp index 70800b505..22cc87769 100644 --- a/express/Executor.cpp +++ b/express/Executor.cpp @@ -220,18 +220,18 @@ void Executor::RuntimeManager::destroy(RuntimeManager* rtmgr) { } void Executor::RuntimeManager::setMode(Interpreter::SessionMode mode) { - mInside->modes.setMode(mode); + mInside->mContent->modes.setMode(mode); } void Executor::RuntimeManager::setHint(Interpreter::HintMode mode, int value) { - mInside->modes.setHint(mode, value); + mInside->mContent->modes.setHint(mode, value); auto current = ExecutorScope::Current(); auto rt = current->getRuntime(); for (auto& iter : rt.first) { - iter.second->setRuntimeHint(mInside->modes.runtimeHint); + iter.second->setRuntimeHint(mInside->mContent->modes.runtimeHint); } } void Executor::RuntimeManager::setExternalPath(std::string path, int type) { - mInside->modes.setExternalPath(path, type); + mInside->mContent->modes.setExternalPath(path, type); } void Executor::RuntimeManager::setHintPtr(Interpreter::HintMode mode, void* value) { auto current = ExecutorScope::Current(); @@ -261,6 +261,10 @@ bool Executor::RuntimeManager::getInfo(Interpreter::SessionInfoCode code, void* *dst = mInside->mRuntime.first.begin()->first; } } break; + case Interpreter::RESIZE_STATUS: { + auto dst = (int*)ptr; + *dst = mInside->mResizeStatus; + } break; default: { // Do nothing } break; @@ -270,10 +274,11 @@ bool Executor::RuntimeManager::getInfo(Interpreter::SessionInfoCode code, void* Executor::RuntimeManager::RuntimeManager() { mInside = new RuntimeAttr; + mInside->mContent.reset(new RuntimeAttr::Immutable); // Default set release for better performance - mInside->modes.callBackMode = Interpreter::Session_Release; - mInside->modes.inputMode = Interpreter::Session_Input_User; - mInside->modes.outputMode = Interpreter::Session_Output_User; + mInside->mContent->modes.callBackMode = Interpreter::Session_Release; + mInside->mContent->modes.inputMode = Interpreter::Session_Input_User; + mInside->mContent->modes.outputMode = Interpreter::Session_Output_User; } Executor::RuntimeManager::~RuntimeManager() { updateCache(); @@ -296,12 +301,12 @@ Executor::RuntimeManager* Executor::RuntimeManager::createRuntimeManager(const S res->mInside->mRuntime.second = originRt.second; res->mInside->mRuntime.first.insert(std::make_pair(type, rt)); res->mInside->mInfo = rt; - res->mInside->mNumberThread = numThread; + res->mInside->mContent->mNumberThread = numThread; if (nullptr != config.backendConfig) { - res->mInside->mConfig = *config.backendConfig; - res->mInside->mUserConfig = true; + res->mInside->mContent->mConfig = *config.backendConfig; + res->mInside->mContent->mUserConfig = true; } else { - res->mInside->mUserConfig = false; + res->mInside->mContent->mUserConfig = false; } return res; } @@ -310,8 +315,8 @@ ExecutorAttr* Executor::getAttr() const { } BackendConfig* Executor::RuntimeManager::getBnConfig() { - if (mInside->mUserConfig) { - return &mInside->mConfig; + if (mInside->mContent->mUserConfig) { + return &mInside->mContent->mConfig; } return nullptr; } @@ -359,7 +364,7 @@ void Executor::RuntimeManager::setCache(std::string cacheName) { } void Executor::RuntimeManager::setExternalFile(std::string fileName) { - mInside->mExternalFile = fileName; + mInside->mContent->mExternalFile = fileName; } void Executor::RuntimeManager::updateCache() { @@ -369,7 +374,7 @@ void Executor::RuntimeManager::updateCache() { std::lock_guard _l(mLock); // Backend_Auto and no Async work, then don't need updateCache - if(mInside->modes.backendMode == Interpreter::Session_Backend_Auto && !(mInside->mInfo->hasAsyncWork())) { + if(mInside->mContent->modes.backendMode == Interpreter::Session_Backend_Auto && !(mInside->mInfo->hasAsyncWork())) { return; } diff --git a/express/NeuralNetWorkOp.cpp b/express/NeuralNetWorkOp.cpp index 28580d792..c3b8dd64d 100644 --- a/express/NeuralNetWorkOp.cpp +++ b/express/NeuralNetWorkOp.cpp @@ -1878,8 +1878,13 @@ VARP _Im2Col(VARP x, INTS kernelSize, INTS dilate, INTS pads, INTS stride) { auto common = new Convolution2DCommonT; param->common.reset(common); op->main.value = param; - common->padX = pads[0]; - common->padY = pads[1]; + if (pads.size() >= 4) { + common->pads = pads; + } else { + // Compability for old model + common->padX = pads[0]; + common->padY = pads[1]; + } common->strideX = stride[0]; common->strideY = stride[1]; common->dilateX = dilate[0]; diff --git a/express/RuntimeAttr.hpp b/express/RuntimeAttr.hpp index 2c19f8b37..2f4e1d52c 100644 --- a/express/RuntimeAttr.hpp +++ b/express/RuntimeAttr.hpp @@ -11,16 +11,20 @@ struct Cache { size_t lastCacheSize = 0; }; struct RuntimeAttr { - Session::ModeGroup modes; + struct Immutable { + Session::ModeGroup modes; + BackendConfig mConfig; + bool mUserConfig; + int mNumberThread; + std::string mExternalFile; + }; + std::shared_ptr mContent; RuntimeInfo mRuntime; std::shared_ptr mInfo; std::shared_ptr mCache; - BackendConfig mConfig; - bool mUserConfig; - int mNumberThread; // Use for static module to compute flops float mFlops; - std::string mExternalFile; + mutable int mResizeStatus = 0; }; struct ExecutorAttr { std::shared_ptr constantBackend; diff --git a/express/module/Module.cpp b/express/module/Module.cpp index e3372db0c..9e565b1ae 100644 --- a/express/module/Module.cpp +++ b/express/module/Module.cpp @@ -15,7 +15,7 @@ #include "MNN_generated.h" #include "Utils.hpp" #include "RuntimeAttr.hpp" - +#include "ModuleInside.hpp" #include #ifdef MNN_INTERNAL_ENABLED #include "internal/auth/ModelAuth.hpp" @@ -176,7 +176,7 @@ class NetModule : public Module { int mode = 1; if (info->runTimeManager.get() != nullptr) { auto attr = info->runTimeManager->getInside(); - mode = attr->mNumberThread; + mode = attr->mContent->mNumberThread; int backendTypes[MNN_FORWARD_ALL]; info->runTimeManager->getInfo(Interpreter::BACKENDS, &backendTypes); backend = backendTypes[0]; @@ -208,7 +208,8 @@ class NetModule : public Module { virtual std::vector onForward(const std::vector& inputs) override { auto mModule = mChildren[0]; - + // Reset resize staus + mInfo->runTimeManager->getInside()->mResizeStatus = 0; #ifdef MNN_INTERNAL_ENABLED Timer _time; auto glo = ExecutorScope::Current(); @@ -246,9 +247,18 @@ class NetModule : public Module { } virtual Module* clone(CloneContext* ctx) const override { auto mModule = mChildren[0]; + auto origin = mInfo->runTimeManager->getInside(); + ScheduleConfig config; + config.type = origin->mRuntime.first.begin()->first; + config.numThread = origin->mContent->mNumberThread; + std::shared_ptr newRt (Executor::RuntimeManager::createRuntimeManager(config)); + const_cast(newRt->getInside())->mContent = origin->mContent; + std::shared_ptr newInfo(new Module::Info); + *newInfo = *mInfo; + ctx->pRuntimeManager = newRt; + newInfo->runTimeManager = newRt; std::shared_ptr submodule(mModule->clone(ctx)); - - NetModule* module(new NetModule(submodule, mInfo, nullptr, 0, 0.0f)); + NetModule* module(new NetModule(submodule, newInfo, nullptr, 0, 0.0f)); #ifdef MNN_INTERNAL_ENABLED module->mLogInfo = mLogInfo; #endif @@ -333,7 +343,7 @@ Module* Module::load(const std::vector& inputs, const std::vectorgetInside()->mExternalFile.empty()) { + if (rtMgr->getInside()->mContent->mExternalFile.empty()) { // Set Default externalFile rtMgr->setExternalFile(std::string(fileName) + ".weight"); needReset = true; @@ -361,7 +371,7 @@ static Module* loadInternal(const std::vector& inputs, const std::v } bool checkMNNBuffer = true; if (nullptr != _rtMgr) { - checkMNNBuffer = _rtMgr->getInside()->modes.checkNetBuffer; + checkMNNBuffer = _rtMgr->getInside()->mContent->modes.checkNetBuffer; } if (checkMNNBuffer) { flatbuffers::Verifier verify(buffer, length); diff --git a/express/module/ModuleInside.hpp b/express/module/ModuleInside.hpp new file mode 100644 index 000000000..93abff4e2 --- /dev/null +++ b/express/module/ModuleInside.hpp @@ -0,0 +1,35 @@ +// +// ModuleInside.hpp +// MNN +// +// Created by MNN on b'2025/01/13'. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef ModuleInside_hpp +#define ModuleInside_hpp +#include +#include +#include +namespace MNN { +namespace Express { +class Module::CloneContext { +public: + CloneContext() = default; + explicit CloneContext(const bool shareParams) + : mShareParams(shareParams) {} + virtual ~CloneContext() = default; + + const bool shareParams() const { return mShareParams; } + + EXPRP getOrClone(const EXPRP expr); + VARP getOrClone(const VARP var); + std::shared_ptr pRuntimeManager; +private: + bool mShareParams = false; + std::unordered_map mExprMap; + std::unordered_map mVarMap; +}; +}; +}; +#endif diff --git a/express/module/NMSModule.cpp b/express/module/NMSModule.cpp index 7712addf6..7d5601c24 100644 --- a/express/module/NMSModule.cpp +++ b/express/module/NMSModule.cpp @@ -2,7 +2,7 @@ // NMSModule.cpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // diff --git a/express/module/NMSModule.hpp b/express/module/NMSModule.hpp index dafe8bb69..1f87b9256 100644 --- a/express/module/NMSModule.hpp +++ b/express/module/NMSModule.hpp @@ -2,9 +2,10 @@ // NMSModule.hpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // + #ifndef NMSModule_hpp #define NMSModule_hpp #include diff --git a/express/module/PipelineModule.cpp b/express/module/PipelineModule.cpp index ee9f4d496..41970f0a4 100644 --- a/express/module/PipelineModule.cpp +++ b/express/module/PipelineModule.cpp @@ -9,6 +9,7 @@ #include "PipelineModule.hpp" #include #include +#include "ModuleInside.hpp" #include "StaticModule.hpp" #include "IfModule.hpp" #include "WhileModule.hpp" @@ -612,7 +613,7 @@ static std::vector _createSubModuleInfo(std::shared_ptr rt; Backend::Info compute; const BackendConfig* userConfig = nullptr; Session::ModeGroup modes; @@ -722,10 +723,10 @@ Module* PipelineModule::load(const std::vector& inputs, const std:: auto curExe = ExecutorScope::Current(); bool permitCodeGen = false; std::shared_ptr modRuntimeCfgPtr(new ModuleRuntimeConfig); - if (!rtMgr->getInside()->mExternalFile.empty()) { - modRuntimeCfgPtr->externalFile = rtMgr->getInside()->mExternalFile; + if (!rtMgr->getInside()->mContent->mExternalFile.empty()) { + modRuntimeCfgPtr->externalFile = rtMgr->getInside()->mContent->mExternalFile; } - permitCodeGen = rtMgr->getInside()->modes.codegenMode == Interpreter::Session_Codegen_Enable; + permitCodeGen = rtMgr->getInside()->mContent->modes.codegenMode == Interpreter::Session_Codegen_Enable; std::shared_ptr defaultBackend = curExe->getAttr()->constantBackend; std::vector> allTensors; sharedConst->allTensors.resize(net->tensorName()->size()); @@ -733,16 +734,15 @@ Module* PipelineModule::load(const std::vector& inputs, const std:: ModuleRuntimeConfig& modRuntime = *modRuntimeCfgPtr; modRuntime.needGeometry = needGeometry; { - modRuntime.modes = rtMgr->getInside()->modes; - modRuntime.rt = rtMgr->getInside()->mRuntime; - modRuntime.externalFile = rtMgr->getInside()->mExternalFile; - modRuntime.userConfig = &rtMgr->getInside()->mConfig; - modRuntime.compute.type = modRuntime.rt.first.begin()->first; - modRuntime.compute.numThread = 1; - modRuntime.rt.first.begin()->second->setRuntimeHint(rtMgr->getInside()->modes.runtimeHint); - } - auto& rt = modRuntime.rt; - auto firstRt = rt.first[modRuntime.compute.type]; + modRuntime.modes = rtMgr->getInside()->mContent->modes; + modRuntime.rt = rtMgr; + rtMgr->getInside()->mRuntime.first.begin()->second->setRuntimeHint(rtMgr->getInside()->mContent->modes.runtimeHint); + modRuntime.externalFile = rtMgr->getInside()->mContent->mExternalFile; + modRuntime.userConfig = &rtMgr->getInside()->mContent->mConfig; + modRuntime.compute.type = rtMgr->getInside()->mRuntime.first.begin()->first; + } + auto& rt = modRuntime.rt->getInside()->mRuntime; + auto firstRt = rt.first.find(modRuntime.compute.type)->second; sharedConst->constReplaceBackend.reset(firstRt->onCreate(modRuntime.userConfig)); ErrorCode code = NO_ERROR; std::set noneedComputeIndexes; diff --git a/express/module/StaticModule.cpp b/express/module/StaticModule.cpp index 33fa14afe..c220fbff3 100644 --- a/express/module/StaticModule.cpp +++ b/express/module/StaticModule.cpp @@ -2,18 +2,18 @@ // StaticModule.cpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // #include "StaticModule.hpp" #include -#include #include #include #include "Utils.hpp" #include "core/WrapExecution.hpp" #include "core/MNNMemoryUtils.h" +#include "ModuleInside.hpp" #include "RuntimeAttr.hpp" #include "core/TensorUtils.hpp" #include "core/FileLoader.hpp" @@ -233,7 +233,7 @@ void StaticModule::resetInputOutputs() { } pipelineInfo.first.inputTensorCopyCache.insert(std::make_pair(mInputTensors[i], std::make_tuple(nullptr, nullptr, true, true))); mPrevInputTensor[i].first = nullptr; - mPrevInputTensor[i].second = nullptr; + mPrevInputTensor[i].second = MNN_FORWARD_CPU; } mOutputTensors.resize(mResource->mOutputFromTensor.size()); for (int i = 0; i < mResource->mOutputFromTensor.size(); ++i) { @@ -294,11 +294,14 @@ StaticModule::StaticModule(std::vector inputs, Schedule::ScheduleInfo&& scheduleInfo, std::shared_ptr sharedConst, Session::ModeGroup&& mode, - RuntimeInfo&& rt, + std::shared_ptr rtm, const Module::Config& config ) { setType("StaticModule"); mResource.reset(new Resource); + mRuntimeManager = rtm; + MNN_ASSERT(nullptr != rtm); + auto rt = rtm->getInside()->mRuntime; mResource->mSharedConst = sharedConst; mResource->mModes = std::move(mode); mResource->mBnInfo.user = &mResource->mBnConfig; @@ -371,7 +374,6 @@ void StaticModule::onClearCache() { if (nullptr != mSession) { for (int i=0; igetPipelineInfo(0).first.inputTensorCopyCache) { std::get<3>(iter.second) = true; @@ -381,6 +383,8 @@ void StaticModule::onClearCache() { ErrorCode StaticModule::_resize(const std::vector& inputs) { ErrorCode code = NO_ERROR; auto& pipelineInfo = mSession->getPipelineInfo(0); + auto rtmInside = mRuntimeManager->getInside(); + int curStatus = 0; if (mResource->mModes.inputMode == Interpreter::Session_Input_User) { pipelineInfo.first.inputBackendChange = false; bool needResize = mResource->mUseContentInputs; @@ -392,14 +396,18 @@ ErrorCode StaticModule::_resize(const std::vector& inputs) { Schedule::TENSORCACHE* cacheTensor = nullptr; if (mPrevInputTensor[i].first != inputTensor) { auto newBackend = TensorUtils::getDescribeOrigin(inputTensor)->getBackend(); - if (mPrevInputTensor[i].second != newBackend) { + auto newType = MNN_FORWARD_CPU; + if (nullptr != newBackend) { + newType = newBackend->type(); + } + if (mPrevInputTensor[i].second != newType) { pipelineInfo.first.inputBackendChange = true; } auto cacheIter = pipelineInfo.first.inputTensorCopyCache.find(mInputTensors[i]); cacheTensor = &cacheIter->second; MNN_ASSERT(cacheIter != pipelineInfo.first.inputTensorCopyCache.end()); std::get<3>(cacheIter->second) = true; - mPrevInputTensor[i] = std::make_pair(inputTensor, newBackend); + mPrevInputTensor[i] = std::make_pair(inputTensor, newType); if (std::get<1>(*cacheTensor) != nullptr) { if (!WrapExecution::needWrap(inputTensor, TensorUtils::getDescribeOrigin(std::get<0>(*cacheTensor))->getBackend())) { // No need copy now, reset it @@ -480,6 +488,7 @@ ErrorCode StaticModule::_resize(const std::vector& inputs) { } } } + mSession->getInfo(Interpreter::RESIZE_STATUS, &curStatus); code = mSession->resize(); } else { // Resize @@ -496,6 +505,7 @@ ErrorCode StaticModule::_resize(const std::vector& inputs) { mSession->setNeedResize(); } } + mSession->getInfo(Interpreter::RESIZE_STATUS, &curStatus); code = mSession->resize(); // Copy for (int i = 0; i < inputs.size(); ++i) { @@ -507,6 +517,7 @@ ErrorCode StaticModule::_resize(const std::vector& inputs) { mInputTensors[i]->copyFromHostTensor(inputTensor); } } + rtmInside->mResizeStatus = ALIMAX(rtmInside->mResizeStatus, curStatus); return code; } @@ -605,11 +616,11 @@ std::vector StaticModule::onForward(const std::vectormResource = mResource; + module->mRuntimeManager = ctx->pRuntimeManager; if (mResource->mOutputFromTensor.empty()) { return this->cloneBaseTo(ctx, module); } - // TODO: If RuntimeManager is not the same as Runtime, may copy error - auto rt = Executor::getRuntime(); + auto rt = ctx->pRuntimeManager->getInside()->mRuntime; module->mSession.reset(mSession->clone(std::move(rt), mResource->mSharedConst)); module->resetInputOutputs(); return this->cloneBaseTo(ctx, module); diff --git a/express/module/StaticModule.hpp b/express/module/StaticModule.hpp index 582ae92fb..a3ea5dfd9 100644 --- a/express/module/StaticModule.hpp +++ b/express/module/StaticModule.hpp @@ -2,7 +2,7 @@ // StaticModule.hpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // @@ -10,6 +10,7 @@ #define StaticModule_hpp #include +#include #include "core/Schedule.hpp" #include "core/Session.hpp" @@ -20,7 +21,7 @@ struct BufferStorage; namespace Express { class StaticModule : public Module { public: - StaticModule(std::vector inputs, std::vector outputs, std::vector>&& buffer, Schedule::ScheduleInfo&& scheduleInfo, std::shared_ptr sharedConst, Session::ModeGroup&& mode, RuntimeInfo&& rt, const Module::Config& config); + StaticModule(std::vector inputs, std::vector outputs, std::vector>&& buffer, Schedule::ScheduleInfo&& scheduleInfo, std::shared_ptr sharedConst, Session::ModeGroup&& mode, std::shared_ptr rt, const Module::Config& config); virtual ~ StaticModule(); virtual std::vector onForward(const std::vector& inputs) override; virtual void onClearCache() override; @@ -53,11 +54,12 @@ class StaticModule : public Module { }; std::shared_ptr mSession; std::vector mInputTensors; - std::vector> mPrevInputTensor; + std::vector> mPrevInputTensor; std::vector mOutputTensors; std::shared_ptr mResource; bool mShapeInferSeperate = false; std::vector mOutputVars; + std::shared_ptr mRuntimeManager; }; } } diff --git a/express/module/WhileModule.cpp b/express/module/WhileModule.cpp index baf14948b..3f94d7232 100644 --- a/express/module/WhileModule.cpp +++ b/express/module/WhileModule.cpp @@ -2,7 +2,7 @@ // WhileModule.cpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // diff --git a/express/module/WhileModule.hpp b/express/module/WhileModule.hpp index 73af315c0..189985548 100644 --- a/express/module/WhileModule.hpp +++ b/express/module/WhileModule.hpp @@ -2,9 +2,10 @@ // WhileModule.hpp // MNN // -// Created by MNN on b'2020/09/10'. +// Created by MNN on 2020/09/10. // Copyright © 2018, Alibaba Group Holding Limited // + #ifndef WhileModule_hpp #define WhileModule_hpp #include diff --git a/include/MNN/Interpreter.hpp b/include/MNN/Interpreter.hpp index bffb2f313..b0fe9867e 100644 --- a/include/MNN/Interpreter.hpp +++ b/include/MNN/Interpreter.hpp @@ -216,7 +216,8 @@ class MNN_PUBLIC Interpreter { // Geometry Compute option, default is 0xFFFF GEOMETRY_COMPUTE_MASK = 4, - // 0: Close dynamic quant; 1: per batch quant; 2: per tensor quant + // 0: Close dynamic quant; + // 1: For general convolution, use one scale&zeropoint to quant. DYNAMIC_QUANT_OPTIONS = 5, // For Mobile CPU with big-litter core, set decrease rate to let MNN divide task differential by CPU's performance @@ -423,7 +424,10 @@ class MNN_PUBLIC Interpreter { /** Backends in session in M, int*, length >= 1 + number of configs when create session */ BACKENDS = 2, - /** Resize Info, int*, 0: ready to execute, 1: need malloc, 2: need resize */ + /** Resize Info, int* , the mean different from API + Interpreter::getSessionInfo: 0: ready to execute, 1: need malloc, 2: need resize + RuntimeManager::getInfo: 0: no resize, 1: re-malloc, 2: resize + */ RESIZE_STATUS = 3, /** Mode / NumberThread, int* */ diff --git a/include/MNN/MNNDefine.h b/include/MNN/MNNDefine.h index 91e7877a8..04c52103b 100644 --- a/include/MNN/MNNDefine.h +++ b/include/MNN/MNNDefine.h @@ -71,10 +71,11 @@ MNN_ERROR("Check failed: %s ==> %s\n", #success, #log); \ #else #define MNN_PUBLIC __attribute__((visibility("default"))) #endif + #define STR_IMP(x) #x #define STR(x) STR_IMP(x) #define MNN_VERSION_MAJOR 3 #define MNN_VERSION_MINOR 0 -#define MNN_VERSION_PATCH 3 +#define MNN_VERSION_PATCH 4 #define MNN_VERSION STR(MNN_VERSION_MAJOR) "." STR(MNN_VERSION_MINOR) "." STR(MNN_VERSION_PATCH) #endif /* MNNDefine_h */ diff --git a/include/MNN/MNNForwardType.h b/include/MNN/MNNForwardType.h index 31665c1ec..b611012c5 100644 --- a/include/MNN/MNNForwardType.h +++ b/include/MNN/MNNForwardType.h @@ -53,23 +53,23 @@ typedef enum { } MNNForwardType; typedef enum { - // choose one tuning mode Only - MNN_GPU_TUNING_NONE = 1 << 0,/* Forbidden tuning, performance not good */ - MNN_GPU_TUNING_HEAVY = 1 << 1,/* heavily tuning, usually not suggested */ - MNN_GPU_TUNING_WIDE = 1 << 2,/* widely tuning, performance good. Default */ - MNN_GPU_TUNING_NORMAL = 1 << 3,/* normal tuning, performance may be ok */ - MNN_GPU_TUNING_FAST = 1 << 4,/* fast tuning, performance may not good */ - - // choose one opencl memory mode Only - /* User can try OpenCL_MEMORY_BUFFER and OpenCL_MEMORY_IMAGE both, - then choose the better one according to performance*/ - MNN_GPU_MEMORY_BUFFER = 1 << 6,/* User assign mode */ - MNN_GPU_MEMORY_IMAGE = 1 << 7,/* User assign mode */ - // choose one opencl memory mode Only, this mode Only support for Qualcomm gpu - /* User can try MNN_GPU_RECORD_OP and MNN_GPU_RECORD_KERNEL both, - then choose the better one according to performance*/ - MNN_GPU_RECORD_OP = 1 << 8,/* the kernels in one op execution record into one recording */ - MNN_GPU_RECORD_BATCH = 1 << 9,/* 10 kernels record into one recording */ + // For the OpenCL backend, all five of the following options are valid. The user is allowed to enable any one of them. + // For the Vulkan backend, only options MNN_GPU_TUNING_NONE, MNN_GPU_TUNING_HEAVY, and MNN_GPU_TUNING_WIDE are valid. The user is allowed to enable any one of these three. + MNN_GPU_TUNING_NONE = 1 << 0, /* Forbidden tuning, performance not good.(OpenCL/Vulkan) */ + MNN_GPU_TUNING_HEAVY = 1 << 1, /* Heavily tuning, usually not suggested.(OpenCL/Vulkan) */ + MNN_GPU_TUNING_WIDE = 1 << 2, /* Widely tuning, performance good. Default.(OpenCL/Vulkan) */ + MNN_GPU_TUNING_NORMAL = 1 << 3, /* Normal tuning, performance may be ok.(OpenCL) */ + MNN_GPU_TUNING_FAST = 1 << 4, /* Fast tuning, performance may not good.(OpenCL) */ + + // For the OpenCL backend, the following two options are both valid. The user could try OpenCL_MEMORY_BUFFER and OpenCL_MEMORY_IMAGE both, and then choose the better one based on performance. + // For the Vulkan backend, neither option is valid. The user uses the CMake option MNN_VULKAN_IMAGE to select between image memory mode and buffer memory mode. + MNN_GPU_MEMORY_BUFFER = 1 << 6, /* OpenCL_MEMORY_BUFFER */ + MNN_GPU_MEMORY_IMAGE = 1 << 7, /* OpenCL_MEMORY_IMAGE */ + + // For the OpenCL backend, the following two options are effective only on Qualcomm GPUs. When using a Qualcomm GPU, the user could try both options and choose the better one based on performance. + // For the Vulkan backend, only option MNN_GPU_RECORD_BATCH is valid. When MNN_GPU_RECORD_BATCH is enabled, all ops would share one commandBuffer. + MNN_GPU_RECORD_OP = 1 << 8, /* The kernels in one op execution record into one recording.(OpenCL) */ + MNN_GPU_RECORD_BATCH = 1 << 9, /* 10 kernels record into one recording.(OpenCL) All ops share one commandBuffer.(Vulkan) */ } MNNGpuMode; #ifdef __cplusplus diff --git a/include/MNN/expr/Module.hpp b/include/MNN/expr/Module.hpp index a2e5dc41b..15462f888 100644 --- a/include/MNN/expr/Module.hpp +++ b/include/MNN/expr/Module.hpp @@ -10,7 +10,6 @@ #define MNN_Train_Module_hpp #include -#include #include #include @@ -97,23 +96,7 @@ class MNN_PUBLIC Module { std::string bizCode; }; const Info* getInfo() const; - class CloneContext { - public: - CloneContext() = default; - explicit CloneContext(const bool shareParams) - : mShareParams(shareParams) {} - virtual ~CloneContext() = default; - - const bool shareParams() const { return mShareParams; } - - EXPRP getOrClone(const EXPRP expr); - VARP getOrClone(const VARP var); - private: - bool mShareParams = false; - std::unordered_map mExprMap; - std::unordered_map mVarMap; - }; - + class CloneContext; virtual Module* clone(CloneContext* ctx) const { return nullptr; } diff --git a/pymnn/pip_package/MNN/expr/__init__.py b/pymnn/pip_package/MNN/expr/__init__.py index bb93f0b7f..a76e236f9 100644 --- a/pymnn/pip_package/MNN/expr/__init__.py +++ b/pymnn/pip_package/MNN/expr/__init__.py @@ -93,7 +93,7 @@ def _to_var(x, dtype=None): x = x.astype(np.float32) x = _F.const(x, x.shape, dtype=_F.float) else: - raise ValueError('Just support i/f dtype numpy.') + raise ValueError('Just support int32/float dtype numpy. Please Call numpy.astype(float32) / numpy.astype(int32) before') except: pass # 3. Sequence diff --git a/pymnn/pip_package/MNN/llm/__init__.py b/pymnn/pip_package/MNN/llm/__init__.py index ebf4cf84e..76eb24527 100644 --- a/pymnn/pip_package/MNN/llm/__init__.py +++ b/pymnn/pip_package/MNN/llm/__init__.py @@ -34,7 +34,7 @@ def generate(self, input_ids): Example: ------- >>> input_ids = [151644, 872, 198, 108386, 151645, 198, 151644, 77091] - >>> output_ids = qwen.generate(input_ids) + >>> output_ids = llm.generate(input_ids) ''' return super.generate(input_ids) @@ -53,7 +53,7 @@ def response(self, prompt, stream = False): Example: ------- - >>> res = qwen.response('Hello', True) + >>> res = llm.response('Hello', True) ''' return super.response(prompt, stream) @@ -71,10 +71,64 @@ def txt_embedding(self, prompt): Example: ------- - >>> res = qwen.txt_embedding('Hello') + >>> res = llm.txt_embedding('Hello') ''' return super.txt_embedding(prompt) + def apply_lora(self, lora_path): + ''' + apply lora model on base model + + Parameters + ---------- + lora_path : lora model path + + Returns + ------- + index : int index + + Example: + ------- + >>> lora_index = llm.apply_lora('./qwen-1.8b-int4/qwen-1.8b-lora.mnn') + ''' + return super.apply_lora(lora_path) + + def select_module(self, module_index): + ''' + select current module + + Parameters + ---------- + module_index : module index + + Returns + ------- + res : bool + + Example: + ------- + >>> res = llm.select_module(lora_index) + ''' + return super.select_module(module_index) + + def release_module(self, module_index): + ''' + release module + + Parameters + ---------- + module_index : module index, int + + Returns + ------- + res : bool + + Example: + ------- + >>> res = llm.release_module(lora_index) + ''' + return super.release_module(module_index) + def create(config_path, embedding_model = False): ''' create LLM instance by `config.json` @@ -89,6 +143,6 @@ def create(config_path, embedding_model = False): Example: ------- - >>> qwen = llm.create('./qwen-1.8b-int4/config.json') + >>> llm = mllm.create('./qwen-1.8b-int4/config.json') ''' return _F.create(config_path, embedding_model) \ No newline at end of file diff --git a/pymnn/pip_package/MNN/numpy/__init__.py b/pymnn/pip_package/MNN/numpy/__init__.py index 258741875..a9ea5c390 100644 --- a/pymnn/pip_package/MNN/numpy/__init__.py +++ b/pymnn/pip_package/MNN/numpy/__init__.py @@ -516,12 +516,7 @@ def array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0, like=N else: x = object else: - if not isinstance(object, _F._Sequence): - x = _F.scalar(object, dtype) - else: - # get shape and dtype of sequence - dst_shape, item_type = _F._list_shape_type(object) - x = _F.const(object, dst_shape, dtype=item_type) + x = _F._to_var(object) # if give dtype, cast to dtype if dtype is not None and dtype != x.dtype: x = _F.cast(x, dtype) diff --git a/pymnn/pip_package/README.md b/pymnn/pip_package/README.md index 9efd2badd..133f581da 100644 --- a/pymnn/pip_package/README.md +++ b/pymnn/pip_package/README.md @@ -12,5 +12,5 @@ Installation Dependencies --------------------------------------------------- - flatbuffers, numpy + numpy diff --git a/pymnn/pip_package/build_manylinux2014.sh b/pymnn/pip_package/build_manylinux2014.sh index 3ce43fa3c..948fa138b 100755 --- a/pymnn/pip_package/build_manylinux2014.sh +++ b/pymnn/pip_package/build_manylinux2014.sh @@ -24,7 +24,7 @@ for PYBIN in /opt/python/*/bin; do elif [ "$1" == "-cuda" ]; then USE_CUDA=true "${PYBIN}/python" setup.py bdist_wheel else - "${PYBIN}/python" setup.py bdist_wheel + "${PYBIN}/python" setup.py bdist_wheel $1 fi done diff --git a/pymnn/pip_package/setup.py b/pymnn/pip_package/setup.py index 5955a9310..52018354c 100644 --- a/pymnn/pip_package/setup.py +++ b/pymnn/pip_package/setup.py @@ -34,10 +34,12 @@ def get_version(): sys.argv = [sys.argv[0]] + unknown import platform +has_numpy = True try: import numpy as np except: print("import numpy failed") + has_numpy = False from setuptools import setup, Extension, find_packages from distutils import core from distutils.core import Distribution @@ -166,7 +168,9 @@ def configure_extension_build(): ] if check_env_flag('WERROR'): extra_compile_args.append('-Werror') - extra_compile_args += ['-DPYMNN_EXPR_API', '-DPYMNN_NUMPY_USABLE', '-DPYMNN_OPENCV_API', '-DPYMNN_AUDIO_API'] + extra_compile_args += ['-DPYMNN_EXPR_API', '-DPYMNN_OPENCV_API', '-DPYMNN_AUDIO_API'] + if has_numpy: + extra_compile_args += ['-DPYMNN_NUMPY_USABLE'] if IS_LINUX and USE_INTERNAL: extra_compile_args += ['-DPYMNN_INTERNAL_SERVING'] if args.env == 'daily': @@ -220,7 +224,8 @@ def configure_extension_build(): # llm include engine_include_dirs += [os.path.join(root_dir, "transformers", "llm", "engine", "include")] engine_include_dirs += [os.path.join(root_dir, "3rd_party")] - engine_include_dirs += [np.get_include()] + if has_numpy: + engine_include_dirs += [np.get_include()] lib_files = [] trt_depend = ['-lTRT_CUDA_PLUGIN', '-lnvinfer', '-lnvparsers', '-lnvinfer_plugin', '-lcudart'] @@ -311,7 +316,8 @@ def configure_extension_build(): tools_include_dirs += [os.path.join(root_dir, "source", "core")] tools_include_dirs += [os.path.join(root_dir, "schema", "current")] tools_include_dirs += [os.path.join(root_dir, "source")] - tools_include_dirs += [np.get_include()] + if has_numpy: + tools_include_dirs += [np.get_include()] # enable logging and model authentication on linux. if IS_LINUX and USE_INTERNAL: @@ -369,7 +375,7 @@ def make_relative_rpath(path): if IS_DARWIN: # conda: dylibs install at site-packages/MNN_*/lib/ # not conda: dylibs instal at .../lib/ for .../lib/python*/site-packages/_mnncengine.cpython-*-darwin.so - return [f'-Wl,-rpath,@loader_path/../../../{path},-rpath,@loader_path/{path}'] + return ['-Wl,-rpath,@loader_path/../../../'+path+',-rpath,@loader_path/'+path] elif IS_WINDOWS: return [] else: diff --git a/pymnn/src/llm.h b/pymnn/src/llm.h index 0b7521918..9692184f5 100644 --- a/pymnn/src/llm.h +++ b/pymnn/src/llm.h @@ -48,6 +48,7 @@ static PyObject* PyMNNLLM_generate(LLM *self, PyObject *args) { if (!PyArg_ParseTuple(args, "O", &input_ids) && isInts(input_ids)) { Py_RETURN_NONE; } + auto output_ids = self->llm->generate(toInts(input_ids)); return toPyObj(output_ids); } @@ -123,6 +124,42 @@ static PyObject* PyMNNLLM_txt_embedding(LLM *self, PyObject *args) { return (PyObject *)embeds; } +static PyObject* PyMNNLLM_apply_lora(LLM *self, PyObject *args) { + if (self->is_embedding) { + Py_RETURN_NONE; + } + const char* path = NULL; + if (!PyArg_ParseTuple(args, "s", &path)) { + Py_RETURN_NONE; + } + int model_index = self->llm->apply_lora(path); + return toPyObj(model_index); +} + +static PyObject* PyMNNLLM_select_module(LLM *self, PyObject *args) { + if (self->is_embedding) { + Py_RETURN_NONE; + } + PyObject *index = nullptr; + if (!PyArg_ParseTuple(args, "O", &index) && isInt(index)) { + Py_RETURN_NONE; + } + bool res = self->llm->select_module(toInt(index)); + return toPyObj(res); +} + +static PyObject* PyMNNLLM_release_module(LLM *self, PyObject *args) { + if (self->is_embedding) { + Py_RETURN_NONE; + } + PyObject *index = nullptr; + if (!PyArg_ParseTuple(args, "O", &index) && isInt(index)) { + Py_RETURN_NONE; + } + bool res = self->llm->release_module(toInt(index)); + return toPyObj(res); +} + static PyMethodDef PyMNNLLM_methods[] = { {"load", (PyCFunction)PyMNNLLM_load, METH_VARARGS, "load model."}, {"forward", (PyCFunction)PyMNNLLM_forward, METH_VARARGS, "forward `logits` by `input_ids`."}, @@ -133,6 +170,9 @@ static PyMethodDef PyMNNLLM_methods[] = { {"tokenizer_encode", (PyCFunction)PyMNNLLM_tokenizer_encode, METH_VARARGS, "tokenizer encode."}, {"tokenizer_decode", (PyCFunction)PyMNNLLM_tokenizer_decode, METH_VARARGS, "tokenizer decode."}, {"txt_embedding", (PyCFunction)PyMNNLLM_txt_embedding, METH_VARARGS, "txt embedding."}, + {"apply_lora", (PyCFunction)PyMNNLLM_apply_lora, METH_VARARGS, "apply_lora."}, + {"select_module", (PyCFunction)PyMNNLLM_select_module, METH_VARARGS, "select_module."}, + {"release_module", (PyCFunction)PyMNNLLM_release_module, METH_VARARGS, "release_module."}, {NULL} /* Sentinel */ }; diff --git a/source/backend/arm82/Arm82Functions.cpp b/source/backend/arm82/Arm82Functions.cpp index 5e2efa3b9..064ab3d63 100644 --- a/source/backend/arm82/Arm82Functions.cpp +++ b/source/backend/arm82/Arm82Functions.cpp @@ -43,10 +43,14 @@ void MNNPackedMatMulRemainFP16_int8(float* C, const float* A, const float* B, si #endif #ifdef MNN_LOW_MEMORY -void MNNAbsMaxFP16(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack); +void MNNAbsMaxFP16_Pack8(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack); +void MNNAbsMaxFP16_Pack4(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack); void MNNQuantScaleFP16(float* sum, float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch); -void MNNDynamicQuantFP16(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack); +void MNNDynamicQuantFP16_Pack8(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack); +void MNNDynamicQuantFP16_Pack4(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack); void MNNQuantSumFP16(float* sum, const float* dequant_scale, size_t thread, size_t batch); +void MNNGeneralIm2col_Arm82(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack); +void MNNGeneralIm2col_Arm86(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack); #endif #if defined(__aarch64__) void CountMinMaxValue_FP16(float* source, float* minVal, float* maxVal, size_t sizeQuad); @@ -799,6 +803,62 @@ static void _ArmBasicMNNPackC4ForMatMul_A_L8(int8_t* destOrigin, int8_t const** } } +#ifdef MNN_LOW_MEMORY +void MNNAbsMaxFP16(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) { + if (pack == 4) { + MNNAbsMaxFP16_Pack4(source, absmax, src_depth_quad, realSize, pack); + return; + } + if (pack == 8) { + MNNAbsMaxFP16_Pack8(source, absmax, src_depth_quad, realSize, pack); + return; + } + // source: (src_depth_quad, realSize, pack) + auto srcStep = pack * realSize; + auto srcPtr = (FLOAT16*)source; + auto dstPtr = (FLOAT16*)absmax; + for (int i = 0; i < realSize; ++i) { + FLOAT16 absmaxVal = 0; // absmaxVal>=0 + for (int c = 0; c < src_depth_quad; ++c) { + auto src = srcPtr + c * srcStep + i * pack; + for (int k = 0; k < pack; ++k) { + if (std::abs(src[k]) > absmaxVal) { + absmaxVal = std::abs(src[k]); + } + } + } + dstPtr[i] = absmaxVal; + } + return; +} + +static void MNNDynamicQuantFP16(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) { + if (pack == 8) { + MNNDynamicQuantFP16_Pack8(src, dst, scale, src_depth_quad,realSize, pack); + return; + } + if (pack == 4) { + MNNDynamicQuantFP16_Pack4(src, dst, scale, src_depth_quad,realSize, pack); + return; + } + int8_t* dstPtr = dst; + auto srcPtr = (FLOAT16*)src; + + for (int i = 0; i < realSize; ++i) { + auto scaleVal = static_cast(scale[i]); + for (int c = 0; c < src_depth_quad; ++c) { + auto srcZ = srcPtr + c * pack * realSize + i * pack; + auto dstZ = dstPtr + c * pack * realSize + i * pack; + for (int k = 0; k < pack; ++k) { + int val = (int)roundf(srcZ[k] * scaleVal); + dstZ[k] = val; + } + } + } + return; +} +#endif + static CoreFunctions* gInstance = nullptr; static CoreInt8Functions* gArm82CoreInt8Functions = nullptr; @@ -840,6 +900,13 @@ bool Arm82Functions::init() { FUNC_PTR_ASSIGN(gInstance->MNNStrassenMergeCFunction, ARM82StrassenMerge); #ifdef MNN_LOW_MEMORY FUNC_PTR_ASSIGN(gInstance->MNNDynamicUpdateConvBiasScale, origin->MNNDynamicUpdateConvBiasScale); + if (origin->supportSDot) { + FUNC_PTR_ASSIGN(gInstance->MNNGeneralIm2Col, MNNGeneralIm2col_Arm82); + } + if (origin->supportI8mm) { + FUNC_PTR_ASSIGN(gInstance->MNNGeneralIm2Col, MNNGeneralIm2col_Arm86); + } + #endif gInstance->penalty = 2.0f; FUNC_PTR_ASSIGN(gInstance->MNNScaleAndAddBias, MNNScaleAndAddBiasFP16); @@ -863,8 +930,6 @@ bool Arm82Functions::init() { gInstance->supportI8mm = origin->supportI8mm; #ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM // Weight Dequant Gemm Kernels - FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMul_int4, MNNPackedMatMulFP16_int4); - FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMulRemain_int4, MNNPackedMatMulRemainFP16_int4); FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMul_int8, MNNPackedMatMulFP16_int8); FUNC_PTR_ASSIGN(gInstance->MNNPackedMatMulRemain_int8, MNNPackedMatMulRemainFP16_int8); #endif diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNCountMinMax_ARM82.S b/source/backend/arm82/asm/arm64/low_memory/CountMinMaxValue_FP16.S similarity index 99% rename from source/backend/arm82/asm/arm64/low_memory/MNNCountMinMax_ARM82.S rename to source/backend/arm82/asm/arm64/low_memory/CountMinMaxValue_FP16.S index 7c32ca912..51ee4b60a 100644 --- a/source/backend/arm82/asm/arm64/low_memory/MNNCountMinMax_ARM82.S +++ b/source/backend/arm82/asm/arm64/low_memory/CountMinMaxValue_FP16.S @@ -1,5 +1,5 @@ // -// MNNAbsMaxFP16.S +// CountMinMaxValue_FP16.S // MNN // // Created by MNN on 2023/10/31. diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_Pack4.S b/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_Pack4.S new file mode 100644 index 000000000..b7a4ad121 --- /dev/null +++ b/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_Pack4.S @@ -0,0 +1,226 @@ +// +// MNNAbsMaxFP16_Pack4.S +// MNN +// +// Created by MNN on 2023/10/31. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +.macro Abs z0, z1, z2, z3 + fabs \z0\().4h, \z0\().4h + fabs \z1\().4h, \z1\().4h + fabs \z2\().4h, \z2\().4h + fabs \z3\().4h, \z3\().4h +.endm + +.macro Max d0, d1, d2, d3, z0, z1, z2, z3 + fmax \d0\().4h, \d0\().4h, \z0\().4h + fmax \d1\().4h, \d1\().4h, \z1\().4h + fmax \d2\().4h, \d2\().4h, \z2\().4h + fmax \d3\().4h, \d3\().4h, \z3\().4h +.endm + +.macro ReduceMax_8 s0, s1, s2, s3, s4, s5, s6, s7 + fmaxp \s0\().4h, \s0\().4h, \s1\().4h // 0 0 1 1 + fmaxp \s2\().4h, \s2\().4h, \s3\().4h // 2 2 3 3 + fmaxp \s4\().4h, \s4\().4h, \s5\().4h // 4 4 5 6 + fmaxp \s6\().4h, \s6\().4h, \s7\().4h // 6 6 7 7 + fmaxp \s0\().4h, \s0\().4h, \s2\().4h // 0 1 2 3 + fmaxp \s4\().4h, \s4\().4h, \s6\().4h // 4 5 6 7 + mov \s0\().d[1], \s4\().d[0] // 0 1 2 3 4 5 6 7 +.endm + +.macro ReduceMax_4 s0, s1, s2, s3 + fmaxp \s0\().4h, \s0\().4h, \s1\().4h // 0 0 1 1 + fmaxp \s2\().4h, \s2\().4h, \s3\().4h // 2 2 3 3 + fmaxp \s0\().4h, \s0\().4h, \s2\().4h // 0 1 2 3 +.endm + +//void MNNAbsMaxFP16_Pack4(const float* source, float* absmax, size_t LU, size_t EP, int LP) +// LP = 4 +asm_function MNNAbsMaxFP16_Pack4 + +// x0: source, x1:absmax, x2:src_depth_quad, x3:realSize, x4: pack(no used) +stp d14, d15, [sp, #(-16 * 4)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] + +Start: +lsl x6, x3, #3 // src_step = batch * 4 * sizeof(float16_t) = batch << 3 + +TILE_12: +cmp x3, #12 +blt TILE_10 +mov x5, x2 // src_depth_quad +mov x7, x0 // src +sub x8, x6, #64 // src_step + +// absmax: v0-11 +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x7], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x7], #32 +ld1 {v8.4h, v9.4h, v10.4h, v11.4h}, [x7], x8 +Abs v0, v1, v2, v3 +Abs v4, v5, v6, v7 +Abs v8, v9, v10, v11 +subs x5, x5, #1 +beq Tile12End + +LoopSz_12: +ld1 {v12.4h, v13.4h, v14.4h, v15.4h}, [x7], #32 +ld1 {v16.4h, v17.4h, v18.4h, v19.4h}, [x7], #32 +ld1 {v20.4h, v21.4h, v22.4h, v23.4h}, [x7], x8 +// absmax = fmax(absmax, abs(x)) +Abs v12, v13, v14, v15 +Abs v16, v17, v18, v19 +Abs v20, v21, v22, v23 +Max v0, v1, v2, v3, v12, v13, v14, v15 +Max v4, v5, v6, v7, v16, v17, v18, v19 +Max v8, v9, v10, v11, v20, v21, v22, v23 + +subs x5, x5, #1 +bne LoopSz_12 + +Tile12End: + +ReduceMax_8 v0, v1, v2, v3, v4, v5, v6, v7 +ReduceMax_4 v8, v9, v10, v11 +st1 {v0.8h}, [x1], #16 +st1 {v8.4h}, [x1], #8 + +sub x3, x3, #12 +add x0, x0, #96 // src += 12 * 4 * 2 +b TILE_12 + +TILE_10: +cmp x3, #10 +blt TILE_8 +mov x5, x2 // src_depth_quad +mov x7, x0 // src +sub x8, x6, #64 // src_step + +// absmax: v0-9 +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x7], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x7], #32 +ld1 {v8.4h, v9.4h}, [x7], x8 +Abs v0, v1, v2, v3 +Abs v4, v5, v6, v7 +fabs v8.4h, v8.4h +fabs v9.4h, v9.4h + +subs x5, x5, #1 +beq Tile10End + +LoopSz_10: +ld1 {v20.4h, v21.4h, v22.4h, v23.4h}, [x7], #32 +ld1 {v24.4h, v25.4h, v26.4h, v27.4h}, [x7], #32 +ld1 {v28.4h, v29.4h}, [x7], x8 + +// absmax = fmax(absmax, abs(x)) +Abs v20, v21, v22, v23 +Abs v24, v25, v26, v27 +fabs v28.4h, v28.4h +fabs v29.4h, v29.4h + +Max v0, v1, v2, v3, v20, v21, v22, v23 +Max v4, v5, v6, v7, v24, v25, v26, v27 +fmax v8.4h, v8.4h, v28.4h +fmax v9.4h, v9.4h, v29.4h + +subs x5, x5, #1 +bne LoopSz_10 + +Tile10End: +fmaxp v0.4h, v0.4h, v1.4h // 0 0 1 1 +fmaxp v0.4h, v0.4h, v0.4h // 0 1 +ReduceMax_8 v2, v3, v4, v5, v6, v7, v8, v9 +st1 {v0.s}[0], [x1], #4 +st1 {v2.8h}, [x1], #16 + +sub x3, x3, #10 +add x0, x0, #80 // src += 10 * 4 * 2 +b TILE_10 + +TILE_8: +cmp x3, #8 +blt TILE_1 +mov x5, x2 // src_depth_quad +mov x7, x0 // src +sub x8, x6, #32 // src_step + +// absmax: v0-7 +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x7], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x7], x8 + +Abs v0, v1, v2, v3 +Abs v4, v5, v6, v7 + +subs x5, x5, #1 +beq Tile8End + +LoopSz_8: +ld1 {v16.4h, v17.4h, v18.4h, v19.4h}, [x7], #32 +ld1 {v20.4h, v21.4h, v22.4h, v23.4h}, [x7], x8 + +// absmax = fmax(absmax, abs(x)) +Abs v16, v17, v18, v19 +Abs v20, v21, v22, v23 +Max v0, v1, v2, v3, v16, v17, v18, v19 +Max v4, v5, v6, v7, v20, v21, v22, v23 + +subs x5, x5, #1 +bne LoopSz_8 + +Tile8End: +ReduceMax_8 v0, v1, v2, v3, v4, v5, v6, v7 +st1 {v0.8h}, [x1], #16 +sub x3, x3, #8 +add x0, x0, #64 // src += 8 * 4 * 2 +b TILE_8 + +TILE_1: +cmp x3, #1 +blt End +mov x5, x2 // src_depth_quad +mov x7, x0 // src + +// absmax: v0 +ld1 {v0.4h}, [x7], x6 +fabs v0.8h, v0.8h +subs x5, x5, #1 +beq Tile1End + +LoopSz_1: +ld1 {v16.4h}, [x7], x6 + +// absmax = fmax(absmax, abs(x)) +fabs v16.4h, v16.4h +fmax v0.4h, v0.4h, v16.4h + +subs x5, x5, #1 +bne LoopSz_1 + +Tile1End: + +fmaxp v2.4h, v0.4h, v0.4h +fmaxp v3.4h, v2.4h, v2.4h +st1 {v3.h}[0], [x1], #2 + +sub x3, x3, #1 +add x0, x0, #8 // src += 1 * 4 * 2 +b TILE_1 + +End: +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 4) +ret + +#endif \ No newline at end of file diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16.S b/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_pack8.S similarity index 96% rename from source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16.S rename to source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_pack8.S index 3e94ce784..bbdb35963 100644 --- a/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16.S +++ b/source/backend/arm82/asm/arm64/low_memory/MNNAbsMaxFP16_pack8.S @@ -1,5 +1,5 @@ // -// MNNAbsMaxFP16.S +// MNNAbsMaxFP16_Pack8.S // MNN // // Created by MNN on 2023/10/31. @@ -50,8 +50,8 @@ fmaxp \s0\().8h, \s0\().8h, \s0\().8h // 0 0 1 1 2 2 3 3 .endm -//void MNNAbsMaxFP16(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) -asm_function MNNAbsMaxFP16 +//void MNNAbsMaxFP16_Pack8(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) +asm_function MNNAbsMaxFP16_Pack8 // x0: source, x1:absmax, x2:src_depth_quad, x3:realSize, x4: pack(no used) stp d14, d15, [sp, #(-16 * 4)]! diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack4.S b/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack4.S new file mode 100644 index 000000000..9d987b33f --- /dev/null +++ b/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack4.S @@ -0,0 +1,492 @@ +// +// MNNDynamicQuantFP16_Pack4.S +// MNN +// +// Created by MNN on 2023/10/31. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +.macro Round z0, z1, z2, z3 + fcvtas \z0\().8h, \z0\().8h + fcvtas \z1\().8h, \z1\().8h + fcvtas \z2\().8h, \z2\().8h + fcvtas \z3\().8h, \z3\().8h +.endm + +//void MNNDynamicQuantFP16(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) +asm_function MNNDynamicQuantFP16_Pack4 + +// x0: src, x1:dst, x2:scale, x3:src_depth_quad, x4:realSize +stp d14, d15, [sp, #(-16 * 4)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] + +// pack=4=LP +Start: +lsl x6, x4, #2 // dst_step = batch * pack * sizeof(int8_t) = batch * 4 = batch << 2 +lsl x7, x4, #3 // src_step = batch * pack * sizeof(float16) = batch * 4 * 2 = batch << 3 + +TILE_24: +cmp x4, #24 +blt TILE_16 +mov x9, x0 // src +mov x10, x1 // dst +sub x15, x6, #64 +mov x12, x3 // src_depth_quad +sub x13, x7, #160 // src_step - 160 + +ld1 {v12.4s, v13.4s, v14.4s, v15.4s}, [x2], #64 +ld1 {v16.4s, v17.4s}, [x2], #32 +fcvtn v12.4h, v12.4s +fcvtn2 v12.8h, v13.4s +fcvtn v13.4h, v14.4s +fcvtn2 v13.8h, v15.4s +fcvtn v14.4h, v16.4s +fcvtn2 v14.8h, v17.4s + +LoopSz_24: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x9], #32 +ld1 {v8.4h, v9.4h, v10.4h, v11.4h}, [x9], #32 +ld1 {v15.4h, v16.4h, v17.4h, v18.4h}, [x9], #32 +ld1 {v19.4h, v20.4h, v21.4h, v22.4h}, [x9], #32 +ld1 {v23.4h, v24.4h, v25.4h, v26.4h}, [x9], x13 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v12.h[0] +fmul v1.4h, v1.4h, v12.h[1] +fmul v2.4h, v2.4h, v12.h[2] +fmul v3.4h, v3.4h, v12.h[3] +fmul v4.4h, v4.4h, v12.h[4] +fmul v5.4h, v5.4h, v12.h[5] +fmul v6.4h, v6.4h, v12.h[6] +fmul v7.4h, v7.4h, v12.h[7] +fmul v8.4h, v8.4h, v13.h[0] +fmul v9.4h, v9.4h, v13.h[1] +fmul v10.4h, v10.4h, v13.h[2] +fmul v11.4h, v11.4h, v13.h[3] +fmul v15.4h, v15.4h, v13.h[4] +fmul v16.4h, v16.4h, v13.h[5] +fmul v17.4h, v17.4h, v13.h[6] +fmul v18.4h, v18.4h, v13.h[7] + +fmul v19.4h, v19.4h, v14.h[0] +fmul v20.4h, v20.4h, v14.h[1] +fmul v21.4h, v21.4h, v14.h[2] +fmul v22.4h, v22.4h, v14.h[3] +fmul v23.4h, v23.4h, v14.h[4] +fmul v24.4h, v24.4h, v14.h[5] +fmul v25.4h, v25.4h, v14.h[6] +fmul v26.4h, v26.4h, v14.h[7] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +mov v4.d[1], v5.d[0] +mov v6.d[1], v7.d[0] +mov v8.d[1], v9.d[0] +mov v10.d[1], v11.d[0] +mov v15.d[1], v16.d[0] +mov v17.d[1], v18.d[0] +mov v19.d[1], v20.d[0] +mov v21.d[1], v22.d[0] +mov v23.d[1], v24.d[0] +mov v25.d[1], v26.d[0] + +// int16_t x = round(x) +Round v0, v2, v4, v6 +Round v8, v10, v15, v17 +Round v19, v21, v23, v25 + +// y = (int8_t)x +sqxtn v27.8b, v0.8h +sqxtn2 v27.16b, v2.8h +sqxtn v28.8b, v4.8h +sqxtn2 v28.16b, v6.8h +sqxtn v29.8b, v8.8h +sqxtn2 v29.16b, v10.8h +sqxtn v30.8b, v15.8h +sqxtn2 v30.16b, v17.8h +sqxtn v0.8b, v19.8h +sqxtn2 v0.16b, v21.8h +sqxtn v1.8b, v23.8h +sqxtn2 v1.16b, v25.8h + +st1 {v27.16b, v28.16b, v29.16b, v30.16b}, [x10], #64 +st1 {v0.16b, v1.16b}, [x10], x15 + +subs x12, x12, #1 +bne LoopSz_24 + +Tile24End: +sub x4, x4, #24 // batch -= 24 +add x0, x0, #192 // src += 24 * 4 * sizeof(float16_t) +add x1, x1, #96 // dst += 24 * 4 * sizeof(int8_t) +b TILE_24 + +TILE_16: +cmp x4, #16 +blt TILE_12 +mov x9, x0 // src +mov x10, x1 // dst +// sub x15, x6, #32 +mov x12, x3 // src_depth_quad +sub x13, x7, #96 // src_step - 192 + +ld1 {v12.4s, v13.4s, v14.4s, v15.4s}, [x2], #64 +fcvtn v12.4h, v12.4s +fcvtn2 v12.8h, v13.4s +fcvtn v13.4h, v14.4s +fcvtn2 v13.8h, v15.4s + +LoopSz_16: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x9], #32 +ld1 {v8.4h, v9.4h, v10.4h, v11.4h}, [x9], #32 +ld1 {v15.4h, v16.4h, v17.4h, v18.4h}, [x9], x13 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v12.h[0] +fmul v1.4h, v1.4h, v12.h[1] +fmul v2.4h, v2.4h, v12.h[2] +fmul v3.4h, v3.4h, v12.h[3] +fmul v4.4h, v4.4h, v12.h[4] +fmul v5.4h, v5.4h, v12.h[5] +fmul v6.4h, v6.4h, v12.h[6] +fmul v7.4h, v7.4h, v12.h[7] +fmul v8.4h, v8.4h, v13.h[0] +fmul v9.4h, v9.4h, v13.h[1] +fmul v10.4h, v10.4h, v13.h[2] +fmul v11.4h, v11.4h, v13.h[3] +fmul v15.4h, v15.4h, v13.h[4] +fmul v16.4h, v16.4h, v13.h[5] +fmul v17.4h, v17.4h, v13.h[6] +fmul v18.4h, v18.4h, v13.h[7] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +mov v4.d[1], v5.d[0] +mov v6.d[1], v7.d[0] +mov v8.d[1], v9.d[0] +mov v10.d[1], v11.d[0] +mov v15.d[1], v16.d[0] +mov v17.d[1], v18.d[0] + +// int16_t x = round(x) +Round v0, v2, v4, v6 +Round v8, v10, v15, v17 + +// y = (int8_t)x +sqxtn v19.8b, v0.8h +sqxtn2 v19.16b, v2.8h +sqxtn v20.8b, v4.8h +sqxtn2 v20.16b, v6.8h +sqxtn v21.8b, v8.8h +sqxtn2 v21.16b, v10.8h +sqxtn v22.8b, v15.8h +sqxtn2 v22.16b, v17.8h + +st1 {v19.16b, v20.16b, v21.16b, v22.16b}, [x10], x6 + +subs x12, x12, #1 +bne LoopSz_16 + +Tile16End: +sub x4, x4, #16 // batch -= 16 +add x0, x0, #128 // src += 16 * 4 * sizeof(float16_t) +add x1, x1, #64 // dst += 16 * 4 * sizeof(int8_t) +b TILE_16 + +TILE_12: +cmp x4, #12 +blt TILE_10 +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad +sub x13, x7, #64 // src_step - 128 + +ld1 {v12.4s, v13.4s, v14.4s}, [x2], #48 +fcvtn v12.4h, v12.4s +fcvtn2 v12.8h, v13.4s +fcvtn v13.4h, v14.4s + +LoopSz_12: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x9], #32 +ld1 {v8.4h, v9.4h, v10.4h, v11.4h}, [x9], x13 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v12.h[0] +fmul v1.4h, v1.4h, v12.h[1] +fmul v2.4h, v2.4h, v12.h[2] +fmul v3.4h, v3.4h, v12.h[3] +fmul v4.4h, v4.4h, v12.h[4] +fmul v5.4h, v5.4h, v12.h[5] +fmul v6.4h, v6.4h, v12.h[6] +fmul v7.4h, v7.4h, v12.h[7] +fmul v8.4h, v8.4h, v13.h[0] +fmul v9.4h, v9.4h, v13.h[1] +fmul v10.4h, v10.4h, v13.h[2] +fmul v11.4h, v11.4h, v13.h[3] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +mov v4.d[1], v5.d[0] +mov v6.d[1], v7.d[0] +mov v8.d[1], v9.d[0] +mov v10.d[1], v11.d[0] + +// int16_t x = round(x) +Round v0, v2, v4, v6 +fcvtas v8.8h, v8.8h +fcvtas v10.8h, v10.8h + +// y = (int8_t)x +sqxtn v14.8b, v0.8h +sqxtn2 v14.16b, v2.8h +sqxtn v15.8b, v4.8h +sqxtn2 v15.16b, v6.8h +sqxtn v16.8b, v8.8h +sqxtn2 v16.16b, v10.8h + +st1 {v14.16b, v15.16b, v16.16b}, [x10], x6 + +subs x12, x12, #1 +bne LoopSz_12 + +Tile12End: +sub x4, x4, #12 // batch -= 12 +add x0, x0, #96 // src += 12 * 4 * sizeof(float16_t) +add x1, x1, #48 // dst += 12 * 4 * sizeof(int8_t) +b TILE_12 + +TILE_10: +cmp x4, #10 +blt TILE_8 +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad +sub x13, x7, #64 // src_step - 64 +sub x15, x6, #32 + +ld1 {v12.4s, v13.4s}, [x2], #32 +ld1 {v14.d}[0], [x2], #8 +fcvtn v10.4h, v12.4s +fcvtn2 v10.8h, v13.4s +fcvtn v11.4h, v14.4s + +LoopSz_10: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x9], #32 +ld1 {v8.4h, v9.4h}, [x9], x13 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v10.h[0] +fmul v1.4h, v1.4h, v10.h[1] +fmul v2.4h, v2.4h, v10.h[2] +fmul v3.4h, v3.4h, v10.h[3] +fmul v4.4h, v4.4h, v10.h[4] +fmul v5.4h, v5.4h, v10.h[5] +fmul v6.4h, v6.4h, v10.h[6] +fmul v7.4h, v7.4h, v10.h[7] +fmul v8.4h, v8.4h, v11.h[0] +fmul v9.4h, v9.4h, v11.h[1] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +mov v4.d[1], v5.d[0] +mov v6.d[1], v7.d[0] +mov v8.d[1], v9.d[0] + +// int16_t x = round(x) +Round v0, v2, v4, v6 +fcvtas v8.8h, v8.8h + +// y = (int8_t)x +sqxtn v0.8b, v0.8h +sqxtn2 v0.16b, v2.8h +sqxtn v1.8b, v4.8h +sqxtn2 v1.16b, v6.8h +sqxtn v2.8b, v8.8h + +st1 {v0.16b, v1.16b}, [x10], #32 +st1 {v2.8b}, [x10], x15 + +subs x12, x12, #1 +bne LoopSz_10 + +Tile10End: +sub x4, x4, #10 // batch -= 10 +add x0, x0, #80 // src += 10 * 4 * sizeof(float16_t) +add x1, x1, #40 // dst += 10 * 4 * sizeof(int8_t) +b TILE_10 + + +TILE_8: +cmp x4, #8 +blt TILE_4 +sub x8, x7, #32 // src_step - 64 +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad + +ld1 {v12.4s, v13.4s}, [x2], #32 +fcvtn v8.4h, v12.4s +fcvtn2 v8.8h, v13.4s + +LoopSz_8: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], #32 +ld1 {v4.4h, v5.4h, v6.4h, v7.4h}, [x9], x8 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v8.h[0] +fmul v1.4h, v1.4h, v8.h[1] +fmul v2.4h, v2.4h, v8.h[2] +fmul v3.4h, v3.4h, v8.h[3] +fmul v4.4h, v4.4h, v8.h[4] +fmul v5.4h, v5.4h, v8.h[5] +fmul v6.4h, v6.4h, v8.h[6] +fmul v7.4h, v7.4h, v8.h[7] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +mov v4.d[1], v5.d[0] +mov v6.d[1], v7.d[0] + +// int16_t x = round(x) +Round v0, v2, v4, v6 + +// y = (int8_t)x +sqxtn v9.8b, v0.8h +sqxtn2 v9.16b, v2.8h +sqxtn v10.8b, v4.8h +sqxtn2 v10.16b, v6.8h + +st1 {v9.16b, v10.16b}, [x10], x6 + +subs x12, x12, #1 +bne LoopSz_8 + +Tile8End: +sub x4, x4, #8 // batch -= 8 +add x0, x0, #64 // src += 8 * 4 * sizeof(float16_t) +add x1, x1, #32 // dst += 8 * 4 * sizeof(int8_t) +b TILE_8 + +TILE_4: +cmp x4, #4 +blt TILE_2 +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad + +ld1 {v12.4s}, [x2], #16 +fcvtn v8.4h, v12.4s + +LoopSz_4: +ld1 {v0.4h, v1.4h, v2.4h, v3.4h}, [x9], x7 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v8.h[0] +fmul v1.4h, v1.4h, v8.h[1] +fmul v2.4h, v2.4h, v8.h[2] +fmul v3.4h, v3.4h, v8.h[3] + +mov v0.d[1], v1.d[0] +mov v2.d[1], v3.d[0] +// int16_t x = round(x) +fcvtas v0.8h, v0.8h +fcvtas v2.8h, v2.8h + +// y = (int8_t)x +sqxtn v4.8b, v0.8h +sqxtn2 v4.16b, v2.8h + +st1 {v4.16b}, [x10], x6 + +subs x12, x12, #1 +bne LoopSz_4 + +Tile4End: +sub x4, x4, #4 // batch -= 4 +add x0, x0, #32 // src += 4 * 4 * sizeof(float16_t) +add x1, x1, #16 // dst += 4 * 4 * sizeof(int8_t) +b TILE_4 + + +TILE_2: +cmp x4, #2 +blt TILE_1 +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad + +ld1 {v12.d}[0], [x2], #8 +fcvtn v8.4h, v12.4s + +LoopSz_2: +ld1 {v0.4h, v1.4h}, [x9], x7 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v8.h[0] +fmul v1.4h, v1.4h, v8.h[1] +mov v0.d[1], v1.d[0] + +// int16_t x = round(x) +fcvtas v0.8h, v0.8h + +// y = (int8_t)x +sqxtn v2.8b, v0.8h + +st1 {v2.8b}, [x10], x6 + +subs x12, x12, #1 +bne LoopSz_2 + +Tile2End: +sub x4, x4, #2 // batch -= 2 +add x0, x0, #16 // src += 2 * 4 * sizeof(float16_t) +add x1, x1, #8 // dst += 2 * 4 * sizeof(int8_t) +b TILE_2 + + +TILE_1: +cmp x4, #1 +blt End +mov x9, x0 // src +mov x10, x1 // dst +mov x12, x3 // src_depth_quad + +ld1 {v12.s}[0], [x2], #4 +fcvtn v8.4h, v12.4s + +LoopSz_1: +ld1 {v0.4h}, [x9], x7 + +// float16_t x = x * quant_scale +fmul v0.4h, v0.4h, v8.h[0] +// int16_t x = round(x) +fcvtas v0.8h, v0.8h +// y = (int8_t)x +sqxtn v0.8b, v0.8h + +st1 {v0.s}[0], [x10], x6 + +subs x12, x12, #1 +bne LoopSz_1 + + +End: +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 4) +ret + +#endif diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16.S b/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack8.S similarity index 99% rename from source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16.S rename to source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack8.S index b39d24994..6ba214cc1 100644 --- a/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16.S +++ b/source/backend/arm82/asm/arm64/low_memory/MNNDynamicQuantFP16_Pack8.S @@ -1,5 +1,5 @@ // -// MNNDynamicQuantFP16.S +// MNNDynamicQuantFP16_Pack8.S // MNN // // Created by MNN on 2023/10/31. @@ -20,7 +20,7 @@ .endm //void MNNDynamicQuantFP16(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) -asm_function MNNDynamicQuantFP16 +asm_function MNNDynamicQuantFP16_Pack8 // x0: src, x1:dst, x2:scale, x3:src_depth_quad, x4:realSize stp d14, d15, [sp, #(-16 * 4)]! @@ -278,7 +278,6 @@ mov x9, x0 // src mov x10, x1 // dst mov x12, x3 // src_depth_quad sub x13, x7, #128 // src_step - 128 -sub x14, x6, #32 // dst_step - 32 sub x15, x6, #64 // quant_scale: v10, v11 diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm82.S b/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm82.S new file mode 100644 index 000000000..1b1d83f10 --- /dev/null +++ b/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm82.S @@ -0,0 +1,260 @@ +// +// MNNGeneralIm2col_Arm82.S +// MNN +// +// Created by MNN on 2024/12/25. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +//void MNNGeneralIm2col_Arm82(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack) +asm_function MNNGeneralIm2col_Arm82 + +// x0:destOrigin, x1:sourceGroup, x2:info, x3:el, x4:LP, x5:pack +stp d14, d15, [sp, #(-16 * 5)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] +stp x19, x20, [sp, #(16 * 4)] + +// load el info +ldr w6, [x2, #0] // number +ldr w7, [x2, #4] // eReal +ldr w15, [x2, #8] // eDest (< EP) +ldr w9, [x2, #12] // offset (stride) +ldr x14, [x1, #0] // src start +lsl x9, x9, #4 // pack*offset*sizeof(float16_t) +// stride +lsl x19, x15, #3 // eDest*LP*sizeof(float16_t) +lsl x7, x7, #4 // eReal*pack*sizeof(float16_t) +mov x20, #3 // Arm82,LP=4 + +LoopNum: + +ldr w10, [x3], #4 // e +ldr w11, [x3], #4 // l +ldr w12, [x3], #4 // eOffset +ldr w13, [x3], #4 // lOffset +// dst address: x2 +and x2, x13, x20 // lR +sub x13, x13, x2 // lOffset-lR +mul x13, x13, x15 // (lOffset-lR)*(eDest) +add x13, x13, x2 // (lOffset-lR)*(eDest)+lR +add x13, x13, x12, LSL #2 // + eoffset*lp +add x2, x0, x13, LSL #1 // *sizeof(float16_t) + +lsl x8, x19, #1 // 2*(eDest*LP*sizeof(float16_t)) +cmp x11, #8 +blt LoopL4 + +LoopL8: +mov x5, x2 +mov x4, x14 +mov x13, x10 +add x12, x2, x19 // eDest*LP*sizeof(float16_t) + +cmp x13, #12 +blt LoopL8E8 + +sub x8, x8, #64 +LoopL8E12: +sub x13, x13, #12 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +ld1 {v8.8h}, [x14], x9 +ld1 {v9.8h}, [x14], x9 +ld1 {v10.8h}, [x14], x9 +ld1 {v11.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +zip1 v14.2d, v4.2d, v5.2d +zip1 v15.2d, v6.2d, v7.2d +zip1 v16.2d, v8.2d, v9.2d +zip1 v17.2d, v10.2d, v11.2d +zip2 v18.2d, v0.2d, v1.2d +zip2 v19.2d, v2.2d, v3.2d +zip2 v20.2d, v4.2d, v5.2d +zip2 v21.2d, v6.2d, v7.2d +zip2 v22.2d, v8.2d, v9.2d +zip2 v23.2d, v10.2d, v11.2d + +st1 {v12.8h, v13.8h, v14.8h, v15.8h}, [x2], #64 +st1 {v16.8h, v17.8h}, [x2], x8 +st1 {v18.8h, v19.8h, v20.8h, v21.8h}, [x12], #64 +st1 {v22.8h, v23.8h}, [x12], x8 +cmp x13, #12 +bge LoopL8E12 +add x8, x8, #64 + +LoopL8E8: +cmp x13, #8 +blt LoopL8E4 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +zip1 v14.2d, v4.2d, v5.2d +zip1 v15.2d, v6.2d, v7.2d +zip2 v18.2d, v0.2d, v1.2d +zip2 v19.2d, v2.2d, v3.2d +zip2 v20.2d, v4.2d, v5.2d +zip2 v21.2d, v6.2d, v7.2d +st1 {v12.8h, v13.8h, v14.8h, v15.8h}, [x2], #64 +st1 {v18.8h, v19.8h, v20.8h, v21.8h}, [x12], #64 +sub x13, x13, #8 + +LoopL8E4: +cmp x13, #4 +blt LoopL8E2 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +zip2 v18.2d, v0.2d, v1.2d +zip2 v19.2d, v2.2d, v3.2d +st1 {v12.8h, v13.8h}, [x2], #32 +st1 {v18.8h, v19.8h}, [x12], #32 +sub x13, x13, #4 + +LoopL8E2: +cmp x13, #2 +blt LoopL8E1 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip2 v18.2d, v0.2d, v1.2d +st1 {v12.8h}, [x2], #16 +st1 {v18.8h}, [x12], #16 +sub x13, x13, #2 + +LoopL8E1: +cmp x13, #1 +blt EndL8LoopE +ld1 {v0.8h}, [x14], x9 +st1 {v0.d}[0], [x2], #8 +st1 {v0.d}[1], [x12], #8 + +EndL8LoopE: +sub x11, x11, #8 +cmp x11, #8 +add x2, x5, x8 // eDest*LP*2*sizeof(float16_t) +add x14, x4, x7 +bge LoopL8 +cbz x11, EndLoopL + +LoopL4: +mov x5, x2 +mov x4, x14 +mov x13, x10 + +cmp x13, #12 +blt LoopL4E8 + +sub x8, x19, #64 +LoopL4E12: +sub x13, x13, #12 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +ld1 {v8.8h}, [x14], x9 +ld1 {v9.8h}, [x14], x9 +ld1 {v10.8h}, [x14], x9 +ld1 {v11.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +zip1 v14.2d, v4.2d, v5.2d +zip1 v15.2d, v6.2d, v7.2d +zip1 v16.2d, v8.2d, v9.2d +zip1 v17.2d, v10.2d, v11.2d + +st1 {v12.8h, v13.8h, v14.8h, v15.8h}, [x2], #64 +st1 {v16.8h, v17.8h}, [x2], x8 +cmp x13, #12 +bge LoopL4E12 + +LoopL4E8: +cmp x13, #8 +blt LoopL4E4 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +zip1 v14.2d, v4.2d, v5.2d +zip1 v15.2d, v6.2d, v7.2d +st1 {v12.8h, v13.8h, v14.8h, v15.8h}, [x2], #64 +sub x13, x13, #8 + +LoopL4E4: +cmp x13, #4 +blt LoopL4E2 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +zip1 v13.2d, v2.2d, v3.2d +st1 {v12.8h, v13.8h}, [x2], #32 +sub x13, x13, #4 + +LoopL4E2: +cmp x13, #2 +blt LoopL4E1 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +zip1 v12.2d, v0.2d, v1.2d +st1 {v12.8h}, [x2], #16 +sub x13, x13, #2 + +LoopL4E1: +cmp x13, #1 +blt EndLoopL +ld1 {v0.8h}, [x14], x9 +st1 {v0.d}[0], [x2], #8 + +EndLoopL: +subs x6, x6, #1 +add x1, x1, #8 +ldr x14, [x1, #0] +bne LoopNum + + +End: +ldp x19, x20, [sp, #(16 * 4)] +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 5) +ret + +#endif + diff --git a/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm86.S b/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm86.S new file mode 100644 index 000000000..3a3b3373c --- /dev/null +++ b/source/backend/arm82/asm/arm64/low_memory/MNNGeneralIm2col_Arm86.S @@ -0,0 +1,139 @@ +// +// MNNGeneralIm2col_Arm86.S +// MNN +// +// Created by MNN on 2024/12/25. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +//void MNNGeneralIm2col_Arm86(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack) +asm_function MNNGeneralIm2col_Arm86 + +// x0:destOrigin, x1:sourceGroup, x2:info, x3:el, x4:LP, x5:pack +stp d14, d15, [sp, #(-16 * 5)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] +stp x19, x20, [sp, #(16 * 4)] + +// load el info +ldr w6, [x2, #0] // number +ldr w7, [x2, #4] // eReal +ldr w8, [x2, #8] // eDest (< EP) +ldr w9, [x2, #12] // offset (stride) +ldr x14, [x1, #0] // src start +lsl x9, x9, #4 // pack*offset*sizeof(float) +// stride +lsl x19, x8, #3 // eDest*LP +lsl x7, x7, #4 // eReal*pack*sizeof(float16_t) +mov x20, #7 // Arm86,LP=8 + +LoopNum: + +ldr w10, [x3], #4 // e +ldr w11, [x3], #4 // l +ldr w12, [x3], #4 // eOffset +ldr w13, [x3], #4 // lOffset +// dst address: x2 +and x2, x13, x20 // lR +sub x13, x13, x2 // lOffset-lR +mul x13, x13, x8 // (lOffset-lR)*(eDest) +add x13, x13, x2 // (lOffset-lR)*(eDest)+lR +add x13, x13, x12, LSL #3 // + eoffset*lp +add x2, x0, x13, LSL #1 // *sizeof(float16_t) + +LoopL8: +mov x5, x2 +mov x4, x14 +mov x13, x10 + +cmp x13, #10 +blt LoopL8E8 + +LoopL8E10: +sub x13, x13, #10 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +ld1 {v8.8h}, [x14], x9 +ld1 {v9.8h}, [x14], x9 +st1 {v0.8h, v1.8h, v2.8h, v3.8h}, [x2], #64 +st1 {v4.8h, v5.8h, v6.8h, v7.8h}, [x2], #64 +st1 {v8.8h, v9.8h}, [x2], #32 +cmp x13, #10 +bge LoopL8E10 + +LoopL8E8: +cmp x13, #8 +blt LoopL8E4 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +ld1 {v4.8h}, [x14], x9 +ld1 {v5.8h}, [x14], x9 +ld1 {v6.8h}, [x14], x9 +ld1 {v7.8h}, [x14], x9 +st1 {v0.8h, v1.8h, v2.8h, v3.8h}, [x2], #64 +st1 {v4.8h, v5.8h, v6.8h, v7.8h}, [x2], #64 +sub x13, x13, #8 + +LoopL8E4: +cmp x13, #4 +blt LoopL8E2 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +ld1 {v2.8h}, [x14], x9 +ld1 {v3.8h}, [x14], x9 +st1 {v0.8h, v1.8h, v2.8h, v3.8h}, [x2], #64 +sub x13, x13, #4 + +LoopL8E2: +cmp x13, #2 +blt LoopL8E1 +ld1 {v0.8h}, [x14], x9 +ld1 {v1.8h}, [x14], x9 +st1 {v0.8h, v1.8h}, [x2], #32 +sub x13, x13, #2 + +LoopL8E1: +cmp x13, #1 +blt EndL8LoopE +ld1 {v0.8h}, [x14], x9 +st1 {v0.8h}, [x2], #16 + +EndL8LoopE: +sub x11, x11, #8 +cmp x11, #8 +add x2, x5, x19, LSL #1 +add x14, x4, x7 +bge LoopL8 + + +subs x6, x6, #1 +add x1, x1, #8 +ldr x14, [x1, #0] +bne LoopNum + + +End: +ldp x19, x20, [sp, #(16 * 4)] +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 5) +ret + +#endif + diff --git a/source/backend/cpu/CPUBackend.cpp b/source/backend/cpu/CPUBackend.cpp index 9720e132f..462baa31d 100644 --- a/source/backend/cpu/CPUBackend.cpp +++ b/source/backend/cpu/CPUBackend.cpp @@ -241,14 +241,14 @@ Backend* CPURuntime::onCreate(const BackendConfig* config, Backend* origin) cons bool autoRemove = true; if (hint().useCachedMmap) { autoRemove = false; - std::string fileName = MNNFilePathConcat(hint().weightMemoryPath, prefix + "0.static"); + std::string fileName = MNNFilePathConcat(hint().weightMemoryPath, prefix + "sync.static"); const_cast(hint()).useCachedMmap += MNNFileExist(fileName.c_str()); } if (nullptr == mStaticAllocatorCache.get()) { // Only support set weightmap dir once mStaticAllocatorCache = mStaticAllocator; auto mmapMem = BufferAllocator::Allocator::createMmap(hint().weightMemoryPath.c_str(), prefix.c_str(), "static", autoRemove); - int mmapSize = hint().mmapFileSize * 1024 * 1024; + size_t mmapSize = static_cast(hint().mmapFileSize) * 1024 * 1024; mStaticAllocator.reset(new EagerBufferAllocator(mmapMem, 32, mmapSize)); } } @@ -445,9 +445,12 @@ CPUBackend::~CPUBackend() { mCacheGroup.clear(); } void CPUBackend::_resetDynamicMemory() const { - mDmaInfo->mDynamicAllocator->apply(); + mRuntime->pCurrentStatus = mDmaInfo->mDynamicAllocator->apply(); + if (NO_ERROR != mRuntime->pCurrentStatus) { + return; + } if (nullptr != mDmaInfo->mDynamicAllocatorBackup.get()) { - mDmaInfo->mDynamicAllocatorBackup->apply(); + mRuntime->pCurrentStatus = mDmaInfo->mDynamicAllocatorBackup->apply(); } } @@ -677,6 +680,7 @@ const Runtime* CPUBackend::getRuntime() { bool CPUBackend::onClearBuffer() { if (nullptr != mRuntime->mStaticAllocatorCache.get()) { + mStaticAllocator->sync(); mStaticAllocator = mRuntime->mStaticAllocatorCache; } mCache->reset(); diff --git a/source/backend/cpu/CPUDeconvolution.cpp b/source/backend/cpu/CPUDeconvolution.cpp index 8bfcf0738..7ade140ad 100644 --- a/source/backend/cpu/CPUDeconvolution.cpp +++ b/source/backend/cpu/CPUDeconvolution.cpp @@ -227,6 +227,11 @@ ErrorCode CPUDeconvolutionOrigin::onResize(const std::vector& inputs, c auto threadNumber = static_cast(backend())->threadNumber(); auto tileCount = UP_DIV(plane, eP); threadNumber = ALIMIN(tileCount, threadNumber); + auto memMode = static_cast(backend())->memoryMode(); + if (memMode != BackendConfig::Memory_High) { + // Limit threadNumber to avoid too large memory + threadNumber = ALIMIN(threadNumber, 4); + } auto im2colOutputStride = input->channel() * eP * core->bytes; mGemmInput = allocator->alloc(threadNumber * im2colOutputStride); auto gemmOutputStride = kernelCount * core->pack * eP * core->bytes; diff --git a/source/backend/cpu/CPUEltwise.cpp b/source/backend/cpu/CPUEltwise.cpp index 5ed9357b6..1c63238ae 100644 --- a/source/backend/cpu/CPUEltwise.cpp +++ b/source/backend/cpu/CPUEltwise.cpp @@ -22,6 +22,7 @@ CPUEltwise::CPUEltwise(Backend *b, EltwiseType type, std::vector coef) : } ErrorCode CPUEltwise::onExecute(const std::vector &inputs, const std::vector &outputs) { + MNN_ASSERT(inputs.size() > 1); auto inputTensor = inputs[0]; const int size = static_cast(backend())->getTensorSize(inputTensor); auto core = static_cast(backend())->functions(); diff --git a/source/backend/cpu/CPURaster.cpp b/source/backend/cpu/CPURaster.cpp index 73dd37152..6d07e26b5 100644 --- a/source/backend/cpu/CPURaster.cpp +++ b/source/backend/cpu/CPURaster.cpp @@ -104,7 +104,7 @@ ErrorCode CPURaster::onResize(const std::vector &____inputs, const std if (des->regions.size() == 1) { OpCommonUtils::turnRegion2Convert(des->regions[0], output, mSingleConvert); if (mSingleConvert.type > 0) { - mUseThreads = (mSingleConvert.batch * mSingleConvert.channel * mSingleConvert.area > LAUNCH_MULTI_THREADS_WORKLOAD) ? true : false; + mUseThreads = (mSingleConvert.batch * mSingleConvert.area > LAUNCH_MULTI_THREADS_WORKLOAD) ? true : false; return NO_ERROR; } } @@ -571,7 +571,7 @@ void CPURaster::tensorConvert(Tensor* input, Tensor* output, int bytes) { const int bitLength = bytes; auto core = static_cast(backend())->functions(); auto threadNumber = static_cast(backend())->threadNumber(); - if (mUseThreads) { + if (!mUseThreads) { threadNumber = 1; } MNN_CONCURRENCY_BEGIN(tId, threadNumber) { @@ -871,8 +871,9 @@ class CPULoop : public Execution { ::memcpy(reg.src.stride, srcView->stride()->data(), 3 * sizeof(int32_t)); ::memcpy(reg.dst.stride, dstView->stride()->data(), 3 * sizeof(int32_t)); auto input = mStack[cmd->indexes()->data()[1]]; - auto inputSize = input->elementSize(); + auto inputSize = input->usize() / input->buffer().type.bytes(); auto output = mStack[cmd->indexes()->data()[0]]; + auto outputSize = output->usize() / output->buffer().type.bytes(); auto bytes = input->getType().bytes(); if (halide_type_float == input->getType().code) { bytes = static_cast(backend())->functions()->bytes; @@ -885,7 +886,7 @@ class CPULoop : public Execution { auto dstIter = *(iter0 + iter0Stride * iter); auto srcOffset = srcIter * step1 + srcView->offset(); auto dstOffset = dstIter * step0 + dstView->offset(); - if (dstOffset >= 0) { + if (dstOffset >= 0 && dstOffset < outputSize) { if (srcOffset >= 0 && srcOffset < inputSize) { _blit(reg, bytes, input->host() + bytes * srcOffset, output->host() + bytes * dstOffset, false); } else { @@ -1114,7 +1115,6 @@ class CPULoop : public Execution { } }; if (mLoop->parallel()) { -// threadNumber = 1; MNN_CONCURRENCY_BEGIN(tId, threadNumber) { for (int iter=tId; iter < mLoop->loopNumber(); iter+=threadNumber) { func(iter, tId); diff --git a/source/backend/cpu/CPURuntime.cpp b/source/backend/cpu/CPURuntime.cpp index 3fbbb2ad6..de63f4d72 100644 --- a/source/backend/cpu/CPURuntime.cpp +++ b/source/backend/cpu/CPURuntime.cpp @@ -16,9 +16,7 @@ #include #include #include -#if defined(__linux__) && defined(__aarch64__) #include -#endif #include #include #include diff --git a/source/backend/cpu/CPUStft.cpp b/source/backend/cpu/CPUStft.cpp index fc24b6b4c..41b42a28d 100644 --- a/source/backend/cpu/CPUStft.cpp +++ b/source/backend/cpu/CPUStft.cpp @@ -11,7 +11,6 @@ #define M_PI 3.141592654 #endif #include -#include #include "backend/cpu/CPUStft.hpp" #include "backend/cpu/CPUBackend.hpp" #include "core/Concurrency.h" @@ -28,10 +27,10 @@ static void MNNDftAbs(const float* input, const float* window, float* output, fl float real_sum = 0.f, imag_sum = 0.f; for (int n = 0; n < nfft; ++n) { float angle = 2 * M_PI * k * n / nfft; - real_sum += buffer[n] * cosf(angle); - imag_sum -= buffer[n] * sinf(angle); + real_sum += buffer[n] * std::cos(angle); + imag_sum -= buffer[n] * std::sin(angle); } - output[k] = sqrtf(real_sum * real_sum + imag_sum * imag_sum); + output[k] = std::sqrt(real_sum * real_sum + imag_sum * imag_sum); } } diff --git a/source/backend/cpu/arm/arm32/MNNExpC8.S b/source/backend/cpu/arm/arm32/MNNExpC8.S index cf1b75585..eac02b106 100644 --- a/source/backend/cpu/arm/arm32/MNNExpC8.S +++ b/source/backend/cpu/arm/arm32/MNNExpC8.S @@ -18,7 +18,7 @@ asm_function MNNExpC8 //r0: dest, r1:source, r2: offset, r3:parameters, r4:countC8 push {r4, r5, lr} ldr r4, [sp, #12] -vpush {q4, q5, q6, q7} +vpush {q4-q7} vmov.i32 q7, #0 ldr r5, [r2, #0] vdup.32 q4, r5 // Alpha @@ -59,6 +59,9 @@ vcvt.f32.s32 q13, q9 vmls.f32 q10, q12, d0[0] vmls.f32 q11, q13, d0[0] +vmul.f32 q10, q10, d1[0] +vmul.f32 q11, q11, d1[0] + .macro MLA_TWO z0 z1 z2 z3 vdup.32 \z1, \z0 vmla.f32 \z1, \z2, \z3 @@ -72,11 +75,16 @@ MLA_TWO d2[0], q12, q10, q14 MLA_TWO d2[0], q13, q11, q15 MLA_TWO d1[1], q14, q10, q12 MLA_TWO d1[1], q15, q11, q13 -MLA_TWO d1[0], q12, q10, q14 -MLA_TWO d1[0], q13, q11, q15 +MLA_TWO d1[1], q12, q10, q14 +MLA_TWO d1[1], q13, q11, q15 //q12, q13 is expRemain +vmul.f32 q12, q12, q12 +vmul.f32 q13, q13, q13 +vmul.f32 q12, q12, q12 +vmul.f32 q13, q13, q13 + vshl.i32 q8, q8, #23 vshl.i32 q9, q9, #23 vadd.i32 q12, q12, q8 @@ -99,7 +107,7 @@ vadd.f32 d14, d14, d15 vadd.f32 d0, d14, d0 vst1.32 {d0[0]}, [r5] -vpop {q4, q5, q6, q7} +vpop {q4-q7} pop {r4, r5, pc} diff --git a/source/backend/cpu/arm/arm64/MNNExpC8.S b/source/backend/cpu/arm/arm64/MNNExpC8.S index 3b89f039d..3edb91f15 100644 --- a/source/backend/cpu/arm/arm64/MNNExpC8.S +++ b/source/backend/cpu/arm/arm64/MNNExpC8.S @@ -55,6 +55,9 @@ scvtf v21.4s, v17.4s fmls v18.4s, v20.4s, v0.s[0] fmls v19.4s, v21.4s, v0.s[0] +fmul v18.4s, v18.4s, v0.s[2] +fmul v19.4s, v19.4s, v0.s[2] + .macro MLA_TWO z0 z1 z2 z3 dup \z1, \z0 fmla \z1, \z2, \z3 @@ -68,10 +71,14 @@ MLA_TWO v1.s[0], v20.4s, v18.4s, v22.4s MLA_TWO v1.s[0], v21.4s, v19.4s, v23.4s MLA_TWO v0.s[3], v22.4s, v18.4s, v20.4s MLA_TWO v0.s[3], v23.4s, v19.4s, v21.4s -MLA_TWO v0.s[2], v20.4s, v18.4s, v22.4s -MLA_TWO v0.s[2], v21.4s, v19.4s, v23.4s +MLA_TWO v0.s[3], v20.4s, v18.4s, v22.4s +MLA_TWO v0.s[3], v21.4s, v19.4s, v23.4s //v20.4s, v21.4s is expRemain +fmul v20.4s, v20.4s, v20.4s +fmul v21.4s, v21.4s, v21.4s +fmul v20.4s, v20.4s, v20.4s +fmul v21.4s, v21.4s, v21.4s ushl v16.4s, v16.4s, v2.4s ushl v17.4s, v17.4s, v2.4s diff --git a/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM82.S b/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM82.S index c62416972..c9daf6f64 100644 --- a/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM82.S +++ b/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM82.S @@ -27,12 +27,14 @@ asm_function MNNSumByAxisLForMatmul_A_ARM82 // MNNSumByAxisLForMatmul_A_ARM82(float_t* dest, int8_t* source, float* dequantScale, ssize_t realDstCount, // ssize_t kernelCountUnitDouble, ssize_t col_buffer_unit_size, ssize_t EP, ssize_t LP, ssize_t blockNum, ssize_t oneScale); // x0: dest, x1: source, x2: dequantScale, x3: realDstCount, x4: sumParams -// x4: kernelCountUnitDouble, x5: col_buffer_unit_size +// x5: oneScale // Load from sp: x8: blockNum +ldr x12, [x4, #48] // Valid ldr x8, [x4, #32] // blockNum ldr x5, [x4, #40] // oneScale -ldr x4, [x4, #0] // kernelCountUnitDouble +ldr x14, [x4, #56] // kx*ky +ldr x4, [x4, #64] // LU stp d14, d15, [sp, #(-16 * 4)]! stp d12, d13, [sp, #(16 * 1)] @@ -40,8 +42,16 @@ stp d10, d11, [sp, #(16 * 2)] stp d8, d9, [sp, #(16 * 3)] movi v31.16b, #1 +mov v29.16b, v31.16b ld1r {v30.4s}, [x2] // Dequant scale sdiv x4, x4, x8 // src_depth_quad per block +cbz x12, TILE_12 +mov x13, #0xFFFFFFFF +lsl x12, x12, #3 +lsl x13, x13, x12 +dup v28.4s, w13 +bic v29.16b, v31.16b, v28.16b + TILE_12: cmp x3, #12 @@ -52,21 +62,30 @@ cbnz x5, TILE12_BLOCK_NUM ld1 {v13.4s, v14.4s, v15.4s}, [x2], #48 // batch quant scale TILE12_BLOCK_NUM: -mov x15, x4 // kernelCountUnitDouble - +mov x15, x14 // kx*ky movi v10.4s, #0 movi v11.4s, #0 movi v12.4s, #0 - +/* for range(kx*ky)...for range(ic/pack) */ TILE12_BLOCK_INNER: +sub x12, x4, #1 // icDiv4 +cbz x12, TILE12_LAST_QUAD +TILE12_PRE_QUAD: ld1 {v0.16b, v1.16b, v2.16b}, [x1], #48 // E: 0,1,2,3,...,11 -subs x15, x15, #1 - .inst 0x4e8097ea // sdot v10.4s, v31.16b, v0.16b // sum LP axis for E0, E1, E2, E3 .inst 0x4e8197eb // sdot v11.4s, v31.16b, v1.16b .inst 0x4e8297ec // sdot v12.4s, v31.16b, v2.16b +subs x12, x12, #1 // icDiv4-- +bne TILE12_PRE_QUAD +TILE12_LAST_QUAD: +ld1 {v0.16b, v1.16b, v2.16b}, [x1], #48 // E: 0,1,2,3,...,11 +.inst 0x4e8097aa // sdot v10.4s, v29.16b, v0.16b // sum LP axis for E0, E1, E2, E3 +.inst 0x4e8197ab // sdot v11.4s, v29.16b, v1.16b +.inst 0x4e8297ac // sdot v12.4s, v29.16b, v2.16b + +subs x15, x15, #1 bne TILE12_BLOCK_INNER TILE12_BLOCK_INNER_END: @@ -114,17 +133,29 @@ cbnz x5, TILE2_BLOCK_NUM ld1 {v13.d}[0], [x2], #8 // batch quant scale TILE2_BLOCK_NUM: -mov x15, x4 // kernelCountUnitDouble +mov x15, x14 // kx*ky movi v10.4s, #0 -TILE2_BLOCK_INNER: +TILE2_BLOCK_INNER: // range(kxky) +sub x12, x4, #1 // icDiv4 +cbz x12, TILE2_LAST_QUAD + +TILE2_PRE_QUAD: // range(icDiv4) ld1 {v0.d}[0], [x7] // E: 0,1 add x7, x7, x6 -subs x15, x15, #1 +subs x12, x12, #1 .inst 0x4e8097ea // sdot v10.4s, v31.16b, v0.16b // sum LP axis for E0 +bne TILE2_PRE_QUAD + +TILE2_LAST_QUAD: +ld1 {v0.d}[0], [x7] // E: 0,1 +add x7, x7, x6 +.inst 0x4e8097aa // sdot v10.4s, v29.16b, v0.16b + +subs x15, x15, #1 // kxky-- bne TILE2_BLOCK_INNER -TILE2_BLOCK_INNER_ENd: +TILE2_BLOCK_INNER_END: scvtf v10.4s, v10.4s cbnz x5, TILE2_MUL_ONE_SCALE @@ -158,15 +189,26 @@ cbnz x5, TILE1_BLOCK_NUM ld1 {v13.s}[0], [x2], #4 // batch quant scale TILE1_BLOCK_NUM: -mov x15, x4 // kernelCountUnitDouble +mov x15, x14 // kx*ky movi v10.4s, #0 TILE1_BLOCK_INNER: +sub x12, x4, #1 +cbz x12, TILE1_LAST_QUAD + +TILE1_PRE_QUAD: ld1 {v0.s}[0], [x7] // E: 0 -subs x15, x15, #1 add x7, x7, x6 .inst 0x4e8097ea // sdot v10.4s, v31.16b, v0.16b // sum LP axis for E0 +subs x12, x12, #1 // icDiv4-- +bne TILE1_PRE_QUAD + +TILE1_LAST_QUAD: +ld1 {v0.s}[0], [x7] // E: 0 +add x7, x7, x6 +.inst 0x4e8097aa // sdot v10.4s, v29.16b, v0.16b +subs x15, x15, #1 // kxky-- bne TILE1_BLOCK_INNER TILE1_BLOCK_INNER_END: diff --git a/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM86.S b/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM86.S index b402cdd9c..cf0f2ac2b 100644 --- a/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM86.S +++ b/source/backend/cpu/arm/arm64/MNNPackC4Int8ForMatMulA_ARM86.S @@ -30,7 +30,9 @@ asm_function MNNSumByAxisLForMatmul_A_ARM86 ldr x6, [x4, #32] // blockNum ldr x12, [x4, #40] // oneScale -ldr x5, [x4, #0] // kernelCountUnitDouble +ldr x11, [x4, #48] // valid +ldr x5, [x4, #56] // kx*ky +ldr x4, [x4, #64] // icDiv4 stp d14, d15, [sp, #(-16 * 4)]! stp d12, d13, [sp, #(16 * 1)] @@ -38,8 +40,18 @@ stp d10, d11, [sp, #(16 * 2)] stp d8, d9, [sp, #(16 * 3)] movi v31.16b, #1 +movi v29.16b, #1 ld1r {v30.4s}, [x2] // dequant scale -sdiv x5, x5, x6 // src_depth_quad_per_block +sdiv x4, x4, x6 // src_depth_quad_per_block + +cbz x11, START +/* mask */ +movi v31.16b, #1 +mov x13, #0xFFFFFFFFFFFFFFFF +lsl x14, x11, #3 +lsl x13, x13, x14 +dup v27.2d, x13 +bic v29.16b, v31.16b, v27.16b START: lsl x11, x3, #2 @@ -58,30 +70,36 @@ ld1 {v5.4s, v6.4s}, [x2], #32 ld1 {v7.d}[0], [x2], #8 TILE10_BLOCK_NUM: -cbz x9, TILE10_END - -mov x15, x5 // kernelCountUnitDouble of a block +mov x15, x5 // kx*ky SET_0 v10, v11, v12, v13 movi v14.4s, #0 -TILE10_BLOCK_SRC_QUAD: - -//Loop_EPxLP: // EP*LP=10*8 +TILE10_BLOCK_INNER: +sub x14, x4, #1 // icDiv4 +cbz x14, TILE10_LAST_QUAD +TILE10_PRE_QUAD: ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x1], #64 // E: 0,1,...,7 -ld1 {v4.16b}, [x1], #16 // E: 8,9 -subs x15, x15, #1 - +ld1 {v4.16b}, [x1], #16 // E: 8,9 +subs x14, x14, #1 // icDiv4-- .inst 0x4e80a7ea // smmla v10.4s, v31.16b, v0.16b // sum LP axis for E0 and E1 .inst 0x4e81a7eb // smmla v11.4s, v31.16b, v1.16b .inst 0x4e82a7ec // smmla v12.4s, v31.16b, v2.16b .inst 0x4e83a7ed // smmla v13.4s, v31.16b, v3.16b .inst 0x4e84a7ee // smmla v14.4s, v31.16b, v4.16b +bne TILE10_PRE_QUAD -bne TILE10_BLOCK_SRC_QUAD +TILE10_LAST_QUAD: +ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x1], #64 // E: 0,1,...,7 +ld1 {v4.16b}, [x1], #16 // E: 8,9 +.inst 0x4e80a7aa // smmla v10.4s, v29.16b, v0.16b +.inst 0x4e81a7ab // smmla v11.4s, v29.16b, v1.16b +.inst 0x4e82a7ac // smmla v12.4s, v29.16b, v2.16b +.inst 0x4e83a7ad // smmla v13.4s, v29.16b, v3.16b +.inst 0x4e84a7ae // smmla v14.4s, v29.16b, v4.16b +subs x15, x15, #1 +bne TILE10_BLOCK_INNER TILE10_PER_BLOCK_END: -sub x9, x9, #1 // blockNum-- - trn1 v20.2d, v10.2d, v11.2d trn1 v21.2d, v12.2d, v13.2d @@ -101,9 +119,10 @@ fmul v21.4s, v21.4s, v30.4s fmul v14.4s, v14.4s, v30.4s TILE10_STORE: +subs x9, x9, #1 // blockNum-- st1 {v20.4s, v21.4s}, [x0], #32 st1 {v14.d}[0], [x0], #8 -b TILE10_BLOCK_NUM // Finish one block +bne TILE10_BLOCK_NUM // Finish one block TILE10_END: sub x3, x3, #10 // realDstCount-=10 @@ -129,24 +148,34 @@ ld1 {v5.4s, v6.4s}, [x2], #32 TILE8_BLOCK_NUM: cbz x9, TILE8_END -mov x15, x5 // kernelCountUnitDouble - +mov x15, x5 // kx*ky SET_0 v10, v11, v12, v13 -TILE8_BLOCK_SRC_QUAD: - +TILE8_BLOCK_INNER: +sub x14, x4, #1 +cbz x14, TILE8_LAST_QUAD +TILE8_PRE_QUAD: ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x7] // E: 0,1,...,7 -subs x15, x15, #1 add x7, x7, x8 // x7=x7+EP*LP .inst 0x4e80a7ea // smmla v10.4s, v31.16b, v0.16b // sum LP axis for E0 and E1 .inst 0x4e81a7eb // smmla v11.4s, v31.16b, v1.16b .inst 0x4e82a7ec // smmla v12.4s, v31.16b, v2.16b .inst 0x4e83a7ed // smmla v13.4s, v31.16b, v3.16b +subs x14, x14, #1 +bne TILE8_PRE_QUAD + +TILE8_LAST_QUAD: +ld1 {v0.16b, v1.16b, v2.16b, v3.16b}, [x7] // E: 0,1,...,7 +add x7, x7, x8 // x7=x7+EP*LP +.inst 0x4e80a7aa // smmla v10.4s, v29.16b, v0.16b // sum LP axis for E0 and E1 +.inst 0x4e81a7ab // smmla v11.4s, v29.16b, v1.16b +.inst 0x4e82a7ac // smmla v12.4s, v29.16b, v2.16b +.inst 0x4e83a7ad // smmla v13.4s, v29.16b, v3.16b -bne TILE8_BLOCK_SRC_QUAD +subs x15, x15, #1 // kxky-- +bne TILE8_BLOCK_INNER TILE8_PER_BLOCK_END: -sub x9, x9, #1 // blockNum-- trn1 v20.2d, v10.2d, v11.2d trn1 v21.2d, v12.2d, v13.2d @@ -164,8 +193,9 @@ fmul v20.4s, v20.4s, v30.4s fmul v21.4s, v21.4s, v30.4s TILE8_STORE: +subs x9, x9, #1 // blockNum-- st1 {v20.4s, v21.4s}, [x10], x11 // Go to next block for this 8 remain. -b TILE8_BLOCK_NUM +bne TILE8_BLOCK_NUM TILE8_END: add x0, x0, #32 // finish 8 dstCount * sizeof(float) @@ -185,24 +215,31 @@ cbnz x12, TILE4_BLOCK_NUM ld1 {v5.4s}, [x2], #16 TILE4_BLOCK_NUM: -cbz x9, TILE4_END -mov x15, x5 // kernelCountUnitDouble +mov x15, x5 // kx*ky movi v10.4s, #0 movi v11.4s, #0 -TILE4_BLOCK_SRC_QUAD: - +TILE4_BLOCK_INNER: +sub x14, x4, #1 +cbz x14, TILE4_LAST_QUAD +TILE4_PRE_QUAD: ld1 {v0.16b, v1.16b}, [x7] // E: 0,1,2,3 -subs x15, x15, #1 add x7, x7, x8 +subs x14, x14, #1 .inst 0x4e80a7ea // smmla v10.4s, v31.16b, v0.16b // sum LP axis for E0 and E1 .inst 0x4e81a7eb // smmla v11.4s, v31.16b, v1.16b +bne TILE4_PRE_QUAD + +TILE4_LAST_QUAD: +ld1 {v0.16b, v1.16b}, [x7] // E: 0,1,2,3 +add x7, x7, x8 +.inst 0x4e80a7aa // smmla v10.4s, v29.16b, v0.16b // sum LP axis for E0 and E1 +.inst 0x4e81a7ab // smmla v11.4s, v29.16b, v1.16b -bne TILE4_BLOCK_SRC_QUAD +subs x15, x15, #1 +bne TILE4_BLOCK_INNER TILE4_PER_BLOCK_END: -sub x9, x9, #1 // blockNum-- - trn1 v20.2d, v10.2d, v11.2d scvtf v20.4s, v20.4s @@ -212,8 +249,9 @@ b TILE4_STORE TILE4_ONE_SCALE: fmul v20.4s, v20.4s, v30.4s TILE4_STORE: +subs x9, x9, #1 // blockNum-- st1 {v20.4s}, [x10], x11 -b TILE4_BLOCK_NUM +bne TILE4_BLOCK_NUM TILE4_END: add x0, x0, #16 // finish 4 dstCount * sizeof(float) @@ -230,21 +268,30 @@ mov x9, x6 // blockNum cbnz x12, TILE2_BLOCK_NUM ld1 {v5.d}[0], [x2], #8 + TILE2_BLOCK_NUM: cbz x9, TILE2_END -mov x15, x5 // kernelCountUnitDouble - +mov x15, x5 // kx*ky movi v10.4s, #0 -TILE2_BLOCK_SRC_QUAD: +TILE2_BLOCK_INNER: +sub x14, x4, #1 +cbz x14, TILE2_LAST_QUAD +TILE2_PRE_QUAD: ld1 {v0.16b}, [x7] // E: 0,1 -subs x15, x15, #1 add x7, x7, x8 - .inst 0x4e80a7ea // smmla v10.4s, v31.16b, v0.16b // sum LP axis for E0 and E1 +subs x14, x14, #1 +bne TILE2_PRE_QUAD + +TILE2_LAST_QUAD: +ld1 {v0.16b}, [x7] // E: 0,1 +add x7, x7, x8 +.inst 0x4e80a7aa // smmla v10.4s, v29.16b, v0.16b // sum LP axis for E0 and E1 -bne TILE2_BLOCK_SRC_QUAD +subs x15, x15, #1 +bne TILE2_BLOCK_INNER TILE2_PER_BLOCK_END: sub x9, x9, #1 // blockNum-- @@ -276,22 +323,29 @@ cbnz x12, TILE1_BLOCK_NUM ld1 {v5.s}[0], [x2], #4 TILE1_BLOCK_NUM: -cbz x9, TILE1_END -mov x15, x5 // kernelCountUnitDouble +mov x15, x5 // kx*ky movi v10.4s, #0 -TILE1_BLOCK_SRC_QUAD: +TILE1_BLOCK_INNER: +sub x14, x4, #1 +cbz x14, TILE1_LAST_QUAD +TILE1_PRE_QUAD: ld1 {v0.d}[0], [x7] // E: 0 -subs x15, x15, #1 add x7, x7, x8 .inst 0x4e80a7ea // smmla v10.4s, v31.16b, v0.16b // sum LP axis for E0 +subs x14, x14, #1 +bne TILE1_PRE_QUAD + +TILE1_LAST_QUAD: +ld1 {v0.d}[0], [x7] // E: 0 +add x7, x7, x8 +.inst 0x4e80a7aa // smmla v10.4s, v29.16b, v0.16b // sum LP axis for E0 -bne TILE1_BLOCK_SRC_QUAD +subs x15, x15, #1 +bne TILE1_BLOCK_INNER TILE1_PER_BLOCK_END: -sub x9, x9, #1 // blockNum-- - scvtf v10.4s, v10.4s cbnz x12, TILE1_ONE_SCALE @@ -301,8 +355,9 @@ b TILE1_STORE TILE1_ONE_SCALE: fmul v10.4s, v10.4s, v30.4s TILE1_STORE: +subs x9, x9, #1 // blockNum-- st1 {v10.s}[0], [x10], x11 -b TILE1_BLOCK_NUM +bne TILE1_BLOCK_NUM TILE1_END: sub x3, x3, #1 // realDstCount-=1 diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNAbsMaxFP32.S b/source/backend/cpu/arm/arm64/low_memory/MNNAbsMaxFP32.S index c22593fc5..8318e392b 100644 --- a/source/backend/cpu/arm/arm64/low_memory/MNNAbsMaxFP32.S +++ b/source/backend/cpu/arm/arm64/low_memory/MNNAbsMaxFP32.S @@ -1,5 +1,5 @@ // -// MNNAbsMaxFP32.S +// MNNAbsMaxFP32_Pack4.S // // Created by MNN on 2023/10/31. // Copyright © 2018, Alibaba Group Holding Limited @@ -30,8 +30,8 @@ fmaxp \s2\().4s, \s2\().4s, \s3\().4s // 2 2 3 3 fmaxp \z0\().4s, \s0\().4s, \s2\().4s // 0 1 2 3 .endm -//void MNNAbsMaxFP32(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) -asm_function MNNAbsMaxFP32 +//void MNNAbsMaxFP32_Pack4(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) +asm_function MNNAbsMaxFP32_Pack4 // x0: source, x1:absmax, x2:src_depth_quad, x3:realSize stp d14, d15, [sp, #(-16 * 4)]! diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNDynamicQuantFP32.S b/source/backend/cpu/arm/arm64/low_memory/MNNDynamicQuantFP32.S index f8971acaa..43585ead9 100644 --- a/source/backend/cpu/arm/arm64/low_memory/MNNDynamicQuantFP32.S +++ b/source/backend/cpu/arm/arm64/low_memory/MNNDynamicQuantFP32.S @@ -1,5 +1,5 @@ // -// MNNDynamicQuantFP32.S +// MNNDynamicQuantFP32_Pack4.S // MNN // // Created by MNN on 2023/10/31. @@ -37,8 +37,8 @@ add \d0\().4s, \d0\().4s, \d2\().4s .endm -//void MNNDynamicQuantFP32(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) -asm_function MNNDynamicQuantFP32 +//void MNNDynamicQuantFP32_Pack4(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) +asm_function MNNDynamicQuantFP32_Pack4 // x0: src, x1:dst, x2:scale, x3:src_depth_quad, x4:realSize stp d14, d15, [sp, #(-16 * 4)]! diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm82.S b/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm82.S new file mode 100644 index 000000000..50d6dcccb --- /dev/null +++ b/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm82.S @@ -0,0 +1,140 @@ +// +// MNNGeneralIm2col_Fp32Arm82.S +// MNN +// +// Created by MNN on 2025/01/13. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +//void MNNGeneralIm2col_Fp32Arm82(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack) +asm_function MNNGeneralIm2col_Fp32Arm82 + +// x0:destOrigin, x1:sourceGroup, x2:info, x3:el, x4:LP, x5:pack +stp d14, d15, [sp, #(-16 * 5)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] +stp x19, x20, [sp, #(16 * 4)] + +// load el info +ldr w6, [x2, #0] // number +ldr w7, [x2, #4] // eReal +ldr w15, [x2, #8] // eDest (< EP) +ldr w9, [x2, #12] // offset (stride) +ldr x14, [x1, #0] // src start +lsl x9, x9, #4 // pack*offset*sizeof(float32_t) +// stride +lsl x19, x15, #4 // eDest*LP*sizeof(float32_t) +lsl x7, x7, #4 // eReal*pack*sizeof(float32_t) +mov x20, #3 // Arm82,LP=4 + +LoopNum: + +ldr w10, [x3], #4 // e +ldr w11, [x3], #4 // l +ldr w12, [x3], #4 // eOffset +ldr w13, [x3], #4 // lOffset +// dst address: x2 +and x2, x13, x20 // lR +sub x13, x13, x2 // lOffset-lR +mul x13, x13, x15 // (lOffset-lR)*(eDest) +add x13, x13, x2 // (lOffset-lR)*(eDest)+lR +add x13, x13, x12, LSL #2 // + eoffset*lp +add x2, x0, x13, LSL #2 // *sizeof(float32_t) + +LoopL4: +mov x5, x2 +mov x4, x14 +mov x13, x10 + +cmp x13, #12 +blt LoopL4E8 + +LoopL4E12: +sub x13, x13, #12 +ld1 {v0.4s}, [x14], x9 +ld1 {v1.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v3.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v5.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v7.4s}, [x14], x9 +ld1 {v8.4s}, [x14], x9 +ld1 {v9.4s}, [x14], x9 +ld1 {v10.4s}, [x14], x9 +ld1 {v11.4s}, [x14], x9 + +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +st1 {v4.4s, v5.4s, v6.4s, v7.4s}, [x2], #64 +st1 {v8.4s, v9.4s, v10.4s, v11.4s}, [x2], #64 +cmp x13, #12 +bge LoopL4E12 + +LoopL4E8: +cmp x13, #8 +blt LoopL4E4 +ld1 {v0.4s}, [x14], x9 +ld1 {v1.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v3.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v5.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v7.4s}, [x14], x9 +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +st1 {v4.4s, v5.4s, v6.4s, v7.4s}, [x2], #64 +sub x13, x13, #8 + +LoopL4E4: +cmp x13, #4 +blt LoopL4E2 +ld1 {v0.4s}, [x14], x9 +ld1 {v1.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v3.4s}, [x14], x9 +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +sub x13, x13, #4 + +LoopL4E2: +cmp x13, #2 +blt LoopL4E1 +ld1 {v0.4s}, [x14], x9 +ld1 {v1.4s}, [x14], x9 +st1 {v0.4s, v1.4s}, [x2], #32 +sub x13, x13, #2 + +LoopL4E1: +cmp x13, #1 +blt EndL4LoopE +ld1 {v0.4s}, [x14], x9 +st1 {v0.4s}, [x2], #16 + +EndL4LoopE: +add x2, x5, x19 // eDest*LP*sizeof(float32_t) +add x14, x4, x7 +subs x11, x11, #4 +bne LoopL4 + +EndLoopL: +subs x6, x6, #1 +add x1, x1, #8 +ldr x14, [x1, #0] +bne LoopNum + + +End: +ldp x19, x20, [sp, #(16 * 4)] +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 5) +ret + +#endif \ No newline at end of file diff --git a/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm86.S b/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm86.S new file mode 100644 index 000000000..cadc63461 --- /dev/null +++ b/source/backend/cpu/arm/arm64/low_memory/MNNGeneralIm2col_Fp32Arm86.S @@ -0,0 +1,264 @@ +// +// MNNGeneralIm2col_Fp32Arm86.S +// MNN +// +// Created by MNN on 2025/01/14. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifdef __aarch64__ + +#include "MNNAsmGlobal.h" +.text +.align 5 + +//void MNNGeneralIm2col_Fp32Arm86(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack) +asm_function MNNGeneralIm2col_Fp32Arm86 + +// x0:destOrigin, x1:sourceGroup, x2:info, x3:el, x4:LP, x5:pack +stp d14, d15, [sp, #(-16 * 5)]! +stp d12, d13, [sp, #(16 * 1)] +stp d10, d11, [sp, #(16 * 2)] +stp d8, d9, [sp, #(16 * 3)] +stp x19, x20, [sp, #(16 * 4)] + +// load el info +ldr w6, [x2, #0] // number +ldr w7, [x2, #4] // eReal +ldr w15, [x2, #8] // eDest (< EP) +ldr w9, [x2, #12] // offset (stride) +ldr x14, [x1, #0] // src start +lsl x9, x9, #4 // pack*offset*sizeof(float32_t) +// stride +lsl x19, x15, #5 // eDest*LP*sizeof(float32_t) +lsl x7, x7, #4 // eReal*pack*sizeof(float32_t) +mov x20, #7 // Arm86,LP=8 + +LoopNum: + +ldr w10, [x3], #4 // e +ldr w11, [x3], #4 // l +ldr w12, [x3], #4 // eOffset +ldr w13, [x3], #4 // lOffset +// dst address: x2 +and x2, x13, x20 // lR +sub x13, x13, x2 // lOffset-lR +mul x13, x13, x15 // (lOffset-lR)*(eDest) +add x13, x13, x2 // (lOffset-lR)*(eDest)+lR +add x13, x13, x12, LSL #3 // + eoffset*lp +add x2, x0, x13, LSL #2 // *sizeof(float32_t) + +cmp x11, #8 +blt LoopL4 + +LoopL8: +mov x5, x2 +mov x4, x14 +mov x13, x10 +add x12, x14, x7 + +cmp x13, #10 +blt LoopL8E8 + +LoopL8E10: +sub x13, x13, #10 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v8.4s}, [x14], x9 +ld1 {v10.4s}, [x14], x9 +ld1 {v12.4s}, [x14], x9 +ld1 {v14.4s}, [x14], x9 +ld1 {v16.4s}, [x14], x9 +ld1 {v18.4s}, [x14], x9 + +ld1 {v1.4s}, [x12], x9 +ld1 {v3.4s}, [x12], x9 +ld1 {v5.4s}, [x12], x9 +ld1 {v7.4s}, [x12], x9 +ld1 {v9.4s}, [x12], x9 +ld1 {v11.4s}, [x12], x9 +ld1 {v13.4s}, [x12], x9 +ld1 {v15.4s}, [x12], x9 +ld1 {v17.4s}, [x12], x9 +ld1 {v19.4s}, [x12], x9 + +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +st1 {v4.4s, v5.4s, v6.4s, v7.4s}, [x2], #64 +st1 {v8.4s, v9.4s, v10.4s, v11.4s}, [x2], #64 +st1 {v12.4s, v13.4s, v14.4s, v15.4s}, [x2], #64 +st1 {v16.4s, v17.4s, v18.4s, v19.4s}, [x2], #64 +cmp x13, #10 +bge LoopL8E10 + +LoopL8E8: +cmp x13, #8 +blt LoopL8E4 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v8.4s}, [x14], x9 +ld1 {v10.4s}, [x14], x9 +ld1 {v12.4s}, [x14], x9 +ld1 {v14.4s}, [x14], x9 + +ld1 {v1.4s}, [x12], x9 +ld1 {v3.4s}, [x12], x9 +ld1 {v5.4s}, [x12], x9 +ld1 {v7.4s}, [x12], x9 +ld1 {v9.4s}, [x12], x9 +ld1 {v11.4s}, [x12], x9 +ld1 {v13.4s}, [x12], x9 +ld1 {v15.4s}, [x12], x9 +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +st1 {v4.4s, v5.4s, v6.4s, v7.4s}, [x2], #64 +st1 {v8.4s, v9.4s, v10.4s, v11.4s}, [x2], #64 +st1 {v12.4s, v13.4s, v14.4s, v15.4s}, [x2], #64 +sub x13, x13, #8 + +LoopL8E4: +cmp x13, #4 +blt LoopL8E2 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 + +ld1 {v1.4s}, [x12], x9 +ld1 {v3.4s}, [x12], x9 +ld1 {v5.4s}, [x12], x9 +ld1 {v7.4s}, [x12], x9 +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +st1 {v4.4s, v5.4s, v6.4s, v7.4s}, [x2], #64 +sub x13, x13, #4 + +LoopL8E2: +cmp x13, #2 +blt LoopL8E1 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 + +ld1 {v1.4s}, [x12], x9 +ld1 {v3.4s}, [x12], x9 +st1 {v0.4s, v1.4s, v2.4s, v3.4s}, [x2], #64 +sub x13, x13, #2 + +LoopL8E1: +cmp x13, #1 +blt EndL8LoopE +ld1 {v0.4s}, [x14], x9 + +ld1 {v1.4s}, [x12], x9 +st1 {v0.4s, v1.4s}, [x2], #32 + +EndL8LoopE: +sub x11, x11, #8 +add x2, x5, x19 // + eDest*LP*sizeof(float32_t) +add x14, x4, x7, LSL #1 +cmp x11, #8 +bge LoopL8 +cbz x11, EndLoopL + +LoopL4: +mov x13, x10 +mov x5, #32 + +cmp x13, #10 +blt LoopL4E8 + +LoopL4E10: +sub x13, x13, #10 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v8.4s}, [x14], x9 +ld1 {v10.4s}, [x14], x9 +ld1 {v12.4s}, [x14], x9 +ld1 {v14.4s}, [x14], x9 +ld1 {v16.4s}, [x14], x9 +ld1 {v18.4s}, [x14], x9 + +st1 {v0.4s}, [x2], x5 +st1 {v2.4s}, [x2], x5 +st1 {v4.4s}, [x2], x5 +st1 {v6.4s}, [x2], x5 +st1 {v8.4s}, [x2], x5 +st1 {v10.4s}, [x2], x5 +st1 {v12.4s}, [x2], x5 +st1 {v14.4s}, [x2], x5 +st1 {v16.4s}, [x2], x5 +st1 {v18.4s}, [x2], x5 + +cmp x13, #10 +bge LoopL4E10 + +LoopL4E8: +cmp x13, #8 +blt LoopL4E4 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 +ld1 {v8.4s}, [x14], x9 +ld1 {v10.4s}, [x14], x9 +ld1 {v12.4s}, [x14], x9 +ld1 {v14.4s}, [x14], x9 + +st1 {v0.4s}, [x2], x5 +st1 {v2.4s}, [x2], x5 +st1 {v4.4s}, [x2], x5 +st1 {v6.4s}, [x2], x5 +st1 {v8.4s}, [x2], x5 +st1 {v10.4s}, [x2], x5 +st1 {v12.4s}, [x2], x5 +st1 {v14.4s}, [x2], x5 +sub x13, x13, #8 + +LoopL4E4: +cmp x13, #4 +blt LoopL4E2 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +ld1 {v4.4s}, [x14], x9 +ld1 {v6.4s}, [x14], x9 + +st1 {v0.4s}, [x2], x5 +st1 {v2.4s}, [x2], x5 +st1 {v4.4s}, [x2], x5 +st1 {v6.4s}, [x2], x5 +sub x13, x13, #4 + +LoopL4E2: +cmp x13, #2 +blt LoopL4E1 +ld1 {v0.4s}, [x14], x9 +ld1 {v2.4s}, [x14], x9 +st1 {v0.4s}, [x2], x5 +st1 {v2.4s}, [x2], x5 +sub x13, x13, #2 + +LoopL4E1: +cmp x13, #1 +blt EndLoopL +ld1 {v0.4s}, [x14], x9 +st1 {v0.4s}, [x2], x5 + +EndLoopL: +subs x6, x6, #1 +add x1, x1, #8 +ldr x14, [x1, #0] +bne LoopNum + + +End: +ldp x19, x20, [sp, #(16 * 4)] +ldp d8, d9, [sp, #(16 * 3)] +ldp d10, d11, [sp, #(16 * 2)] +ldp d12, d13, [sp, #(16 * 1)] +ldp d14, d15, [sp], #(16 * 5) +ret + +#endif \ No newline at end of file diff --git a/source/backend/cpu/bf16/BF16Functions.cpp b/source/backend/cpu/bf16/BF16Functions.cpp index 852cd791b..26c9db357 100644 --- a/source/backend/cpu/bf16/BF16Functions.cpp +++ b/source/backend/cpu/bf16/BF16Functions.cpp @@ -354,8 +354,6 @@ bool BF16Functions::init() { gInstance->MNNPackedMatMulRemain = ARMV86_MNNPackedMatMulRemain_BF16; } #endif - gInstance->MNNPackedMatMul_int4 = nullptr; - gInstance->MNNPackedMatMul_int8 = nullptr; // TODO: raw cpu version of bf16 return true; #endif diff --git a/source/backend/cpu/compute/CommonOptFunction.cpp b/source/backend/cpu/compute/CommonOptFunction.cpp index cbe42ebee..7090f3121 100644 --- a/source/backend/cpu/compute/CommonOptFunction.cpp +++ b/source/backend/cpu/compute/CommonOptFunction.cpp @@ -194,20 +194,6 @@ void MNNPackedMatMulRemain_int8(float* C, const float* A, const float* B, size_t #endif // MNN_CPU_WEIGHT_DEQUANT_GEMM #ifdef MNN_LOW_MEMORY -void MNNAbsMaxFP32(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) { - // source: (ic/4, N, 4) - auto srcStep = pack * realSize; - for (int i = 0; i < realSize; ++i) { - float absmaxVal = 0.f; // absmaxVal>=0 - for (int c = 0; c < src_depth_quad; ++c) { - auto src = source + c * srcStep + i * pack; - for (int k = 0; k < pack; ++k) { - absmaxVal = std::max(absmaxVal, std::abs(src[k])); - } - } - absmax[i] = absmaxVal; - } -} void MNNQuantScaleFP32(float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch) { for (int i = 0; i < batch; ++i) { auto absmaxPtr = absmax + i; @@ -234,7 +220,53 @@ void MNNQuantSumFP32(float* sum, const float* dequant_scale, size_t thread, size sum[i] = sumVal * dequant_scale[i]; } } + +void MNNDynamicUpdateConvBiasScale(float* newbias, float* newscale, float* oldbias, float* weightScale, float* inputScale, float* weightKernelSum, float* inputZero, size_t ocQuad, size_t scaleSize) { + int ocUp4 = 4 * ocQuad; + int pack = 4; + int blockNum = scaleSize / ocUp4; + for (int i = 0; i < ocUp4; ++i) { + newbias[i] = oldbias[i] - weightKernelSum[i] * inputZero[0]; + } + for (int k = 0; k < blockNum; ++k) { + for (int i = 0; i < ocUp4; ++i) { + newscale[i + k * ocUp4] = weightScale[i + k * ocUp4] * inputScale[0]; + } + } +} + +#endif // LOW_MEMORY +#endif // not __aarch64__ + +#ifdef MNN_LOW_MEMORY +static void MNNAbsMaxFP32(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) { +#ifdef __aarch64__ + if (pack == 4) { + MNNAbsMaxFP32_Pack4(source, absmax, src_depth_quad, realSize, pack); + return; + } +#endif + // source: (ic/4, N, 4) + auto srcStep = pack * realSize; + for (int i = 0; i < realSize; ++i) { + float absmaxVal = 0.f; // absmaxVal>=0 + for (int c = 0; c < src_depth_quad; ++c) { + auto src = source + c * srcStep + i * pack; + for (int k = 0; k < pack; ++k) { + absmaxVal = std::max(absmaxVal, std::abs(src[k])); + } + } + absmax[i] = absmaxVal; + } +} + void MNNDynamicQuantFP32(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) { +#ifdef __aarch64__ + if (pack == 4) { + MNNDynamicQuantFP32_Pack4(src, dst, scale, src_depth_quad, realSize, pack); + return; + } +#endif #ifdef MNN_USE_SSE uint8_t* dstPtr = reinterpret_cast(dst); int offset = 128; @@ -254,24 +286,7 @@ void MNNDynamicQuantFP32(const float* src, int8_t* dst, const float* scale, size } } } - -void MNNDynamicUpdateConvBiasScale(float* newbias, float* newscale, float* oldbias, float* weightScale, float* inputScale, float* weightKernelSum, float* inputZero, size_t ocQuad, size_t scaleSize) { - int ocUp4 = 4 * ocQuad; - int pack = 4; - int blockNum = scaleSize / ocUp4; - for (int i = 0; i < ocUp4; ++i) { - newbias[i] = oldbias[i] - weightKernelSum[i] * inputZero[0]; - } - for (int k = 0; k < blockNum; ++k) { - for (int i = 0; i < ocUp4; ++i) { - newscale[i + k * ocUp4] = weightScale[i + k * ocUp4] * inputScale[0]; - } - } -} - -#endif // LOW_MEMORY -#endif // not __aarch64__ - +#endif static void MNNSumByAxisLForMatmul_A(float* dest, int8_t* source, const float* scale, ssize_t realDstCount, SumByAxisParams sumParams) { #ifdef MNN_USE_SSE @@ -280,30 +295,39 @@ static void MNNSumByAxisLForMatmul_A(float* dest, int8_t* source, const float* s int8_t* srcInt8 = source; #endif auto scalePtr = scale; - auto kernelCountUnitDouble = sumParams.kernelCountUnitDouble; auto blockNum = sumParams.blockNum; auto EP = sumParams.DST_XUNIT; auto LP = sumParams.SRC_UNIT; - auto blockSizeQuad = kernelCountUnitDouble / blockNum; auto col_buffer_unit_size = sumParams.col_buffer_unit_size; auto oneScale = sumParams.oneScale; + auto LU = sumParams.LU; + auto valid = sumParams.valid; + auto kernelxy = sumParams.kernelxy; + auto blockSizeQuad = LU / blockNum; + auto lastL = LP; + if (valid) { + lastL = valid; + } + float singlescale = scale[0]; do { int step = ALIMIN(EP, realDstCount); for (int k = 0; k < blockNum; ++k) { - // const auto src_x = srcInt8 + w * LP; const auto src_x = srcInt8 + k * (step * LP * blockSizeQuad); for (int w = 0; w < step; ++w) { - float dequantScale = scale[0]; + float dequantScale = singlescale; if (oneScale == 0) { dequantScale = scalePtr[w]; } int sumint32 = 0; const auto src_y = src_x + w * LP; - for (int j = 0; j < blockSizeQuad; ++j) { - const auto src_z = src_y + j * (step * LP); - for (int i = 0; i < LP; ++i) { - sumint32 += src_z[i]; + for (int j = 0; j < kernelxy; ++j) { + for (int i = 0; i < blockSizeQuad; ++i) { + auto sumsize = i == (blockSizeQuad - 1) ? lastL : LP; + const auto src_z = src_y + j * (blockSizeQuad * step * LP) + i * step * LP; + for (int x = 0; x < sumsize; ++x) { + sumint32 += src_z[x]; + } } } dest[w + k * step] = dequantScale * static_cast(sumint32); @@ -1947,10 +1971,12 @@ void MNNExpC8(float* dest, const float* source, float* offset, const float* para int div2 = (div + 127) << 23; auto xReamin = x - div * param; float expBasic = *(float*)(&div2); - auto t = xReamin; + auto t = xReamin * 0.25f; auto expRemain = - ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + parameters[3]) * t + - parameters[2]; + ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t + + 1.0f; + expRemain = expRemain * expRemain; + expRemain = expRemain * expRemain; dest[i] = expBasic * expRemain + offset[1]; summer+= dest[i]; } @@ -2125,8 +2151,16 @@ void MNNNorm(float *dst, const float *src, const float *gamma, const float *beta for (int j = 0; j < size; ++j) { square_sum += (src[j] - mean) * (src[j] - mean); } +#ifdef __aarch64__ + auto vs = vadd_f32(vdiv_f32(vdup_n_f32(square_sum), vdup_n_f32(size)), vdup_n_f32(epsilon)); + auto vecs = vdiv_f32(vdup_n_f32(1.0f), vsqrt_f32(vs)); + float vars[2]; + vst1_f32(vars, vecs); + float variable = vars[0]; +#else float variable = square_sum / size; variable = 1.f / std::sqrt(variable + epsilon); +#endif if (gamma && beta) { for (int j = 0; j < size; ++j) { @@ -2506,8 +2540,8 @@ void MNNPackTranspose(float* dst, const float* src, size_t area, size_t depth, i void MNNExp(float* dst, const float* src, float* offset, size_t dataSize) { int countC8 = static_cast(dataSize) / 8; int remain = static_cast(dataSize) % 8; - float parameters[] = { - (float)logf(2.0f), 1.0f / (float)logf(2.0f), 1.0f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f}; + static const float parameters[] = { + (float)logf(2.0f), 1.0f / (float)logf(2.0f), 0.25f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f}; if (countC8 > 0) { // Align to eight so asm is easier to write MNNExpC8(dst, src, offset, parameters, countC8); @@ -2526,10 +2560,12 @@ void MNNExp(float* dst, const float* src, float* offset, size_t dataSize) { int div2 = (div + 127) << 23; auto xReamin = x - div * param; float expBasic = *(float*)(&div2); - auto t = xReamin; + auto t = xReamin * 0.25f; auto expRemain = - ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + parameters[3]) * t + - parameters[2]; + ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t + + 1.0f; + expRemain = expRemain * expRemain; + expRemain = expRemain * expRemain; dest[i] = expBasic * expRemain + offset[1]; summer+= dest[i]; } @@ -3189,6 +3225,37 @@ void MNNFp8ToFp16(uint16_t* dst, const uint8_t* src, size_t size) { #endif // USE_NEON } +#ifdef MNN_LOW_MEMORY +static void generalIm2col(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int LP, int pack) { + // LP >= pack + int number = info[0]; + int eReal = info[1]; + int eDest = info[2]; + int offset = info[3]; + for (int n=0; nMNNSumByAxisLForMatmul_A = MNNSumByAxisLForMatmul_A; #ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM // Weight Dequant Gemm Kernels - gCoreFunction->MNNPackedMatMul_int4 = MNNPackedMatMul_int4; - gCoreFunction->MNNPackedMatMulRemain_int4 = MNNPackedMatMulRemain_int4; gCoreFunction->MNNPackedMatMul_int8 = MNNPackedMatMul_int8; gCoreFunction->MNNPackedMatMulRemain_int8 = MNNPackedMatMulRemain_int8; #endif @@ -3311,6 +3376,15 @@ void MNNCoreFunctionInit() { gCoreFunction->MNNQuantSum = MNNQuantSumFP32; // Dynamic Quan Bias gCoreFunction->MNNDynamicUpdateConvBiasScale = MNNDynamicUpdateConvBiasScale; + gCoreFunction->MNNGeneralIm2Col = generalIm2col; +#ifdef __aarch64__ + if (gCoreFunction->supportSDot) { + gCoreFunction->MNNGeneralIm2Col = MNNGeneralIm2col_Fp32Arm82; + } + if (gCoreFunction->supportI8mm) { + gCoreFunction->MNNGeneralIm2Col = MNNGeneralIm2col_Fp32Arm86; + } +#endif #endif MNNCoreInt8FunctionInit(); MNNFunctionInit(); diff --git a/source/backend/cpu/compute/CommonOptFunction.h b/source/backend/cpu/compute/CommonOptFunction.h index 8065fcfba..6febdb4cc 100644 --- a/source/backend/cpu/compute/CommonOptFunction.h +++ b/source/backend/cpu/compute/CommonOptFunction.h @@ -19,7 +19,12 @@ #include "backend/cpu/compute/Int8FunctionsOpt.h" extern "C" { - +#ifdef MNN_LOW_MEMORY +#ifdef __aarch64__ +void MNNGeneralIm2col_Fp32Arm82(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack); +void MNNGeneralIm2col_Fp32Arm86(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack); +#endif +#endif void MNNFp32ToFp8(uint8_t* dst, const float* src, size_t size); void MNNFp8ToFp32(float* dst, const uint8_t* src, size_t size); void MNNFp16ToFp8(uint8_t* dst, const uint16_t* src, size_t size); @@ -131,9 +136,9 @@ void MNNPackedMatMul_int4(float* C, const float* A, const float* B, const size_t void MNNPackedMatMulRemain_int4(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b); void MNNPackedMatMul_int8(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b); void MNNPackedMatMulRemain_int8(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b); -void MNNAbsMaxFP32(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack); +void MNNAbsMaxFP32_Pack4(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack); void MNNQuantScaleFP32(float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch); -void MNNDynamicQuantFP32(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack); +void MNNDynamicQuantFP32_Pack4(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack); void MNNQuantSumFP32(float* sum, const float* dequant_scale, size_t thread, size_t batch); void MNNDynamicUpdateConvBiasScale(float* newbias, float* newscale, float* oldbias, float* weightScale, float* inputScale, float* weightKernelSum, float* inputZero, size_t ocQuad, size_t scaleSize); @@ -181,6 +186,9 @@ struct SumByAxisParams { ssize_t SRC_UNIT; ssize_t blockNum; ssize_t oneScale; + ssize_t valid; + ssize_t kernelxy; + ssize_t LU; }; } @@ -209,11 +217,10 @@ struct CoreFunctions { void(*MNNGetSparseMatMulPackMode)(int* eP, int *lP, int* hP); void(*MNNPackC4ForMatMul_A)(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el); void(*MNNPackForMatMul_B)(float* dest, const float* source, size_t h, size_t l, bool transpose); + void(*MNNGeneralIm2Col)(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el, int32_t LP, int32_t pack); // parameters: e, l, h, CStride, AStride, BStride void(*MNNPackedMatMul)(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b); void(*MNNPackedMatMulRemain)(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b); - void(*MNNPackedMatMul_int4)(float* C, const float* A, const float* B, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) = nullptr; - void(*MNNPackedMatMulRemain_int4)(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b) = nullptr; void(*MNNAbsMax)(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack) = nullptr; void(*MNNQuantScale)(float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch) = nullptr; void(*MNNDynamicQuant)(const float* src, int8_t* dst, const float* scale, size_t src_depth_quad, size_t realSize, int pack) = nullptr; diff --git a/source/backend/cpu/compute/ConvInt8TiledExecutor.cpp b/source/backend/cpu/compute/ConvInt8TiledExecutor.cpp index 1867fa3fd..9005c344f 100644 --- a/source/backend/cpu/compute/ConvInt8TiledExecutor.cpp +++ b/source/backend/cpu/compute/ConvInt8TiledExecutor.cpp @@ -1,4 +1,3 @@ -// // ConvInt8TiledExecutor.cpp // MNN // @@ -43,58 +42,32 @@ ErrorCode ConvInt8TiledExecutor::onResize(const std::vector& inputs, co return NO_ERROR; } -void ConvInt8TiledExecutor::reorderWeight(Tensor* weight, const uint8_t* weightSrc, int SRC_UNIT, int UNIT, int ic, int oc, int kernelCount, int pack, int blockNum) { - auto weightDst = weight->host(); - memset(weightDst, 0, weight->size()); +void ConvInt8TiledExecutor::reorderWeight(Tensor* weight, const uint8_t* weightSrc, int SRC_UNIT, int UNIT, int ic, int oc, int kernelCount, int pack, int blockNum, int32_t initval) { + auto weightDst = weight->host(); + memset(weightDst, (int8_t)initval, weight->size()); int kernelCountUnit = weight->length(1); int blockL = kernelCountUnit / blockNum; int strideOutside = ROUND_UP(oc, UNIT) * SRC_UNIT * blockL; int strideInside = weight->stride(0) / blockNum; - if (SRC_UNIT > pack) { // shape = {blockNum, UP_DIV(oc, UNIT), UP_DIV(UP_DIV(ic, pack) * kernelCount, SRC_UNIT / pack) / blockNum, UNIT, SRC_UNIT}; - auto icDivU = UP_DIV(ic, pack); - for (int k = 0; k < kernelCount; ++k) { - const auto srcK = weightSrc + k; - for (int y = 0; y < ic; ++y) { - const int yOutSide = y / pack; - const int yInSide = y % pack; - const int yIndex = yOutSide + k * icDivU; - const int ySubOutSide = yIndex / (SRC_UNIT / pack); - const int ySubInSide = yIndex % (SRC_UNIT / pack); - - int blockId = ySubOutSide / blockL; - int blockInsideId = ySubOutSide % blockL; - - auto dstY = weightDst + blockId * strideOutside + blockInsideId * weight->stride(1) + ySubInSide * pack + yInSide; - const auto srcY = srcK + y * kernelCount; - for (int x = 0; x < oc; ++x) { - const int xOutSide = x / UNIT; - const int xInSide = x % UNIT; - const int dstIndex = xOutSide * strideInside + xInSide * SRC_UNIT; - const int srcIndex = x * kernelCount * ic; - dstY[dstIndex] = srcY[srcIndex]; - } - } - } - } else { // shape = {blockNum, UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount / blockNum, UNIT, SRC_UNIT}; - for (int k = 0; k < kernelCount; ++k) { - auto icDivU = UP_DIV(ic, SRC_UNIT); - const auto srcK = weightSrc + k; - for (int y = 0; y < ic; ++y) { - const int yOutSide = y / SRC_UNIT; - const int yInSide = y % SRC_UNIT; - - int blockId = (yOutSide + k * icDivU) / blockL; - int blockInsideId = (yOutSide + k * icDivU) % blockL; - - auto dstY = weightDst + blockId * strideOutside + blockInsideId * weight->stride(1) + yInSide; - const auto srcY = srcK + y * kernelCount; - for (int x = 0; x < oc; ++x) { - const int xOutSide = x / UNIT; - const int xInSide = x % UNIT; - const int dstIndex = xOutSide * strideInside + xInSide * SRC_UNIT; - const int srcIndex = x * kernelCount * ic; - dstY[dstIndex] = srcY[srcIndex]; - } + // shape = {blockNum, UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount / blockNum, UNIT, SRC_UNIT}; + for (int k = 0; k < kernelCount; ++k) { + auto icDivU = UP_DIV(ic, SRC_UNIT); + const auto srcK = weightSrc + k; + for (int y = 0; y < ic; ++y) { + const int yOutSide = y / SRC_UNIT; + const int yInSide = y % SRC_UNIT; + + int blockId = (yOutSide + k * icDivU) / blockL; + int blockInsideId = (yOutSide + k * icDivU) % blockL; + + auto dstY = weightDst + blockId * strideOutside + blockInsideId * weight->stride(1) + yInSide; + const auto srcY = srcK + y * kernelCount; + for (int x = 0; x < oc; ++x) { + const int xOutSide = x / UNIT; + const int xInSide = x % UNIT; + const int dstIndex = xOutSide * strideInside + xInSide * SRC_UNIT; + const int srcIndex = x * kernelCount * ic; + dstY[dstIndex] = srcY[srcIndex]; } } } @@ -102,7 +75,7 @@ void ConvInt8TiledExecutor::reorderWeight(Tensor* weight, const uint8_t* weightS static bool _reorderWeightInside(Backend* bn, const Convolution2DCommon* common, const std::shared_ptr& weightOrigin, - std::shared_ptr& weight, int blockNum, bool alloc) { + std::shared_ptr& weight, int blockNum, bool alloc, int32_t initval = 0) { MNN_ASSERT(blockNum > 0); auto core = static_cast(bn)->int8Functions(); auto gcore = static_cast(bn)->functions(); @@ -112,12 +85,8 @@ static bool _reorderWeightInside(Backend* bn, const Convolution2DCommon* common, int oc = common->outputCount(), ic = common->inputCount(), kernelCount = common->kernelX() * common->kernelY(); std::vector shape; int pack = gcore->pack; - if (SRC_UNIT > pack) { - MNN_ASSERT(SRC_UNIT % pack == 0); - shape = {UP_DIV(oc, UNIT), UP_DIV(UP_DIV(ic, pack) * kernelCount, SRC_UNIT / pack), UNIT, SRC_UNIT}; - } else { - shape = {UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount, UNIT, SRC_UNIT}; - } + + shape = {UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount, UNIT, SRC_UNIT}; bool useCachedMmap = bn->getRuntime()->hint().useCachedMmap > 1; if (alloc) { weight.reset(Tensor::createDevice(shape)); @@ -133,7 +102,7 @@ static bool _reorderWeightInside(Backend* bn, const Convolution2DCommon* common, if (useCachedMmap) { return true; } - ConvInt8TiledExecutor::reorderWeight(weight.get(), weightOrigin->host(), SRC_UNIT, UNIT, ic, oc, kernelCount, pack, blockNum); + ConvInt8TiledExecutor::reorderWeight(weight.get(), weightOrigin->host(), SRC_UNIT, UNIT, ic, oc, kernelCount, pack, blockNum, initval); return true; } @@ -141,7 +110,9 @@ static void GetResourceInt8(std::shared_ptr resour // common parameters int outputCount = conv2d->common()->outputCount(); auto core = static_cast(backend)->functions(); - int LSize = conv2d->common()->inputCount() * conv2d->common()->kernelX() * conv2d->common()->kernelY(); + int inputChannel = conv2d->common()->inputCount(); + int kernelSize = conv2d->common()->kernelX() * conv2d->common()->kernelY(); + int LSize = inputChannel * kernelSize; int ocUp4 = ROUND_UP(outputCount, core->pack); bool useCachedMmap = backend->getRuntime()->hint().useCachedMmap > 1; @@ -266,6 +237,7 @@ DenseConvInt8TiledExecutor::DenseConvInt8TiledExecutor(Backend* backend, const O auto gcore = static_cast(backend)->functions(); mResourceInt8.reset(new CPUConvolution::ResourceInt8); mResourceInt8->mDynamicQuant = true; + mResourceInt8->mWeightAsymmetricQuant = quanCommon->asymmetric; int blockNum = 1; GetResourceInt8(mResourceInt8, quanCommon, convOp, backend, &blockNum); mBlockNum = blockNum; @@ -322,12 +294,8 @@ DenseConvInt8TiledExecutor::DenseConvInt8TiledExecutor(Backend* backend, const O // weight shape. std::vector shape; - if (SRC_UNIT > pack) { - MNN_ASSERT(SRC_UNIT % pack == 0); - shape = {UP_DIV(oc, UNIT), UP_DIV(UP_DIV(ic, pack) * kernelCount, SRC_UNIT / pack), UNIT * SRC_UNIT / 2}; - } else { - shape = {UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount, UNIT * SRC_UNIT / 2}; - } + shape = {UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount, UNIT * SRC_UNIT / 2}; + mResourceInt8->mWeightInt8.reset(Tensor::createDevice(shape)); auto res = backend->onAcquireBuffer(mResourceInt8->mWeightInt8.get(), Backend::STATIC); @@ -371,19 +339,26 @@ DenseConvInt8TiledExecutor::DenseConvInt8TiledExecutor(Backend* backend, const O } } else { // std::shared_ptr srcWeight; + int blocksize = ic * kernelCount / blockNum; + int originOffset = 0; if (quanCommon->canUseInt4) { + originOffset = -8; mResourceInt8->mWeightAsymmetricQuant = true; auto srcPtr = reinterpret_cast(quanCommon->weight.get()); - std::vector tmpWeight(weightLength * 2, 0); - for (int i = 0; i < weightLength; ++i) { - int8_t s0 = (srcPtr[i] >> 4) - 8; // For int4 quant weight, +8 saved in quant buffer - int8_t s1 = (srcPtr[i] & 0x0f) - 8; - tmpWeight[2 * i + 0] = s0; - tmpWeight[2 * i + 1] = s1; + std::vector tmpWeight(weightLength * 2, originOffset); + for (int j = 0; j < oc; ++j) { + for (int k = 0; k < blockNum; ++k) { + for (int i = 0; i < blocksize; ++i) { + int index = j * blockNum * blocksize + k * blocksize + i; + uint8_t w_ = srcPtr[index / 2]; + int truew = index % 2 ? (w_ & 0x0f) : (w_ >> 4); + tmpWeight[index] = truew - 8; + } + } } std::shared_ptr srcWeight(Tensor::create({weightLength * 2}, (void*)tmpWeight.data())); std::shared_ptr tmpWeightInt8; - mValid = _reorderWeightInside(backend, convOp->common(), srcWeight, tmpWeightInt8, blockNum, false); + mValid = _reorderWeightInside(backend, convOp->common(), srcWeight, tmpWeightInt8, blockNum, false, -8); if(!mValid) { return; } @@ -532,21 +507,37 @@ void DenseConvInt8TiledExecutor::getPackParameter(int* Unit, int* srcUnit, int* ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& inputs, const std::vector& outputs) { - mUseBatchQuan = (static_cast(backend())->getRuntime()->hint().dynamicQuantOption == 1); - mUseBatchQuan &= mCommon->kernelY() == 1 && mCommon->kernelX() == 1 - && outputs[0]->width() == inputs[0]->width() && outputs[0]->height() == inputs[0]->height() - && mCommon->strideX() == 1 && mCommon->strideY() == 1 && mCommon->padX() == 0 && mCommon->padY() == 0 - && outputs[0]->height() == 1 && outputs[0]->width() == 1; - mUseBatchQuan &= mResourceInt8->mDynamicQuant; - mUseBatchQuan &= (inputs[0]->batch() > 1); + // default option + mUseBatchQuan = false; + mQuantFirst = true; + auto option = static_cast(backend())->getRuntime()->hint().dynamicQuantOption; + int batch = inputs[0]->batch(); + int inC = inputs[0]->channel(); + auto output = outputs[0]; + int inputPlane = batch * inputs[0]->width() * inputs[0]->height(); + auto planeSize = output->width() * output->height() * output->batch(); auto core = static_cast(backend())->int8Functions(); auto gcore =static_cast(backend())->functions(); int UNIT, SRC_UNIT, DST_XUNIT; core->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT); - auto output = outputs[0]; + int kernelCount = mCommon->kernelY() * mCommon->kernelX(); + bool fastway = (kernelCount == 1) && (output->width() == inputs[0]->width()) && (output->height() == inputs[0]->height()) && (mCommon->strideX() * mCommon->strideY()) == 1; + if (inputPlane > 1) { + mUseBatchQuan = true; + } + if (!fastway) { // general conv + mQuantFirst = false; + if (planeSize > 1) { + mUseBatchQuan = true; + } + if (option == 1) { // lowest level. + mQuantFirst = true; + mUseBatchQuan = false; + } + } + float weightBytes = mResourceInt8->mActBits == 4 ? 0.5 : 1; - #ifdef MNN_KLEIDIAI_ENABLED KleidiAI& kai = KleidiAI::getInstance(); if(mResourceInt8->mDynamicQuant && mResourceInt8->mActBits == 4 && kai.canAccelerate()) { @@ -571,6 +562,8 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input CPUConvolution::onResize(inputs, outputs); ConvolutionTiledExecutor::setIm2ColParameter(mIm2ColParamter, mCommon, inputs[0], outputs[0], mPadX, mPadY, gcore, core); mBlockNum = 1; + mQuantFirst = true; + mUseBatchQuan = false; } else { // Dynamic Quant kernels CPUConvolution::onResize(inputs, outputs); // Gemm Kernel @@ -598,15 +591,18 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input } // input scale buffer - int batch = inputs[0]->batch(); - mInputDeqScales.reset(Tensor::createDevice({batch * 4})); - bool success = backend()->onAcquireBuffer(mInputDeqScales.get(), Backend::DYNAMIC); + const int threads = static_cast(backend())->threadNumber(); // Im2col info - const int threads = static_cast(backend())->threadNumber(); - auto planeSize = output->width() * output->height() * output->batch(); + int im2colBytes = 1; const int L2Size = 2048; - const int tileLimitByC = UP_DIV(L2Size, mIm2ColParamter.kernelCountUnit * SRC_UNIT); + int tileLimitByC = UP_DIV(L2Size, mIm2ColParamter.kernelCountUnit * SRC_UNIT); + + if (mQuantFirst == false) { + im2colBytes = gcore->bytes; + tileLimitByC = 1; + } + int ic = inputs[0]->channel(); int tileLimit = 0; int outC = output->channel(); int outC4 = UP_DIV(outC, gcore->pack); @@ -645,7 +641,6 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input part = UNIT / gcore->pack; } mThreadNums = ALIMIN(threads, threadNeed); - mSplitByOc = true; mDivides.resize(threads+1); mDivides[0] = 0; @@ -670,15 +665,23 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input workPT = mTileCount * DST_XUNIT * mIm2ColCount; } + mInputDeqScales.reset(Tensor::createDevice({inputPlane * 4})); + if (mQuantFirst == false && !mSplitByOc) { + mInputDeqScales.reset(Tensor::createDevice({threads, DST_XUNIT * 4})); + } + bool success = backend()->onAcquireBuffer(mInputDeqScales.get(), Backend::DYNAMIC); + auto bufferAlloc = static_cast(backend())->getBufferAllocator(); auto blitInfoSize = ConvolutionTiledExecutor::computeBlitInfoSize(workPT, mIm2ColParamter.ow, mIm2ColParamter.kernelX * mIm2ColParamter.kernelY, k); mBlitInfoStride = blitInfoSize.second; mBlitInfo = bufferAlloc->alloc(blitInfoSize.first); + int im2colBuffSize = DST_XUNIT * mIm2ColCount * icDiv4KernelCount * SRC_UNIT; + if (!mSplitByOc) { - mTempIm2ColBuffer.reset(Tensor::createDevice({threads, DST_XUNIT * mIm2ColCount * icDiv4KernelCount * SRC_UNIT})); + mTempIm2ColBuffer.reset(Tensor::createDevice({threads, im2colBuffSize * im2colBytes})); mTempSrcSum.resize(threads * mBlockNum * DST_XUNIT * mIm2ColCount * 4); // Use 4 bytes to save kernel sum. } else { - mTempIm2ColBuffer.reset(Tensor::createDevice({mTileCount, DST_XUNIT * mIm2ColCount * icDiv4KernelCount * SRC_UNIT})); + mTempIm2ColBuffer.reset(Tensor::createDevice({mTileCount, im2colBuffSize * im2colBytes})); mTempSrcSum.resize(mTileCount * mBlockNum * DST_XUNIT * mIm2ColCount * 4); // Use 4 bytes to save kernel sum. } @@ -694,25 +697,45 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input return NO_ERROR; } - - int inC = inputs[0]->channel(); // set im2col tensor info - mQuantInput.reset((Tensor::createDevice({batch, mIm2ColParamter.ih, mIm2ColParamter.iw, ROUND_UP(inC, gcore->pack)}))); + if (mQuantFirst) { + mQuantInput.reset((Tensor::createDevice({batch, mIm2ColParamter.ih, mIm2ColParamter.iw, ROUND_UP(inC, gcore->pack)}))); + } else if (!mSplitByOc){ + mQuantInput.reset((Tensor::createDevice({threads, im2colBuffSize * 1}))); + // mIm2ColParamter.ic = inC; + } else { + mQuantInput.reset((Tensor::createDevice({mTileCount, im2colBuffSize * 1}))); + } + success &= backend()->onAcquireBuffer(mQuantInput.get(), Backend::DYNAMIC); // set dynamic quant buffer - mTempMaxMinValueBuffer.reset(Tensor::createDevice({mThreadNums, 2 * gcore->bytes})); + // set compute buffer - mDynamicBias.reset(Tensor::createDevice({ocUp4 * 4})); - mScaleFuse.reset(Tensor::createDevice({alphaSize * 4})); + if (!mUseBatchQuan) { + mTempMaxMinValueBuffer.reset(Tensor::createDevice({threads, 2 * gcore->bytes})); + if (mQuantFirst) { + mDynamicBias.reset(Tensor::createDevice({ocUp4 * 4})); + mScaleFuse.reset(Tensor::createDevice({alphaSize * 4})); + } else { + mDynamicBias.reset(Tensor::createDevice({threads, ocUp4 * 4})); + mScaleFuse.reset(Tensor::createDevice({threads, alphaSize * 4})); + } + success &= backend()->onAcquireBuffer(mDynamicBias.get(), Backend::DYNAMIC); + success &= backend()->onAcquireBuffer(mTempMaxMinValueBuffer.get(), Backend::DYNAMIC); + success &= backend()->onAcquireBuffer(mScaleFuse.get(), Backend::DYNAMIC); + } - success &= backend()->onAcquireBuffer(mQuantInput.get(), Backend::DYNAMIC); - success &= backend()->onAcquireBuffer(mDynamicBias.get(), Backend::DYNAMIC); - success &= backend()->onAcquireBuffer(mTempMaxMinValueBuffer.get(), Backend::DYNAMIC); - success &= backend()->onAcquireBuffer(mScaleFuse.get(), Backend::DYNAMIC); + if (mUseBatchQuan) { int infobytes = 4; // use float32 to save dequant scale and quant scale. - int size = mThreadNums * batch * gcore->bytes + 2 * batch * infobytes; - mBatchQuantInfo.reset(Tensor::createDevice({size})); + if (mQuantFirst == false) { + int size = threads * DST_XUNIT * gcore->bytes + 2 * DST_XUNIT * infobytes; + mBatchQuantInfo.reset(Tensor::createDevice({threads, size})); + } else { + int size = mThreadNums * inputPlane * gcore->bytes + 2 * inputPlane * infobytes; + mBatchQuantInfo.reset(Tensor::createDevice({size})); + } + success &= backend()->onAcquireBuffer(mBatchQuantInfo.get(), Backend::DYNAMIC); } if (!success) { @@ -722,9 +745,12 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector& input backend()->onReleaseBuffer(mInputDeqScales.get(), Backend::DYNAMIC); backend()->onReleaseBuffer(mTempIm2ColBuffer.get(), Backend::DYNAMIC); backend()->onReleaseBuffer(mQuantInput.get(), Backend::DYNAMIC); - backend()->onReleaseBuffer(mDynamicBias.get(), Backend::DYNAMIC); - backend()->onReleaseBuffer(mTempMaxMinValueBuffer.get(), Backend::DYNAMIC); - backend()->onReleaseBuffer(mScaleFuse.get(), Backend::DYNAMIC); + + if (mUseBatchQuan == false) { + backend()->onReleaseBuffer(mDynamicBias.get(), Backend::DYNAMIC); + backend()->onReleaseBuffer(mScaleFuse.get(), Backend::DYNAMIC); + backend()->onReleaseBuffer(mTempMaxMinValueBuffer.get(), Backend::DYNAMIC); + } if (mUseBatchQuan) { backend()->onReleaseBuffer(mBatchQuantInfo.get(), Backend::DYNAMIC); } @@ -822,7 +848,6 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu int weight_step_Y = weightBytes * (UNIT__ * SRC_UNIT); int src_step_Y = DST_XUNIT * SRC_UNIT; - auto inputDataPtr = input->host(); auto im2colPtr = mTempIm2ColBuffer->host(); if (SRC_UNIT > PackUnit) { memset(im2colPtr, 0, mTempIm2ColBuffer->size()); @@ -843,39 +868,20 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu (reinterpret_cast(inputScalePtr))[0] = mMutableResource->mInputScale; } - auto SingleDynamicQuant = [&] () { - const auto floatptr = input->host(); - auto int8ptr = mQuantInput->host(); - auto inputsize = static_cast(backend())->getTensorSize(inputs[0]); + auto SingleDynamicQuant = [&] (uint8_t* floatPtr, int32_t& inputZero, uint8_t* inputDequantScale, uint8_t* matmulBiasPtr, uint8_t* matmulScalePtr, int inputsize, int threads, uint8_t* maxMinValPtr, int8_t* int8ptr) { float quantscale = 0.f; float dequantscale = 0.f; float zeropoint = 0; /* Count max and min value to compute input scale and zeropoint */ - auto maxMinValPtr = mTempMaxMinValueBuffer->host(); - int threadNeed = mThreadNums; - auto inputSizeCount = UP_DIV(inputsize, mThreadNums); - if (true) { - threadNeed = 1; - inputSizeCount = inputsize; - } else { - threadNeed = ALIMIN(UP_DIV(inputsize, inputSizeCount), mThreadNums); - inputSizeCount = UP_DIV(inputsize, threadNeed); - } - auto findMaxMinValueFunction = [&](int tId) { - auto perThreadWorkCount = ALIMIN(inputSizeCount, inputsize - tId * inputSizeCount); - auto minValPtrTid = reinterpret_cast(maxMinValPtr + tId * mTempMaxMinValueBuffer->stride(0)); - auto maxValPtrTid = reinterpret_cast(maxMinValPtr + tId * mTempMaxMinValueBuffer->stride(0) + gcore->bytes); - auto inputDataPtrTid = reinterpret_cast(reinterpret_cast(floatptr) + tId * inputSizeCount * gcore->bytes); - gcore->MNNCountMaxMinValue(inputDataPtrTid, minValPtrTid, maxValPtrTid, perThreadWorkCount); + auto findMaxMinValueFunction = [&]() { + auto perThreadWorkCount = inputsize; + auto minValPtrTid = reinterpret_cast(maxMinValPtr); + auto maxValPtrTid = reinterpret_cast(maxMinValPtr + gcore->bytes); + auto inputDataPtrTid = reinterpret_cast(floatPtr); + gcore->MNNCountMaxMinValue(inputDataPtrTid, minValPtrTid, maxValPtrTid, inputsize); }; - MNN_CONCURRENCY_BEGIN(tId, threadNeed) { - findMaxMinValueFunction((int)tId); - } - MNN_CONCURRENCY_END(); - if (threadNeed > 1) { - gcore->MNNCountMaxMinValue(reinterpret_cast(maxMinValPtr),reinterpret_cast(maxMinValPtr), reinterpret_cast(maxMinValPtr + gcore->bytes), 2 * mThreadNums); - } + findMaxMinValueFunction(); float maxVal = 0; float minVal = 0; if (gcore->bytes == 4) { @@ -890,7 +896,7 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu } /* Dynamic quant */ - if (mCommon->padX() > 0 || mCommon->padY() > 0) { // Ensure "0.0f" included in range. + if (mIm2ColParamter.padX > 0 || mIm2ColParamter.padY > 0) { // Ensure "0.0f" included in range. if (minVal > 0.f) { minVal = 0.f; } else if (maxVal < 0.f){ @@ -901,7 +907,7 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu } float range = maxVal - minVal; if (fabs(range) < 1e-7) { - zeropoint = maxVal; + zeropoint = (-1 * maxVal)-128; quantscale = 1.0f; dequantscale = 1.0f; } else { @@ -910,22 +916,7 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu zeropoint = roundf(-minVal * 255.f / range) - 128.0f; } auto sizeDiv = UP_DIV(inputsize, PackUnit); - - threadNeed = mThreadNums; - inputSizeCount = UP_DIV(sizeDiv, mThreadNums); - if (true) { - threadNeed = 1; - inputSizeCount = sizeDiv; - } else { - threadNeed = ALIMIN(UP_DIV(sizeDiv, inputSizeCount), mThreadNums); - inputSizeCount = UP_DIV(sizeDiv, threadNeed); - } - MNN_CONCURRENCY_BEGIN(tId, threadNeed) { - auto perThreadWorkCount = ALIMIN(inputSizeCount, sizeDiv - tId * inputSizeCount); - auto inptr_ = (float*)(((int8_t*)floatptr) + tId * inputSizeCount * PackUnit * gcore->bytes); - mQuantFunc(inptr_ , int8ptr + tId * inputSizeCount * PackUnit, perThreadWorkCount, &quantscale, -128, 127, &zeropoint, 0); - } - MNN_CONCURRENCY_END(); + mQuantFunc((float*)floatPtr , int8ptr, sizeDiv, &quantscale, -128, 127, &zeropoint, 0); /* bias float */ #ifdef MNN_USE_SSE @@ -937,83 +928,86 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu auto weightDequantScale = mResourceInt8->mOriginScale->host(); float zerofp32 = (zeropoint + offset) * dequantscale; - gcore->MNNDynamicUpdateConvBiasScale(mDynamicBias->host(), mScaleFuse->host(), biasfp32, weightDequantScale, &dequantscale, mResourceInt8->mWeightKernelSum->host(), &zerofp32, UP_DIV(output->channel(), 4), alphaSize); + gcore->MNNDynamicUpdateConvBiasScale((float*)matmulBiasPtr, (float*)matmulScalePtr, biasfp32, weightDequantScale, &dequantscale, mResourceInt8->mWeightKernelSum->host(), &zerofp32, UP_DIV(output->channel(), 4), alphaSize); // Move step for A and B for each block computing - inputZeroPoint = zeropoint; - (reinterpret_cast(inputScalePtr))[0] = dequantscale; - biasPtr = mDynamicBias->host(); - scalePtr = mScaleFuse->host(); - inputDataPtr = int8ptr; + inputZero = zeropoint; + (reinterpret_cast(inputDequantScale))[0] = dequantscale; + biasPtr = matmulBiasPtr; + scalePtr = matmulScalePtr; }; - auto BatchDynamicQuant = [&]() { + auto BatchDynamicQuant = [&](uint8_t* floatPtr, int32_t& inputZero, uint8_t* inputDequantScale, int LU, int EP, int LP, uint8_t* info, int32_t availableThreads, int8_t* dstInt8) { // Allocate input max/sum/dequant/quant buffer auto infobytes = 4; - auto dequantPtr = mBatchQuantInfo->host(); - auto quantPtr = dequantPtr + batch * infobytes; - auto maxPtr = mBatchQuantInfo->host() + 2 * batch * infobytes; - + auto quantPtr = info; + auto maxPtr = info + 2 * EP * infobytes; + // compute sum and absmax - int icDiv4 = UP_DIV(input->channel(), PackUnit); - int threadwork = UP_DIV(icDiv4, mThreadNums); - int threadNeed = UP_DIV(icDiv4, threadwork); - int threadTmp = ALIMIN(mThreadNums, threadNeed); - threadwork = UP_DIV(icDiv4, threadTmp); + int threadwork = UP_DIV(LU, availableThreads); + int threadNeed = UP_DIV(LU, threadwork); + int threadTmp = ALIMIN(availableThreads, threadNeed); + threadwork = UP_DIV(LU, threadTmp); + MNN_CONCURRENCY_BEGIN(tId, threadTmp) { int workCount = threadwork; if (tId == threadTmp - 1) { - workCount = icDiv4 - tId * threadwork; + workCount = LU - tId * threadwork; } int icIndex = tId * threadwork; - auto inputData = reinterpret_cast(input->host() + icIndex * batch * PackUnit * gcore->bytes); - auto batchMax = reinterpret_cast(maxPtr + tId * batch * gcore->bytes); - gcore->MNNAbsMax(inputData, batchMax, workCount, batch, PackUnit); + auto inputData = reinterpret_cast(floatPtr + icIndex * EP * LP * gcore->bytes); + auto batchMax = reinterpret_cast(maxPtr + tId * EP * gcore->bytes); + gcore->MNNAbsMax(inputData, batchMax, workCount, EP, LP); } MNN_CONCURRENCY_END(); // Compute quant scale - gcore->MNNQuantScale((float*)maxPtr, (float*)quantPtr, (float*)dequantPtr, threadTmp, batch); + gcore->MNNQuantScale((float*)maxPtr, (float*)quantPtr, (float*)inputDequantScale, threadTmp, EP); // quant MNN_CONCURRENCY_BEGIN(tId, threadTmp) { int workCount = threadwork; if (tId == threadTmp - 1) { - workCount = icDiv4 - tId * threadwork; + workCount = LU - tId * threadwork; } auto icIndex = tId * threadwork; - auto inputData = reinterpret_cast(input->host() + icIndex * batch * PackUnit * gcore->bytes); - auto int8ptr = mQuantInput->host() + icIndex * batch * PackUnit; + auto inputData = reinterpret_cast(floatPtr + icIndex * EP * LP * gcore->bytes); + auto int8ptr = dstInt8 + icIndex * EP * LP; auto scale_ptr = reinterpret_cast(quantPtr); - gcore->MNNDynamicQuant(inputData, int8ptr, scale_ptr, workCount, batch, PackUnit); + gcore->MNNDynamicQuant(inputData, int8ptr, scale_ptr, workCount, EP, LP); } MNN_CONCURRENCY_END(); - inputZeroPoint = 0; - inputScalePtr = (uint8_t*)dequantPtr; - inputDataPtr = mQuantInput->host(); - biasPtr = mResourceInt8->mOriginBias->host(); - scalePtr = mResourceInt8->mOriginScale->host(); + inputZero = 0; }; - ssize_t oneScale = 1; + + ssize_t oneScale = mUseBatchQuan ? 0 : 1; if (mUseBatchQuan) { - BatchDynamicQuant(); - oneScale = 0; - } else if (mResourceInt8->mDynamicQuant) { -// MNN::Timer _t; - SingleDynamicQuant(); -// auto time = (float)_t.durationInUs() / 1000.0f; -// printf("time = %f \n", time); - } else { - // offline quant. + biasPtr = mResourceInt8->mOriginBias->host(); + scalePtr = mResourceInt8->mOriginScale->host(); + } + int8_t* inputDataPtr = nullptr; // Matmul input. + auto im2colSrc = input->host(); // if not quant first, im2colSrc is original float input data. + auto inputsize = UP_DIV(input->channel(), PackUnit) * PackUnit * batch * input->height() * input->width(); + if (mQuantFirst) { // quant first, then im2col + if (mUseBatchQuan) { + BatchDynamicQuant(input->host(), inputZeroPoint, inputScalePtr, UP_DIV(input->channel(), PackUnit), batch, PackUnit, mBatchQuantInfo->host(), mThreadNums, mQuantInput->host()); + inputDataPtr = mQuantInput->host(); + } else if (mResourceInt8->mDynamicQuant) { + SingleDynamicQuant(input->host(), inputZeroPoint, inputScalePtr, mDynamicBias->host(), mScaleFuse->host(), inputsize, 1, mTempMaxMinValueBuffer->host(), mQuantInput->host()); + inputDataPtr = mQuantInput->host(); + } else { + // offline quant. + inputDataPtr = input->host(); + } + im2colSrc = (uint8_t*)inputDataPtr; } - if (mResourceInt8->mActBits == 4) { weightBytes = 0.5; weight_step_Y *= 0.5; } - + auto inputchannel = input->channel(); SumByAxisParams sumParams; sumParams.oneScale = oneScale; sumParams.SRC_UNIT = SRC_UNIT; @@ -1021,21 +1015,19 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu sumParams.DST_XUNIT = DST_XUNIT; sumParams.col_buffer_unit_size = col_buffer_unit_size; sumParams.kernelCountUnitDouble = kernelCountUnitDouble; + sumParams.valid = inputchannel % SRC_UNIT; + sumParams.kernelxy = mIm2ColParamter.kernelX * mIm2ColParamter.kernelY; + sumParams.LU = UP_DIV(inputchannel, SRC_UNIT); + + int im2colBytes = mQuantFirst == true ? 1 : gcore->bytes; - auto ThreadFunction = [&](int tId, int eStartIndex, int eEndIndex, int estep, int ocIndex) { + auto tileSplitFunction = [&](int tId, int eStartIndex, int eEndIndex, int estep) { auto ocDivThread = ocDiv4; - if (mSplitByOc) { // Thread split by OC - ocDivThread = ALIMIN(mDivides[tId + 1] - mDivides[tId], ocDiv4 - mDivides[tId]); - } float* reluPtr = mResourceInt8->mReluThreshold.data(); - uint8_t* extraScale = nullptr; // input scale for batch dynamic quant. QuanPostTreatParameters quanParam; quanParam.blockNum = mBlockNum; - if (mUseBatchQuan) { - extraScale = inputScalePtr; - } #ifdef MNN_USE_SSE - quanParam.extraBias = mResourceInt8->mWeightKernelSum->host() + ocIndex; + quanParam.extraBias = mResourceInt8->mWeightKernelSum->host(); #endif if (dstBytes != 1) { quanParam.useInt8 = 0; @@ -1048,71 +1040,94 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu quanParam.minValue = mMutableResource->mClampMin; } } - auto outputTid = outputDataPtr + ocIndex * plane * dstBytes; - const auto biasFloatTid = reinterpret_cast(biasPtr + ocIndex * 4); - const auto scaleFloatTid = reinterpret_cast(scalePtr + ocIndex * 4); - const auto weightDequanBiasTid = reinterpret_cast(weightDequantBias + ocIndex * 4); - const auto weightPtrTid = weightDataPtr + static_cast(ocIndex * blockL * SRC_UNIT * weightBytes); + auto biasFloatTid = reinterpret_cast(biasPtr); + auto scaleFloatTid = reinterpret_cast(scalePtr); + auto weightDequanBiasTid = reinterpret_cast(weightDequantBias); + auto weightPtrTid = weightDataPtr; if (mBlockNum == 1) { quanParam.biasFloat = biasFloatTid; quanParam.scale = scaleFloatTid; quanParam.weightQuanBias = weightDequanBiasTid; } - + // auto im2colPtr = mTempIm2ColBuffer->host(); auto colAddr = im2colPtr + tId * mTempIm2ColBuffer->stride(0); auto srcPtr = (int8_t const **)(mBlitInfo.ptr() + tId * mBlitInfoStride.first); auto el = (int32_t *)(srcPtr + mBlitInfoStride.second); auto xKernelSumPtrTid = reinterpret_cast(srcKernelSumPtr + tId * mBlockNum * DST_XUNIT * mIm2ColCount * 4); - int32_t info[6]; + int32_t info[5]; info[1] = mIm2ColParamter.iw * mIm2ColParamter.ih * batch; info[2] = static_cast(col_buffer_unit_size); info[3] = mIm2ColParamter.strideX; - info[5] = kernelCountUnitDouble; for (int tIndex = eStartIndex; tIndex < eEndIndex; tIndex += estep) { const int xIndexStart = tIndex * DST_XUNIT * mIm2ColCount; + auto outputInTilePtr = outputDataPtr + xIndexStart * PackUnit * dstBytes; int realDstCount = ALIMIN(plane - xIndexStart, DST_XUNIT * mIm2ColCount); - auto ptrExtraScale = extraScale != nullptr ? (extraScale + xIndexStart * 4) : nullptr; auto ptrInputscale = mUseBatchQuan == true ? (inputScalePtr + xIndexStart * 4) : inputScalePtr; + auto ptrExtraScale = mUseBatchQuan == true ? ptrInputscale : nullptr; // im2col - auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo((const float**)srcPtr, el, xIndexStart, realDstCount, mIm2ColParamter, (const uint8_t*)inputDataPtr, 1); + auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo((const float**)srcPtr, el, xIndexStart, realDstCount, mIm2ColParamter, (uint8_t*)im2colSrc, im2colBytes); int number = res.first; bool needZero = res.second; - if (needZero) { + if (needZero && mQuantFirst) { #ifdef MNN_USE_SSE ::memset(colAddr, inputZeroPoint + 128, col_buffer_size); #else ::memset(colAddr, inputZeroPoint, col_buffer_size); #endif + } else if (needZero) { + ::memset(colAddr, 0, mTempIm2ColBuffer->stride(0)); } info[0] = number; info[4] = realDstCount; - if (number > 0) { + int8_t* colAddrTemp = colAddr; + if (mQuantFirst && number > 0) { blitProc(colAddr, srcPtr, info, el); + colAddrTemp = colAddr; + } else if (number > 0) { + if (SRC_UNIT > PackUnit) { + memset(colAddr, 0, mTempIm2ColBuffer->stride(0)); + } + info[2] = realDstCount; + gcore->MNNGeneralIm2Col((float*)colAddr, (float const**)srcPtr, info, el, SRC_UNIT, PackUnit); // colAddr: [lu, realDstCount, lp] + } + if (!mQuantFirst) { + ptrInputscale = mInputDeqScales->host() + tId * mInputDeqScales->stride(0); + if (mUseBatchQuan) { + BatchDynamicQuant((uint8_t*)colAddr, inputZeroPoint, ptrInputscale, kernelCountUnitDouble, realDstCount, SRC_UNIT, mBatchQuantInfo->host() + tId * mBatchQuantInfo->stride(0), 1, mQuantInput->host() + tId * mQuantInput->stride(0)); + ptrExtraScale = ptrInputscale; + } else { + biasFloatTid = reinterpret_cast(mDynamicBias->host() + tId * mDynamicBias->stride(0)); + scaleFloatTid = reinterpret_cast(mScaleFuse->host() + tId * mScaleFuse->stride(0)); + auto maxMinPtr = mTempMaxMinValueBuffer->host() + tId * mTempMaxMinValueBuffer->stride(0); + SingleDynamicQuant((uint8_t*)colAddr, inputZeroPoint, ptrInputscale, (uint8_t*)biasFloatTid, (uint8_t*)scaleFloatTid, kernelCountUnitDouble*realDstCount*SRC_UNIT, 1, maxMinPtr, mQuantInput->host() + tId * mQuantInput->stride(0)); + if (mBlockNum == 1) { + quanParam.biasFloat = biasFloatTid; + quanParam.scale = scaleFloatTid; + } + } + colAddrTemp = mQuantInput->host() + tId * mQuantInput->stride(0); } if (mResourceInt8->mWeightAsymmetricQuant) { - mSumByAxisLFunc(xKernelSumPtrTid, colAddr, (float*)ptrInputscale, realDstCount, sumParams); + mSumByAxisLFunc(xKernelSumPtrTid, colAddrTemp, (float*)ptrInputscale, realDstCount, sumParams); } - auto outputInTilePtr = outputTid + xIndexStart * PackUnit * dstBytes; - auto colAddrTemp = colAddr; auto ptrX = xKernelSumPtrTid; if (mBlockNum == 1) { do { int step = ALIMIN(DST_XUNIT, realDstCount); quanParam.srcKernelSum = ptrX; - quanParam.extraScale = extraScale != nullptr ? (float*)ptrExtraScale : nullptr; - // printf("step=%d, ocDivThread=%d\n", step, ocDivThread); + quanParam.extraScale = mUseBatchQuan == true ? (float*)ptrExtraScale : nullptr; mGemmKernel(outputInTilePtr, colAddrTemp, weightPtrTid, kernelCountUnitDouble, dstZStep * dstBytes, ocDivThread, &quanParam, step); ptrX += step; realDstCount-=step; outputInTilePtr += DST_XUNIT * PackUnit * dstBytes; colAddrTemp += col_buffer_unit_size; - ptrExtraScale = extraScale != nullptr ? (ptrExtraScale + step * 4) : nullptr; + ptrExtraScale = mUseBatchQuan == true ? (ptrExtraScale + step * 4) : nullptr; } while(realDstCount > 0); } else { // Now offline quant do not run into. do { int step = ALIMIN(DST_XUNIT, realDstCount); - quanParam.extraScale = extraScale != nullptr ? (float*)ptrExtraScale : nullptr; + quanParam.extraScale = mUseBatchQuan == true ? (float*)ptrExtraScale : nullptr; for (int k = 0; k < mBlockNum; ++k) { quanParam.biasFloat = nullptr; quanParam.fp32minmax = nullptr; @@ -1132,48 +1147,91 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu realDstCount-=step; outputInTilePtr += DST_XUNIT * PackUnit * dstBytes; colAddrTemp += col_buffer_unit_size; - ptrExtraScale = extraScale != nullptr ? (ptrExtraScale + step * 4) : nullptr; + ptrExtraScale = mUseBatchQuan == true ? (ptrExtraScale + step * 4) : nullptr; } while(realDstCount > 0); } } }; - auto exeFunction = [&](int threads) { // Thread split by OC - auto colAddr = im2colPtr; + auto ocSplitFunction = [&](int threads) { // Thread split by OC + auto colAddr = mTempIm2ColBuffer->host(); auto srcPtr = (int8_t const **)(mBlitInfo.ptr()); auto el = (int32_t *)(srcPtr + mBlitInfoStride.second); auto xKernelSumPtrTid = reinterpret_cast(srcKernelSumPtr); - int32_t info[6]; + int32_t info[5]; info[1] = mIm2ColParamter.iw * mIm2ColParamter.ih * batch; info[2] = static_cast(col_buffer_unit_size); info[3] = mIm2ColParamter.strideX; - info[5] = kernelCountUnitDouble; float* reluPtr = mResourceInt8->mReluThreshold.data(); - // im2col - auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo((const float**)srcPtr, el, 0, plane, mIm2ColParamter, (const uint8_t*)inputDataPtr, 1); - int number = res.first; - bool needZero = res.second; - if (needZero) { + int8_t* matmulInput; + if (mQuantFirst) { // im2col + auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo((const float**)srcPtr, el, 0, plane, mIm2ColParamter, (uint8_t*)im2colSrc, im2colBytes); + int number = res.first; + bool needZero = res.second; + if (needZero) { #ifdef MNN_USE_SSE - ::memset(colAddr, inputZeroPoint + 128, mTempIm2ColBuffer->size()); + ::memset(colAddr, inputZeroPoint + 128, mTempIm2ColBuffer->size()); #else - ::memset(colAddr, inputZeroPoint, mTempIm2ColBuffer->size()); + ::memset(colAddr, inputZeroPoint, mTempIm2ColBuffer->size()); #endif + } + info[0] = number; + info[4] = plane; + if (number > 0) { + blitProc(colAddr, srcPtr, info, el); + } + matmulInput = colAddr; } - info[0] = number; - info[4] = plane; - if (number > 0) { - blitProc(colAddr, srcPtr, info, el); + if (false == mQuantFirst) { + int realDstCount = plane; + int start = 0; + auto im2colDst = colAddr; + auto ptrInputscale = mInputDeqScales->host(); + auto int8Ptr = mQuantInput->host(); + int sizePacked = 0; + if (SRC_UNIT > PackUnit) { + memset(colAddr, 0, mTempIm2ColBuffer->size()); + } + while (realDstCount > 0) { + int work = std::min(realDstCount, DST_XUNIT); + sizePacked += (work * SRC_UNIT * kernelCountUnitDouble); + auto res = ConvolutionTiledExecutor::turnIm2ColToBlitInfo((const float**)srcPtr, el, start, work, mIm2ColParamter, (uint8_t*)im2colSrc, im2colBytes); + int number = res.first; + bool needZero = res.second; + if (needZero) { + ::memset(im2colDst, 0, col_buffer_unit_size * gcore->bytes); + } + info[0] = number; + info[2] = work; + if (number > 0) { // im2col + gcore->MNNGeneralIm2Col((float*)im2colDst, (float const**)srcPtr, info, el, SRC_UNIT, PackUnit); // colAddr: [lu, realDstCount, lp] + } + // batch quant + if (mUseBatchQuan) { + BatchDynamicQuant((uint8_t*)im2colDst, inputZeroPoint, ptrInputscale, kernelCountUnitDouble, work, SRC_UNIT, mBatchQuantInfo->host(), 1, int8Ptr); + ptrInputscale += (work * sizeof(int32_t)); + int8Ptr += col_buffer_unit_size; + } + realDstCount -= work; + start += work; + im2colDst += (col_buffer_unit_size * gcore->bytes); + } + if (!mUseBatchQuan) { + SingleDynamicQuant((uint8_t*)colAddr, inputZeroPoint, ptrInputscale, mDynamicBias->host(), mScaleFuse->host(), sizePacked, 1, mTempMaxMinValueBuffer->host(), mQuantInput->host()); + } + matmulInput = mQuantInput->host(); } + if (mResourceInt8->mWeightAsymmetricQuant) { - mSumByAxisLFunc(xKernelSumPtrTid, colAddr, (float*)inputScalePtr, plane, sumParams); + mSumByAxisLFunc(xKernelSumPtrTid, matmulInput, (float*)inputScalePtr, plane, sumParams); } MNN_CONCURRENCY_BEGIN(tId, threads) { int ocIndex = PackUnit * mDivides[tId]; auto ocDivThread = ALIMIN(mDivides[tId + 1] - mDivides[tId], ocDiv4 - mDivides[tId]); if (ocIndex < ocUp4) { + auto colAddrTemp = matmulInput; QuanPostTreatParameters quanParam; quanParam.blockNum = mBlockNum; uint8_t* extraScale = nullptr; // input scale for batch dynamic quant. @@ -1207,14 +1265,12 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu int realDstCount = plane; auto ptrExtraScale = extraScale != nullptr ? (extraScale) : nullptr; - auto colAddrTemp = colAddr; auto ptrX = xKernelSumPtrTid; if (mBlockNum == 1) { do { int step = ALIMIN(DST_XUNIT, realDstCount); quanParam.srcKernelSum = ptrX; quanParam.extraScale = extraScale != nullptr ? (float*)ptrExtraScale : nullptr; - // printf("step=%d, ocDivThread=%d\n", step, ocDivThread); mGemmKernel(outputInTilePtr, colAddrTemp, weightPtrTid, kernelCountUnitDouble, dstZStep * dstBytes, ocDivThread, &quanParam, step); ptrX += step; realDstCount-=step; @@ -1256,12 +1312,13 @@ ErrorCode DenseConvInt8TiledExecutor::onExecute(const std::vector& inpu const int threads = static_cast(backend())->threadNumber(); if (!mSplitByOc) { MNN_CONCURRENCY_BEGIN(tId, threads) { - ThreadFunction((int)tId, mDivides[tId], mDivides[tId + 1], 1, 0); - + if (mDivides[tId + 1] - mDivides[tId] > 0) { + tileSplitFunction((int)tId, mDivides[tId], mDivides[tId + 1], 1); + } } MNN_CONCURRENCY_END(); } else { - exeFunction(threads); + ocSplitFunction(threads); } return NO_ERROR; } diff --git a/source/backend/cpu/compute/ConvInt8TiledExecutor.hpp b/source/backend/cpu/compute/ConvInt8TiledExecutor.hpp index 6c46b9161..21f87bf70 100644 --- a/source/backend/cpu/compute/ConvInt8TiledExecutor.hpp +++ b/source/backend/cpu/compute/ConvInt8TiledExecutor.hpp @@ -24,7 +24,7 @@ class ConvInt8TiledExecutor : public CPUConvolution { virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override; virtual void getPackParameter(int* Unit, int* SrcUnit, int* DestUnit, const CoreInt8Functions* core) = 0; - static void reorderWeight(Tensor* weight, const uint8_t* weightSrc, int SRC_UNIT, int UNIT, int ic, int oc, int kernelCount, int pack, int blockNum = 1); + static void reorderWeight(Tensor* weight, const uint8_t* weightSrc, int SRC_UNIT, int UNIT, int ic, int oc, int kernelCount, int pack, int blockNum = 1, int32_t initval = 0); protected: ConvolutionCommon::Im2ColParameter mIm2ColParamter; @@ -36,6 +36,7 @@ class ConvInt8TiledExecutor : public CPUConvolution { MemChunk mBlitInfo; std::pair mBlitInfoStride; int mIm2ColCount; + bool mQuantFirst; }; // diff --git a/source/backend/cpu/compute/Convolution1x1Strassen.cpp b/source/backend/cpu/compute/Convolution1x1Strassen.cpp index f149532fe..72debfb6b 100644 --- a/source/backend/cpu/compute/Convolution1x1Strassen.cpp +++ b/source/backend/cpu/compute/Convolution1x1Strassen.cpp @@ -19,7 +19,7 @@ namespace MNN { Convolution1x1Strassen::Convolution1x1Strassen(const Convolution2DCommon *common, Backend *b, const float *originWeight, - size_t originWeightSize, const float *bias, size_t biasSize, std::shared_ptr quantInfo) + size_t originWeightSize, const float *bias, size_t biasSize) : CPUConvolution(common, b) { auto outputCount = (int)biasSize; int ePack, lPack, hPack; @@ -33,25 +33,6 @@ Convolution1x1Strassen::Convolution1x1Strassen(const Convolution2DCommon *common mValid = false; return; } -#ifdef MNN_LOW_MEMORY - if ((originWeightSize == 0 || nullptr == originWeight) && nullptr != quantInfo.get()) { // Use Int8 Weight. - originWeightSize = quantInfo->weight.size(); - int lSize = (int)originWeightSize / (int)biasSize * common->kernelX() * common->kernelY(); - auto hU = UP_DIV(outputCount, hPack); - auto lU = UP_DIV(lSize, lPack); - mSrcCount = (int)originWeightSize / outputCount; - - mResource->mWeight.reset(Tensor::createDevice(std::vector{UP_DIV(outputCount, hPack), UP_DIV(mSrcCount, lPack) * lPack, hPack})); - mValid = b->onAcquireBuffer(mResource->mWeight.get(), Backend::STATIC); - if (!mValid) { - MNN_ERROR("Not Enough Memory\n"); - return; - } - - DenseConvolutionTiledExecutor::initQuantizeResource(quantInfo, mResource, hU, hPack, lU, lPack, outputCount, (int)originWeightSize / (int)biasSize, common->kernelX() * common->kernelY(), core->bytes); - return; - } -#endif // Use Float Weight. mResource->mWeight.reset(Tensor::createDevice(std::vector{UP_DIV(outputCount, hPack), UP_DIV(mSrcCount, lPack) * lPack, hPack})); mValid = b->onAcquireBuffer(mResource->mWeight.get(), Backend::STATIC); @@ -128,14 +109,7 @@ ErrorCode Convolution1x1Strassen::onResize(const std::vector &inputs, uint8_t* dequantAlpha = nullptr; uint8_t* dequantBias = nullptr; int dequantBits = bytes * 8; // fp16:16, fp32:32 -#ifdef MNN_LOW_MEMORY - if (mResource && mResource->mDequantize.bits <= 8) { - dequantAlpha = mResource->mDequantize.mScaleBias->host(); - dequantBias = dequantAlpha + mResource->hU * mResource->hP * bytes; - dequantBits = mResource->mDequantize.bits; - } -#endif - mWeightBytes = static_cast(dequantBits) / 8.0f; + mWeightBytes = bytes; if (matrixSizeE > CONVOLUTION_TILED_NUMBER * 8 * numberThread && matrixSizeE > ocC4) { std::vector divides(numberThread+1); divides[0] = 0; @@ -154,7 +128,7 @@ ErrorCode Convolution1x1Strassen::onResize(const std::vector &inputs, unit.offset[2] = 0; unit.offset[0] = core->pack * planeStart * bytes; unit.offset[3] = core->pack * planeStart * bytes; - unit.mStracssenComputor.reset(new StrassenMatrixComputor(backend(), false, maxDepth, dequantAlpha, dequantBias, dequantBits)); + unit.mStracssenComputor.reset(new StrassenMatrixComputor(backend(), false, maxDepth)); int e = planeSize; int l = ic; int h = oc; @@ -200,7 +174,7 @@ ErrorCode Convolution1x1Strassen::onResize(const std::vector &inputs, unit.offset[0] = 0; unit.offset[3] = core->pack * matrixSizeE * ocStart * bytes; - unit.mStracssenComputor.reset(new StrassenMatrixComputor(backend(), false, maxDepth, dequantAlpha, dequantBias, dequantBits)); + unit.mStracssenComputor.reset(new StrassenMatrixComputor(backend(), false, maxDepth)); int e = matrixSizeE; int l = ic; int h = std::min(ocSize * core->pack, ocWeightSize * hPack); diff --git a/source/backend/cpu/compute/Convolution1x1Strassen.hpp b/source/backend/cpu/compute/Convolution1x1Strassen.hpp index 728f9988c..cda597c55 100644 --- a/source/backend/cpu/compute/Convolution1x1Strassen.hpp +++ b/source/backend/cpu/compute/Convolution1x1Strassen.hpp @@ -15,8 +15,7 @@ namespace MNN { class Convolution1x1Strassen : public CPUConvolution { public: - Convolution1x1Strassen(const Convolution2DCommon *common, Backend *b, const float *originWeight, - size_t originWeightSize, const float *bias, size_t biasSize, std::shared_ptr); + Convolution1x1Strassen(const Convolution2DCommon *common, Backend *b, const float *originWeight, size_t originWeightSize, const float *bias, size_t biasSize); Convolution1x1Strassen(std::shared_ptr resource, const Convolution2DCommon *common, Backend* b); virtual ~Convolution1x1Strassen(); @@ -34,7 +33,7 @@ class Convolution1x1Strassen : public CPUConvolution { }; std::vector mUnits; - float mWeightBytes = 4; + int mWeightBytes = 4; }; } // namespace MNN diff --git a/source/backend/cpu/compute/ConvolutionFloatFactory.cpp b/source/backend/cpu/compute/ConvolutionFloatFactory.cpp index d8c6e6cd9..18aa7a33a 100644 --- a/source/backend/cpu/compute/ConvolutionFloatFactory.cpp +++ b/source/backend/cpu/compute/ConvolutionFloatFactory.cpp @@ -62,10 +62,7 @@ static Execution* _createUnit(const Tensor* input, const Tensor* output, Backend } #endif if (fastWay && cpuBackend->functions()->matmulBytes == 0) { - return new Convolution1x1Strassen(common, backend, originWeight, originWeightSize, bias, biasSize, weightQuantInfo); - } - if (originWeightSize == 0) { - return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, weightQuantInfo); + return new Convolution1x1Strassen(common, backend, originWeight, originWeightSize, bias, biasSize); } if (cpuBackend->getRuntime()->hint().winogradMemoryUsed == 0 || (!ConvolutionWinogradBridge::canUseWinograd(common))) { return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, nullptr); diff --git a/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp b/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp index 5b3adc2e1..53352b88c 100644 --- a/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp +++ b/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp @@ -117,19 +117,20 @@ void ConvolutionTiledExecutor:: setIm2ColParameter(ConvolutionCommon::Im2ColPara dstIm2ColParamter.srcYStep = input->stride(2) * pack; dstIm2ColParamter.packCUnit = pack; dstIm2ColParamter.ic = input->channel(); + dstIm2ColParamter.icup4 = input->channel(); // for float im2col. if (nullptr != int8Core) { // Compute Int8 Info and align ic int UNIT, SRC_UNIT, DynamicDestUnit; auto core = int8Core; core->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DynamicDestUnit); - if (SRC_UNIT > pack) { - const auto srcCountUnit = UP_DIV(input->channel(), pack); - dstIm2ColParamter.kernelCountUnit = UP_DIV(srcCountUnit * kernelCount, SRC_UNIT / pack); - dstIm2ColParamter.ic = dstIm2ColParamter.icDiv4 * pack; + const auto srcCountUnit = UP_DIV(input->channel(), SRC_UNIT); + dstIm2ColParamter.kernelCountUnit = srcCountUnit * kernelCount; + dstIm2ColParamter.ic = srcCountUnit * SRC_UNIT; + + if (SRC_UNIT > pack) { // Carefully change it. + dstIm2ColParamter.icup4 = ROUND_UP(input->channel(), pack); } else { - const auto srcCountUnit = UP_DIV(input->channel(), SRC_UNIT); - dstIm2ColParamter.kernelCountUnit = srcCountUnit * kernelCount; - dstIm2ColParamter.ic = srcCountUnit * SRC_UNIT; + dstIm2ColParamter.icup4 = ROUND_UP(input->channel(), SRC_UNIT); } } if (dstIm2ColParamter.iw == 1 && dstIm2ColParamter.ow == 1 && dstIm2ColParamter.oh > 1 && dstIm2ColParamter.kernelX == 1 && dstIm2ColParamter.padX == 0) { @@ -188,7 +189,7 @@ std::pair ConvolutionTiledExecutor::turnIm2ColToBlitInfo(float const auto srcKx = srcKy + ((oxBegin + sta) * p.strideX + p.dilateX * kx - p.padX) * bytes * unit; srcPtr[number] = (const float*)srcKx; el[4 * number + 0] = end - sta; - el[4 * number + 1] = p.ic; + el[4 * number + 1] = p.icup4; el[4 * number + 2] = eStart + sta; el[4 * number + 3] = lOffset; number++; diff --git a/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp b/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp index fea897d71..70ce13c5b 100644 --- a/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp +++ b/source/backend/cpu/compute/DenseConvolutionTiledExecutor.cpp @@ -50,50 +50,24 @@ bool DenseConvolutionTiledExecutor::initQuantizeResource(std::shared_ptrweight.get(); + std::vector blob; if (int8Info->canUseInt4) { - MNN_ASSERT(weightLength % 2 == 0); - weightLength = UP_DIV(weightLength, 2); - resource->mDequantize.bits = 4; - resource->mWeight.reset(Tensor::createDevice(std::vector{weightLength})); - auto res = resource->backend->onAcquireBuffer(resource->mWeight.get(), Backend::STATIC); - if (!res) { - return false; - } - auto dstWInt4 = resource->mWeight->host(); - auto srcWInt4 = int8Info->weight.get(); - if (kernelSize == 1 && srcChannel % 2 == 0 && hU * hP == outputCount) { - for (int i = 0; i < hU; i++) { - for (int j = 0; j < srcChannel/2; j++) { - for (int k = 0; k < hP/2; k++) { - uint8_t s0 = srcWInt4[((i * hP + (k * 2 + 0)) * srcChannel) / 2 + j]; - uint8_t s1 = srcWInt4[((i * hP + (k * 2 + 1)) * srcChannel) / 2 + j]; - uint8_t d0 = (s0 & 0xf0) | (s1 >> 4); - uint8_t d1 = (s0 << 4) | (s1 & 0x0f); - dstWInt4[(i * srcChannel + (j * 2 + 0)) * hP / 2 + k] = d0; - dstWInt4[(i * srcChannel + (j * 2 + 1)) * hP / 2 + k] = d1; - } - } - } - } else { - // [oc, ic, ks] -> [oc/hP, ks, ic, hP] - ::memset(dstWInt4, 0, resource->mWeight->usize()); - for (int y = 0; y < outputCount; ++y) { - int yo = y / hP; - int yi = y % hP; - for (int iz = 0; iz < srcChannel; ++iz) { - for (int k=0; k < kernelSize; ++k) { - int sx = y * srcChannel * kernelSize + iz * kernelSize + k; - int dx = yo * lP * hP * lU + (iz + k * srcChannel) * hP + yi; - uint8_t s = srcWInt4[sx/2]; - s = (sx % 2) ? (s & 0xf) : (s >> 4); - s = (dx % 2) ? s : (s << 4); - dstWInt4[dx/2] |= s; - } - } - } + // Revert int4 to int8 + auto size = int8Info->weight.size(); + blob.resize(int8Info->weight.size() * 2); + auto idxBuf = (uint8_t*)srcWInt8; + for (int i=0; imWeight.reset(Tensor::createDevice(std::vector{hU, lU * lP, hP})); auto res = resource->backend->onAcquireBuffer(resource->mWeight.get(), Backend::STATIC); if (!res) { @@ -101,7 +75,6 @@ bool DenseConvolutionTiledExecutor::initQuantizeResource(std::shared_ptrmWeight->host(); - auto srcWInt8 = int8Info->weight.get(); ::memset(dstWInt8, 0, resource->mWeight->usize()); for (int y=0; yMNNPackedMatMulRemain_int8; *weightBytes = 1; } - if (weightQuantBits == 4) { - *matmulUnit = core->MNNPackedMatMul_int4; - *matmulRemain = core->MNNPackedMatMulRemain_int4; - *weightBytes = 0.5; - } } DenseConvolutionTiledExecutor::DenseConvolutionTiledExecutor(const Convolution2DCommon* common, Backend* b, @@ -485,6 +453,7 @@ ErrorCode DenseConvolutionTiledImpl::onResize(const std::vector& inputs size_t weightStride = 0; #ifdef MNN_LOW_MEMORY if (mResource && mResource->mDequantize.bits <= 8) { + MNN_ASSERT(mResource->mDequantize.bits == 8); DenseConvolutionTiledExecutor::selectLowMemoryMatmulFunc(&matmulUnit, &matmulRemain, &weightBytes, mResource->mDequantize.bits, core); int scaleSize = mResource->mDequantize.mScaleBias->size() / (2 * bytes); blockNum = scaleSize / (mResource->hU * mResource->hP); @@ -492,10 +461,6 @@ ErrorCode DenseConvolutionTiledImpl::onResize(const std::vector& inputs dequantAlpha = mResource->mDequantize.mScaleBias->host(); dequantBias = dequantAlpha + scaleSize * bytes; weightStride = (L - blockSize) * hP; - if (mResource->mDequantize.bits == 4) { - halfStride = 0.5; - weightStride = static_cast(weightStride * halfStride); - } } #endif auto kernel_width = mCommon->kernelX(); diff --git a/source/backend/cpu/compute/IdstConvolutionInt8.cpp b/source/backend/cpu/compute/IdstConvolutionInt8.cpp index 8046ecd15..61530bb3f 100644 --- a/source/backend/cpu/compute/IdstConvolutionInt8.cpp +++ b/source/backend/cpu/compute/IdstConvolutionInt8.cpp @@ -65,12 +65,8 @@ IdstConvolutionInt8::IdstConvolutionInt8(const Convolution2DCommon* convOp, Back auto kernelCount = kx * ky; auto srcCount = mSrcCount; std::vector shape; - if (SRC_UNIT > PackUnit) { - MNN_ASSERT(SRC_UNIT % UNIT == 0); - shape = {UP_DIV(outputCount, UNIT), UP_DIV(UP_DIV(srcCount, PackUnit) * kernelCount, SRC_UNIT / PackUnit), UNIT, SRC_UNIT}; - } else { - shape = {UP_DIV(outputCount, UNIT), UP_DIV(srcCount, SRC_UNIT) * kernelCount, UNIT, SRC_UNIT}; - } + shape = {UP_DIV(outputCount, UNIT), UP_DIV(srcCount, SRC_UNIT) * kernelCount, UNIT, SRC_UNIT}; + mWeight.reset(Tensor::createDevice(shape)); mFakeBias.reset(Tensor::createDevice({(int)ROUND_UP(biasSize, PackUnit)})); mFakeWeightBias.reset(Tensor::createDevice({(int)ROUND_UP(biasSize, PackUnit)})); diff --git a/source/backend/cpu/compute/ResizeFunction.h b/source/backend/cpu/compute/ResizeFunction.h index 7935c6738..69e3ecd57 100644 --- a/source/backend/cpu/compute/ResizeFunction.h +++ b/source/backend/cpu/compute/ResizeFunction.h @@ -11,9 +11,6 @@ #include #include -#ifdef __linux__ - #include -#endif #include "core/Macro.h" #ifdef __cplusplus extern "C" { diff --git a/source/backend/cpu/compute/StrassenMatmulComputor.cpp b/source/backend/cpu/compute/StrassenMatmulComputor.cpp index 094efbd5a..ae6b3a5b2 100644 --- a/source/backend/cpu/compute/StrassenMatmulComputor.cpp +++ b/source/backend/cpu/compute/StrassenMatmulComputor.cpp @@ -42,17 +42,11 @@ class AutoMemory { BufferAllocator* mAllocator; }; -StrassenMatrixComputor::StrassenMatrixComputor(Backend* bn, bool multithread, int maxDepth, uint8_t* dequantAlpha, uint8_t* dequantBias, int32_t dequantBits) : mBackend(bn) { +StrassenMatrixComputor::StrassenMatrixComputor(Backend* bn, bool multithread, int maxDepth) : mBackend(bn) { mMaxDepth = maxDepth; mSupportMultiThread = multithread; - mDequantBias = dequantBias; - mDequantAlpha = dequantAlpha; - mDequantBits = dequantBits; auto core = static_cast(backend())->functions(); mWeightBytes = core->bytes; - if (mDequantBits == 8 || mDequantBits == 4) { - mWeightBytes = (float)mDequantBits / 8; - } }; StrassenMatrixComputor::~StrassenMatrixComputor() { // Do nothing @@ -81,18 +75,8 @@ ErrorCode StrassenMatrixComputor::_generateTrivalMatMul(int e, int l, int h, con auto eReal = aStride / core->bytes / core->pack; auto matmulUnit = core->MNNPackedMatMul; auto matmulRemain = core->MNNPackedMatMulRemain; - const float* dequantAlpha = nullptr; - const float* dequantBias = nullptr; - float weightBytes = 1; -#ifdef MNN_LOW_MEMORY - if (nullptr != mDequantAlpha && nullptr != mDequantBias) { - dequantAlpha = reinterpret_cast(mDequantAlpha); - dequantBias = reinterpret_cast(mDequantBias); - DenseConvolutionTiledExecutor::selectLowMemoryMatmulFunc(&matmulUnit, &matmulRemain, &weightBytes, mDequantBits, core); - } -#endif mFunctions.emplace_back( - std::make_pair([cStride, l, h, xCount, AT, BT, CT, COT, tileBufferBasic, unitNumber, bExtraStride, numberThread, eReal, eP, active, matmulUnit, matmulRemain, dequantAlpha, dequantBias, this](int tId) { + std::make_pair([cStride, l, h, xCount, AT, BT, CT, COT, tileBufferBasic, unitNumber, bExtraStride, numberThread, eReal, eP, active, matmulUnit, matmulRemain, this](int tId) { auto core = static_cast(backend())->functions(); size_t parameters[7]; parameters[0] = xCount * core->bytes; @@ -129,7 +113,7 @@ ErrorCode StrassenMatrixComputor::_generateTrivalMatMul(int e, int l, int h, con int xStart = i * eP; auto aStart = aHost + xStart * packUnit; core->MNNPackC4ForMatMul_A((float*)(tileHost), (const float**)(&aStart), info, stride); - matmulUnit((float*)(cHost + xStart * packUnit), (float*)tileHost, (float*)bHost, parameters, postParametersPtr, (const float*)biasPtr, dequantAlpha, dequantBias); + matmulUnit((float*)(cHost + xStart * packUnit), (float*)tileHost, (float*)bHost, parameters, postParametersPtr, (const float*)biasPtr, nullptr, nullptr); } if (tId != numberThread -1) { return; @@ -143,7 +127,7 @@ ErrorCode StrassenMatrixComputor::_generateTrivalMatMul(int e, int l, int h, con auto aStart = aHost + xStart * packUnit; // Copy core->MNNPackC4ForMatMul_A((float*)(tileHost), (const float**)(&aStart), info, stride); - matmulRemain((float*)(cHost + xStart * packUnit), (float*)tileHost, (float*)bHost, xCount, parameters, postParametersPtr, (const float*)biasPtr, dequantAlpha, dequantBias); + matmulRemain((float*)(cHost + xStart * packUnit), (float*)tileHost, (float*)bHost, xCount, parameters, postParametersPtr, (const float*)biasPtr, nullptr, nullptr); } }, numberThread)); static_cast(backend())->getBufferAllocator()->free(tileBufferBasic); @@ -502,9 +486,6 @@ void StrassenMatrixComputor::onReset() { ErrorCode StrassenMatrixComputor::onEncode(const std::vector& inputs, const std::vector& outputs, const std::vector& postParameters, int inputL, int inputH) { auto core = static_cast(backend())->functions(); mWeightBytes = core->bytes; - if (mDequantBits == 8 || mDequantBits == 4) { - mWeightBytes = (float)mDequantBits / 8; - } MNN_ASSERT(inputs.size() == 2 || inputs.size() == 3); MNN_ASSERT(outputs.size() == 1); auto A = inputs[0]; diff --git a/source/backend/cpu/compute/StrassenMatmulComputor.hpp b/source/backend/cpu/compute/StrassenMatmulComputor.hpp index 5e24fd6bb..ac22c8699 100644 --- a/source/backend/cpu/compute/StrassenMatmulComputor.hpp +++ b/source/backend/cpu/compute/StrassenMatmulComputor.hpp @@ -21,7 +21,7 @@ namespace MNN { */ class StrassenMatrixComputor { public: - StrassenMatrixComputor(Backend* bn, bool multithread, int maxDepth, uint8_t* dequantAlpha=nullptr, uint8_t* dequantBias=nullptr, int32_t dequantBits=32); + StrassenMatrixComputor(Backend* bn, bool multithread, int maxDepth); virtual ~StrassenMatrixComputor(); /* @@ -83,10 +83,7 @@ class StrassenMatrixComputor { std::vector mStack; - uint8_t* mDequantAlpha = nullptr; - uint8_t* mDequantBias = nullptr; - int32_t mDequantBits; - float mWeightBytes = 4; + int mWeightBytes = 4; }; } // namespace MNN diff --git a/source/backend/cpu/x86_x64/AVX2Functions.cpp b/source/backend/cpu/x86_x64/AVX2Functions.cpp index ad105f6ba..75b73053f 100644 --- a/source/backend/cpu/x86_x64/AVX2Functions.cpp +++ b/source/backend/cpu/x86_x64/AVX2Functions.cpp @@ -46,8 +46,6 @@ bool AVX2Functions::init(int cpuFlags) { coreFunction->MNNPackedMatMul = _AVX_MNNPackedMatMul; coreFunction->MNNPackedMatMulRemain = _AVX_MNNPackedMatMulRemain; #ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM - coreFunction->MNNPackedMatMul_int4 = _AVX_MNNPackedMatMul_int4; - coreFunction->MNNPackedMatMulRemain_int4 = _AVX_MNNPackedMatMulRemain_int4; coreFunction->MNNPackedMatMul_int8 = _AVX_MNNPackedMatMul_int8; coreFunction->MNNPackedMatMulRemain_int8 = _AVX_MNNPackedMatMulRemain_int8; #endif diff --git a/source/backend/cpu/x86_x64/FunctionDispatcher.cpp b/source/backend/cpu/x86_x64/FunctionDispatcher.cpp index fc82e6971..328488554 100644 --- a/source/backend/cpu/x86_x64/FunctionDispatcher.cpp +++ b/source/backend/cpu/x86_x64/FunctionDispatcher.cpp @@ -51,8 +51,6 @@ void MNNFunctionInit() { coreFunction->MNNPackedMatMul = _SSE_MNNPackedMatMul; coreFunction->MNNPackedMatMulRemain = _SSE_MNNPackedMatMulRemain; #ifdef MNN_CPU_WEIGHT_DEQUANT_GEMM - coreFunction->MNNPackedMatMul_int4 = _SSE_MNNPackedMatMul_int4; - coreFunction->MNNPackedMatMulRemain_int4 = _SSE_MNNPackedMatMulRemain_int4; coreFunction->MNNPackedMatMul_int8 = _SSE_MNNPackedMatMul_int8; coreFunction->MNNPackedMatMulRemain_int8 = _SSE_MNNPackedMatMulRemain_int8; #endif diff --git a/source/backend/cpu/x86_x64/avx/GemmInt8.cpp b/source/backend/cpu/x86_x64/avx/GemmInt8.cpp index 5a1d26af1..40a975fa0 100644 --- a/source/backend/cpu/x86_x64/avx/GemmInt8.cpp +++ b/source/backend/cpu/x86_x64/avx/GemmInt8.cpp @@ -136,7 +136,7 @@ void _AVX_MNNGemmInt8AddBiasScale_16x4_w4(int8_t* dst, const int8_t* src, const const auto weight_sz = weight_dz + sz * weight_step_Y; const auto src_z = src_x + sz * GEMMINT8_AVX2_L * realDst; LOAD_INT4_TO_INT8; - auto w0 = _mm256_set_m128i(w_1, w_0); + auto w0 = _mm256_insertf128_si256(_mm256_castsi128_si256(w_0), w_1, 1); auto s0 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 0)); auto s1 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 1)); auto s2 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 2)); @@ -243,7 +243,7 @@ void _AVX_MNNGemmInt8AddBiasScale_16x4_w4(int8_t* dst, const int8_t* src, const const auto src_z = src_x + sz * GEMMINT8_AVX2_L * realDst; LOAD_INT4_TO_INT8; - auto w0 = _mm256_set_m128i(w_1, w_0); + auto w0 = _mm256_insertf128_si256(_mm256_castsi128_si256(w_0), w_1, 1); auto s0 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 0)); auto s1 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 1)); auto s2 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 2)); @@ -335,7 +335,7 @@ void _AVX_MNNGemmInt8AddBiasScale_16x4_w4(int8_t* dst, const int8_t* src, const const auto weight_sz = weight_dz + sz * weight_step_Y; const auto src_z = src_x + sz * GEMMINT8_AVX2_L * realDst; LOAD_INT4_TO_INT8; - auto w0 = _mm256_set_m128i(w_1, w_0); + auto w0 = _mm256_insertf128_si256(_mm256_castsi128_si256(w_0), w_1, 1); auto s0 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 0)); auto s1 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 1)); @@ -409,7 +409,7 @@ void _AVX_MNNGemmInt8AddBiasScale_16x4_w4(int8_t* dst, const int8_t* src, const const auto weight_sz = weight_dz + sz * weight_step_Y; const auto src_z = src_x + sz * GEMMINT8_AVX2_L * realDst; LOAD_INT4_TO_INT8; - auto w0 = _mm256_set_m128i(w_1, w_0); + auto w0 = _mm256_insertf128_si256(_mm256_castsi128_si256(w_0), w_1, 1); auto s0 = _mm256_castps_si256(_mm256_broadcast_ss((float*)src_z + 0)); D00 = _mm256_add_epi32(D00, _mm256_madd_epi16(_mm256_maddubs_epi16(s0, w0), oneValue)); diff --git a/source/backend/cpu/x86_x64/avx/MathFunctions.cpp b/source/backend/cpu/x86_x64/avx/MathFunctions.cpp index aef76a84c..feb2d6f31 100644 --- a/source/backend/cpu/x86_x64/avx/MathFunctions.cpp +++ b/source/backend/cpu/x86_x64/avx/MathFunctions.cpp @@ -66,8 +66,8 @@ void _AVX_MNNExpC8(float* dest, const float* source, float* offset, const float* auto C = _mm256_broadcast_ss(offset + 2); auto p0 = _mm256_set1_ps(parameters[0]); auto p1 = _mm256_set1_ps(parameters[1]); - auto p2 = _mm256_set1_ps(parameters[2]); - auto p3 = _mm256_set1_ps(parameters[3]); + auto p2 = _mm256_set1_ps(0.25f); + auto p3 = _mm256_set1_ps(1.0f); auto p4 = _mm256_set1_ps(parameters[4]); auto p5 = _mm256_set1_ps(parameters[5]); auto p6 = _mm256_set1_ps(parameters[6]); @@ -90,7 +90,7 @@ void _AVX_MNNExpC8(float* dest, const float* source, float* offset, const float* div2 = _mm256_mullo_epi32(div2, basic); auto expBasic = _mm256_castsi256_ps(div2); auto xReamin = _mm256_sub_ps(x, _mm256_mul_ps(div, p0)); - auto t = xReamin; + auto t = _mm256_mul_ps(xReamin, p2); auto c0 = _mm256_mul_ps(p7, t); auto c1 = _mm256_add_ps(c0, p6); auto c2 = _mm256_mul_ps(c1, t); @@ -100,8 +100,9 @@ void _AVX_MNNExpC8(float* dest, const float* source, float* offset, const float* auto c6 = _mm256_mul_ps(c5, t); auto c7 = _mm256_add_ps(c6, p3); auto c8 = _mm256_mul_ps(c7, t); - auto c9 = _mm256_add_ps(c8, p2); - auto expRemain = c9; + auto c9 = _mm256_add_ps(c8, p3); + auto expRemain = _mm256_mul_ps(c9, c9); + expRemain = _mm256_mul_ps(expRemain, expRemain); auto res = _mm256_add_ps(_mm256_mul_ps(expBasic, expRemain), B); _mm256_storeu_ps(dest + 8 * i, res); summer = _mm256_add_ps(summer, res); diff --git a/source/backend/cpu/x86_x64/avxfma/MathFunctions.cpp b/source/backend/cpu/x86_x64/avxfma/MathFunctions.cpp index e999d2633..26fe278ea 100644 --- a/source/backend/cpu/x86_x64/avxfma/MathFunctions.cpp +++ b/source/backend/cpu/x86_x64/avxfma/MathFunctions.cpp @@ -62,8 +62,8 @@ void _AVX_MNNExpC8FMA(float* dest, const float* source, float* offset, const flo auto C = _mm256_broadcast_ss(offset + 2); auto p0 = _mm256_set1_ps(parameters[0]); auto p1 = _mm256_set1_ps(parameters[1]); - auto p2 = _mm256_set1_ps(parameters[2]); - auto p3 = _mm256_set1_ps(parameters[3]); + auto p2 = _mm256_set1_ps(0.25f); + auto p3 = _mm256_set1_ps(1.0f); auto p4 = _mm256_set1_ps(parameters[4]); auto p5 = _mm256_set1_ps(parameters[5]); auto p6 = _mm256_set1_ps(parameters[6]); @@ -86,13 +86,14 @@ void _AVX_MNNExpC8FMA(float* dest, const float* source, float* offset, const flo div2 = _mm256_mullo_epi32(div2, basic); auto expBasic = _mm256_castsi256_ps(div2); auto xReamin = _mm256_sub_ps(x, _mm256_mul_ps(div, p0)); - auto t = xReamin; + auto t = _mm256_mul_ps(xReamin, p2); auto c1 = _mm256_fmadd_ps(p7, t, p6); auto c3 = _mm256_fmadd_ps(c1, t, p5); auto c5 = _mm256_fmadd_ps(c3, t, p4); auto c7 = _mm256_fmadd_ps(c5, t, p3); - auto c9 = _mm256_fmadd_ps(c7, t, p2); - auto expRemain = c9; + auto c9 = _mm256_fmadd_ps(c7, t, p3); + auto expRemain = _mm256_mul_ps(c9, c9); + expRemain = _mm256_mul_ps(expRemain, expRemain); auto res = _mm256_fmadd_ps(expBasic, expRemain, B); summer = _mm256_add_ps(summer, res); _mm256_storeu_ps(dest + 8 * i, res); diff --git a/source/backend/cpu/x86_x64/sse/MathFunctions.cpp b/source/backend/cpu/x86_x64/sse/MathFunctions.cpp index 8238cb187..061d70005 100644 --- a/source/backend/cpu/x86_x64/sse/MathFunctions.cpp +++ b/source/backend/cpu/x86_x64/sse/MathFunctions.cpp @@ -20,8 +20,8 @@ void _SSE_MNNExpC8(float* dest, const float* source, float* offset, const float* auto C = _mm_set1_ps(offset[2]); auto p0 = _mm_set1_ps(parameters[0]); auto p1 = _mm_set1_ps(parameters[1]); - auto p2 = _mm_set1_ps(parameters[2]); - auto p3 = _mm_set1_ps(parameters[3]); + auto p2 = _mm_set1_ps(0.25f); + auto p3 = _mm_set1_ps(1.0f); auto p4 = _mm_set1_ps(parameters[4]); auto p5 = _mm_set1_ps(parameters[5]); auto p6 = _mm_set1_ps(parameters[6]); @@ -43,7 +43,7 @@ void _SSE_MNNExpC8(float* dest, const float* source, float* offset, const float* div2 = _mm_slli_epi32(div2, 23); auto expBasic = _mm_castsi128_ps(div2); auto xReamin = _mm_sub_ps(x, _mm_mul_ps(div, p0)); - auto t = xReamin; + auto t = _mm_mul_ps(xReamin, p2); auto c0 = _mm_mul_ps(p7, t); auto c1 = _mm_add_ps(c0, p6); auto c2 = _mm_mul_ps(c1, t); @@ -53,8 +53,9 @@ void _SSE_MNNExpC8(float* dest, const float* source, float* offset, const float* auto c6 = _mm_mul_ps(c5, t); auto c7 = _mm_add_ps(c6, p3); auto c8 = _mm_mul_ps(c7, t); - auto c9 = _mm_add_ps(c8, p2); - auto expRemain = c9; + auto c9 = _mm_add_ps(c8, p3); + auto expRemain = _mm_mul_ps(c9, c9); + expRemain = _mm_mul_ps(expRemain, expRemain); auto res = _mm_add_ps(_mm_mul_ps(expBasic, expRemain), B); _mm_storeu_ps(dest + 4 * i, res); summer = _mm_add_ps(summer, res); diff --git a/source/backend/metal/AllShader.cpp b/source/backend/metal/AllShader.cpp index ba24d1472..418992522 100644 --- a/source/backend/metal/AllShader.cpp +++ b/source/backend/metal/AllShader.cpp @@ -302,7 +302,7 @@ const char* shader_MetalConvolution_metal = " wt4=z_wt[z*cst.kernel_size+y*cst.kernel_x+x];\n" " result0 += FLOAT4(in4_0*wt4);\n" " }\n" -" in4_0=z_in[z*cst.input_size+y*cst.input_width+cst.kernel_x];\n" +" in4_0=z_in[z*cst.input_size*cst.batch+y*cst.input_width+cst.kernel_x];\n" " result1 += FLOAT4(in4_0*wt4);\n" " }\n" " }\n" @@ -407,16 +407,16 @@ const char* shader_MetalConvolution_metal = " auto x_wt=z_wt+y*cst.kernel_x+x;\n" " auto in4=z_in[ y*dilation_h+x*cst.dilation_x];\n" " /* true */ result0 += FLOAT4(in4**x_wt);\n" -" if (valids[0]) { x_wt += ws; result1 += FLOAT4(in4**x_wt); }\n" -" if (valids[1]) { x_wt += ws; result2 += FLOAT4(in4**x_wt); }\n" -" if (valids[2]) { x_wt += ws; result3 += FLOAT4(in4**x_wt); }\n" +" if (valids.x) { x_wt += ws; result1 += FLOAT4(in4**x_wt); }\n" +" if (valids.y) { x_wt += ws; result2 += FLOAT4(in4**x_wt); }\n" +" if (valids.z) { x_wt += ws; result3 += FLOAT4(in4**x_wt); }\n" " }\n" " }\n" " }\n" " /* true */ *z_out=activate(M4(result0+FLOAT4(biasTerms[uz[0]])),cst.activation);\n" -" if (valids[0]) { z_out += cst.output_size; *z_out=activate(M4(result1+FLOAT4(biasTerms[uz[1]])),cst.activation); }\n" -" if (valids[1]) { z_out += cst.output_size; *z_out=activate(M4(result2+FLOAT4(biasTerms[uz[2]])),cst.activation); }\n" -" if (valids[2]) { z_out += cst.output_size; *z_out=activate(M4(result3+FLOAT4(biasTerms[uz[3]])),cst.activation); }\n" +" if (valids.x) { z_out += cst.output_size*cst.batch; *z_out=activate(M4(result1+FLOAT4(biasTerms[uz[1]])),cst.activation); }\n" +" if (valids.y) { z_out += cst.output_size*cst.batch; *z_out=activate(M4(result2+FLOAT4(biasTerms[uz[2]])),cst.activation); }\n" +" if (valids.z) { z_out += cst.output_size*cst.batch; *z_out=activate(M4(result3+FLOAT4(biasTerms[uz[3]])),cst.activation); }\n" "}\n" "kernel void conv_z2(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" @@ -553,38 +553,6 @@ const char* shader_MetalSoftmax_metal = "static inline float4 softmax_filter(float4 V,int z,int limit) {\n" " return select(0,V,z*4+int4(0,1,2,3)= s.inside_size || (int)gid.y >= s.outside_size) return;\n" -" \n" -" auto axis_off=gid.y*s.axis_length*s.inside_size+gid.x;\n" -" auto axis_in=in+axis_off;\n" -" auto axis_out=out+axis_off;\n" -" \n" -" // get max\n" -" float max1=-INFINITY;\n" -" for (int i=tiisg; i= cst.inside || (int)gid.y >= cst.outside) {\n" -" return;\n" -" }\n" -" auto in_data=in+gid.y*cst.inside;\n" -" auto out_data=out+gid.y*cst.inside;\n" -" float mean;\n" -" float sum=0.0f;\n" -" float square_sum=0.0f;\n" -" \n" -" for(int i=tiisg; i= cst.inside/4 || (int)gid.y >= cst.outside) {\n" -" return;\n" -" }\n" -" auto in_data=in+gid.y*cst.inside/4;\n" -" auto out_data=out+gid.y*cst.inside/4;\n" -" float mean;\n" -" float sum=0.0f;\n" -" float square_sum=0.0f;\n" -" \n" -" for(int i=tiisg; i= cst.inside || (int)gid.y >= cst.outside) {\n" -" return;\n" -" }\n" -" auto in_data=in+gid.y*cst.inside;\n" -" auto out_data=out+gid.y*cst.inside;\n" -" float square_sum=0.0f;\n" -" \n" -" for(int i=tiisg; i= cst.inside/4 || (int)gid.y >= cst.outside) {\n" -" return;\n" -" }\n" -" int in_idx=gid.x;\n" -" int out_idx=gid.y;\n" -" auto in_data=in+out_idx*cst.inside/4;\n" -" auto out_data=out+out_idx*cst.inside/4;\n" -" float4 square_sum=0.0f;\n" -" float square_sum_all=0.0f;\n" -" for(int i=tiisg; i= s.size || (int)gid.y >= s.steps*s.batch) return;\n" " int z=gid.y/s.batch;\n" +" int offset=s.offset;\n" +" float4 scale=scalesbias[z];\n" +" float4 bias=scalesbias[z+offset];\n" " out[int(gid.y)*s.size+int(gid.x)] =\n" -" in [int(gid.y)*s.size+int(gid.x)]*M4(scales[z])+M4(biasTerms[z]);\n" +" (M4)((float4)in[int(gid.y)*s.size+int(gid.x)]*scale+bias);\n" "}\n" ; const char* shader_MetalDeconvolution_metal = @@ -1638,24 +1400,6 @@ const char* shader_MetalROIPooling_metal = const char* shader_MetalConvolution1x1_metal = "#define CONV_UNROLL (4)\n" "#define CONV_UNROLL_L (8)\n" -"#define INIT_SIMDGROUP_MATRIX(a,b,d) " -" simdgroup_T8x8 sga[a];" -" simdgroup_T8x8 sgb[b];" -" simdgroup_T8x8 sgd[d];" -" for (int i=0; i(0.f);" -" }\n" -"#define SIMDGROUP_MATRIX_FMA(a,b) " -" for(int j=0; j3) {xy_out[3]=activate(M4(result3),cst.activation); }\n" "}\n" -"kernel void conv1x1_z4_sg(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device M4x4 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" uint3 gid[[threadgroup_position_in_grid]],\n" -" uint tiisg[[thread_index_in_simdgroup]],\n" -" uint sgitg[[simdgroup_index_in_threadgroup]]) {\n" -" if ((int)gid.x >= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return;\n" -" \n" -" int rx=gid.x;\n" -" int uz=gid.y;\n" -" auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in+(int)gid.z*cst.input_size+rx+0;\n" -" auto xy_out=out+(int)gid.z*cst.output_size+uz*cst.output_size*cst.batch+rx;\n" -" auto biasValue=FLOAT4(biasTerms[uz]);\n" -" FLOAT4 result0=0;\n" -" int computeSize=min(cst.output_size-rx,CONV_UNROLL);\n" -" for (auto z=tiisg; z3) {xy_out[3]=activate(M4(result3),cst.activation); }\n" "}\n" -"kernel void conv1x1_gemm_16x16_w4_sg(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device uchar2 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" const device M4 *dequantScale [[buffer(5)]],\n" -" uint3 gid [[threadgroup_position_in_grid]],\n" -" uint tiitg[[thread_index_in_threadgroup]],\n" -" uint sgitg[[simdgroup_index_in_threadgroup]]) {\n" -" /*\n" -" // Read:\n" -" M 0~127 ---> input: [M16,K8]\n" -" M 128~255 ---> input: [K8,N16]\n" -" // Write:\n" -" M 0~255 ---> input: [N2,M2,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[256]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(2,2,4);\n" -" \n" -" int rx=gid.x;// M/16\n" -" int uz=gid.y;// N/16\n" -" \n" -" int kl=tiitg/16;\n" -" int rcl=tiitg % 16;\n" -"// int kl=tiitg % 2;\n" -"// int rcl=tiitg/2;\n" -" \n" -" // boundary limit\n" -" int idx_n4=(4*uz+rcl/4)> 4)-8,(float)(w_int40[0] & 15)-8,(float)(w_int40[1] >> 4)-8,(float)(w_int40[1] & 15)-8);\n" -" \n" -" FLOAT4 res=w40*scale[rcl % 4]+dequant_bias[rcl % 4];\n" -" ((threadgroup FLOAT*)sdata)[256*i+128+(kl*4+0)*16+rcl]=res[0];\n" -" ((threadgroup FLOAT*)sdata)[256*i+128+(kl*4+1)*16+rcl]=res[1];\n" -" ((threadgroup FLOAT*)sdata)[256*i+128+(kl*4+2)*16+rcl]=res[2];\n" -" ((threadgroup FLOAT*)sdata)[256*i+128+(kl*4+3)*16+rcl]=res[3];\n" -" }\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" #pragma unroll(4)\n" -" for(int i=0; i<4; i++) {\n" -" simdgroup_load(sga[0],(const threadgroup FLOAT*)sdata+256*i,8);\n" -" simdgroup_load(sga[1],((const threadgroup FLOAT*)sdata)+64+256*i,8);\n" -" simdgroup_barrier(mem_flags::mem_none);\n" -" \n" -" simdgroup_load(sgb[0],((const threadgroup FLOAT*)sdata)+128+256*i,16);\n" -" simdgroup_load(sgb[1],((const threadgroup FLOAT*)sdata)+136+256*i,16);\n" -" \n" -" SIMDGROUP_MATRIX_FMA(2,2);\n" -" }\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" }\n" -" }\n" -" \n" -" SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata,4);\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" if((16*rx+rcl) input: [M32,K8]\n" -" M 256~383 ---> input: [K8,N16]\n" -" // Write:\n" -" M 0~511 ---> input: [N2,M4,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[128]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(4,2,8);\n" -" \n" -" int rx=gid.x;// M/32\n" -" int uz=gid.y;// N/16\n" -" \n" -" int kl=tiitg % 2;\n" -" int rcl=tiitg/2;\n" -" \n" -" const int size_m=cst.input_size*cst.batch;\n" -" \n" -" // boundary limit\n" -" int idx_n4=(4*uz+rcl/4)> 4)-8,(float)(w_int4[0] & 15)-8,(float)(w_int4[1] >> 4)-8,(float)(w_int4[1] & 15)-8);\n" -" FLOAT4 res=w4*scale[rcl % 4]+dequant_bias[rcl % 4];\n" -" // sdata[32+2* rcl+kl]=res;\n" -" ((threadgroup FLOAT*)sdata)[256+(kl*4+0)*16+rcl]=res[0];\n" -" ((threadgroup FLOAT*)sdata)[256+(kl*4+1)*16+rcl]=res[1];\n" -" ((threadgroup FLOAT*)sdata)[256+(kl*4+2)*16+rcl]=res[2];\n" -" ((threadgroup FLOAT*)sdata)[256+(kl*4+3)*16+rcl]=res[3];\n" -" \n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" simdgroup_load(sga[0],(const threadgroup FLOAT*)sdata,8);\n" -" simdgroup_load(sga[1],((const threadgroup FLOAT*)sdata)+64,8);\n" -" simdgroup_load(sga[2],((const threadgroup FLOAT*)sdata)+128,8);\n" -" simdgroup_load(sga[3],((const threadgroup FLOAT*)sdata)+192,8);\n" -" \n" -" // simdgroup_load(sgb[0],(const threadgroup FLOAT*)sdata+128,8,0,true);\n" -" // simdgroup_load(sgb[1],((const threadgroup FLOAT*)sdata)+192,8,0,true);\n" -" simdgroup_load(sgb[0],((const threadgroup FLOAT*)sdata)+256,16);\n" -" simdgroup_load(sgb[1],((const threadgroup FLOAT*)sdata)+264,16);\n" -" \n" -" SIMDGROUP_MATRIX_FMA(4,2);\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" xy_in0 += 2*cst.input_size*cst.batch;\n" -" xy_in1 += 2*cst.input_size*cst.batch;\n" -" }\n" -" }\n" -" \n" -" SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata,8);\n" -" \n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" if((16*rx+rcl) input: [M16,K8]\n" -" M 128~383 ---> input: [K8,N32]\n" -" // Write:\n" -" M 0~511 ---> input: [N2,N2,M2,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[128]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(2,4,8);\n" -" \n" -" int rx=gid.x;// M/16\n" -" int uz=gid.y;// N/32\n" -" \n" -" int kl=tiitg % 2;\n" -" int rcl=tiitg/2;\n" -" \n" -" // boundary limit\n" -" int idx_n40=(4*uz+rcl/4)> 4)-8,(float)(w_int4[0] & 15)-8,(float)(w_int4[1] >> 4)-8,(float)(w_int4[1] & 15)-8);\n" -" FLOAT4 res=w4*scale0[rcl % 4]+dequant_bias0[rcl % 4];\n" -" // sdata[32+2* rcl+kl]=res;\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+0)*32+rcl]=res[0];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+1)*32+rcl]=res[1];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+2)*32+rcl]=res[2];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+3)*32+rcl]=res[3];\n" -" }\n" -" {\n" -" uchar2 w_int4=xy_wt1[4*z]; // [N/4,K/4,N4,K4]\n" -" FLOAT4 w4=FLOAT4((float)(w_int4[0] >> 4)-8,(float)(w_int4[0] & 15)-8,(float)(w_int4[1] >> 4)-8,(float)(w_int4[1] & 15)-8);\n" -" FLOAT4 res=w4*scale1[rcl % 4]+dequant_bias1[rcl % 4];\n" -" // sdata[32+2* rcl+kl]=res;\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+0)*32+16+rcl]=res[0];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+1)*32+16+rcl]=res[1];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+2)*32+16+rcl]=res[2];\n" -" ((threadgroup FLOAT*)sdata)[128+(kl*4+3)*32+16+rcl]=res[3];\n" -" }\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" simdgroup_load(sga[0],(const threadgroup FLOAT*)sdata,8);\n" -" simdgroup_load(sga[1],((const threadgroup FLOAT*)sdata)+64,8);\n" -" \n" -" simdgroup_load(sgb[0],((const threadgroup FLOAT*)sdata)+128,32);\n" -" simdgroup_load(sgb[1],((const threadgroup FLOAT*)sdata)+136,32);\n" -" simdgroup_load(sgb[2],((const threadgroup FLOAT*)sdata)+144,32);\n" -" simdgroup_load(sgb[3],((const threadgroup FLOAT*)sdata)+152,32);\n" -" \n" -" SIMDGROUP_MATRIX_FMA(2,4);\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" xy_in0 += 2*cst.input_size*cst.batch;\n" -" }\n" -" }\n" -" \n" -" SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata,8);\n" -" \n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" if((16*rx+rcl) input: [M32,K8]\n" -" M 256~767 ---> weight: [K8,N64]\n" -" // Write:\n" -" M 0~2047 ---> input: [M2,N2,N2,N2,M2,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[512]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(2,4,8);\n" -" \n" -" int rx=gid.x;// M/32\n" -" int uz=gid.y;// N/64\n" -" \n" -" int kl=tiitg % 2;// 0~1 -> inner K\n" -" int rcl=tiitg/2;// 0~63\n" -" int ko=rcl % 2;// 0~1 -> outter K\n" -" int ml=rcl/2;// 0~31 -> m\n" -" int ni=rcl % 4;// 0~3 -> inner N\n" -" int no=rcl/4;// 0~15 -> outter N\n" -" \n" -" /** input:\n" -" threadgroup: [M32,K8]\n" -" each thread: K2\n" -" layout: [K/4,M,K4] -> [K/8,K2,M/32,M32,K2,K2]\n" -" index : [K/8,ko,rx,ml,kl,K2]\n" -" */\n" -" /** weight:\n" -" threadgroup: [K8,N64]\n" -" each thread: K4\n" -" layout: [N/4,K/4,N4,K4] -> [N/64,N16,K/8,K2,N4,K4]\n" -" index : [uz,no,K/8,kl,ni,K4]\n" -" */\n" -" /** output:\n" -" threadgroup: [M32,N64]\n" -" each thread: M4N4\n" -" layout: [N/4,M,N4] -> [N/16,N4,M,N4]\n" -" index : [uz*4+(2*ko+kl),N4,idx_m,N4]\n" -" */\n" -" \n" -" // boundary limit\n" -" int idx_n40=(uz*16+no)> 4)-8,(float)(w_int4[0] & 15)-8,(float)(w_int4[1] >> 4)-8,(float)(w_int4[1] & 15)-8);\n" -" FLOAT4 res=w4*scale0[ni]+dequant_bias0[ni];\n" -" // sdata[32+2* rcl+kl]=res;\n" -" ((threadgroup FLOAT*)sdata)[idx_sb]=res[0];\n" -" ((threadgroup FLOAT*)sdata)[idx_sb+64]=res[1];\n" -" ((threadgroup FLOAT*)sdata)[idx_sb+128]=res[2];\n" -" ((threadgroup FLOAT*)sdata)[idx_sb+192]=res[3];\n" -" }\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" \n" -" const threadgroup FLOAT*sdata_a=(const threadgroup FLOAT*)sdata+16*8*(sgitg/2);\n" -" const threadgroup FLOAT*sdata_b=(const threadgroup FLOAT*)sdata+32*8+32*(sgitg%2);\n" -" simdgroup_load(sga[0],(const threadgroup FLOAT*)sdata_a,8);\n" -" simdgroup_load(sga[1],((const threadgroup FLOAT*)sdata_a)+64,8);\n" -" \n" -" simdgroup_load(sgb[0],((const threadgroup FLOAT*)sdata_b)+0,64);\n" -" simdgroup_load(sgb[1],((const threadgroup FLOAT*)sdata_b)+8,64);\n" -" simdgroup_load(sgb[2],((const threadgroup FLOAT*)sdata_b)+16,64);\n" -" simdgroup_load(sgb[3],((const threadgroup FLOAT*)sdata_b)+24,64);\n" -" \n" -" SIMDGROUP_MATRIX_FMA(2,4);\n" -" \n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" xy_in0 += 4*cst.input_size*cst.batch;\n" -" }\n" -" }\n" -" \n" -" threadgroup FLOAT*sdata_c=(threadgroup FLOAT*)sdata+512*sgitg;\n" -" \n" -" SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata_c,8);\n" -" threadgroup_barrier(mem_flags::mem_threadgroup);\n" -" \n" -" // sdata [M2,N2,N2,N2,M2,M8,N8]\n" -" // index [ml/16,ko,kl,N2,(ml/8)%2,ml%8,N2,N4]\n" -" if((rx*32+ml)3) {xy_out[3]=activate(M4(result3),cst.activation); }\n" "}\n" -"kernel void conv1x1_gemv_g8_w4_sg(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device MNN::uchar4x2 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" const device M4 *dequantScale [[buffer(5)]],\n" -" uint3 gid[[threadgroup_position_in_grid]],\n" -" uint tiisg[[thread_index_in_simdgroup]],\n" -" uint sgitg[[simdgroup_index_in_threadgroup]]) {\n" -" // each threadgroup contain 2 simdgroup\n" -" // each simdgroup compute 4 data\n" -" int uz=gid.x*2+sgitg;\n" -" if(uz >= cst.output_slice) {\n" -" return;\n" -" }\n" -" int rx=gid.y;\n" -" auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in+(int)gid.z*cst.input_size+rx+0;\n" -" auto xy_out=out+(int)gid.z*cst.output_size+uz*cst.output_size*cst.batch+rx;\n" -" auto biasValue=FLOAT4(biasTerms[uz]);\n" -" FLOAT4 result0=FLOAT4(0);\n" -" int block=(cst.input_slice+cst.block_size-1)/cst.block_size;\n" -" \n" -" int middle_step=min(SIMD_GROUP_WIDTH,block);\n" -" int outer_step=SIMD_GROUP_WIDTH/middle_step;\n" -" int middle_index=(tiisg) % middle_step;\n" -" int outer_index=(tiisg)/middle_step;\n" -" \n" -" for (int bi= outer_index; bi> 4)-8,(float)(w_int4[i][0] & 15)-8,(float)(w_int4[i][1] >> 4)-8,(float)(w_int4[i][1] & 15)-8);\n" -" FLOAT4 res=w4*scale[i]+dequant_bias[i];\n" -" w_dequant[i]=res;\n" -" }\n" -" result0 += FLOAT4(in40*w_dequant);\n" -" \n" -" }\n" -" }\n" -" FLOAT4 res=simd_sum(result0);\n" -" /* true */\n" -" if (tiisg == 0) {\n" -" xy_out[0]=activate(M4(res+biasValue),cst.activation);\n" -" }\n" -"}\n" -"kernel void conv1x1_gemv_g16_w4_sg(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device MNN::uchar4x2 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" const device M4 *dequantScale [[buffer(5)]],\n" -" uint3 gid[[threadgroup_position_in_grid]],\n" -" uint tiisg[[thread_index_in_simdgroup]],\n" -" uint sgitg[[simdgroup_index_in_threadgroup]]) {\n" -" // each threadgroup contain 2 simdgroup\n" -" // each simdgroup compute 8 data\n" -" int uz=2*(gid.x*2+sgitg);\n" -" if(uz >= cst.output_slice) {\n" -" return;\n" -" }\n" -" \n" -" auto xy_wt=wt+uz*cst.input_slice;\n" -" auto xy_in0=in;\n" -" auto xy_out=out+(int)gid.z*cst.output_size+uz;\n" -" auto biasValue0=FLOAT4(biasTerms[uz]);\n" -" auto biasValue1=FLOAT4(biasTerms[uz+1]);\n" -" FLOAT4 result0=FLOAT4(0);\n" -" FLOAT4 result1=FLOAT4(0);\n" -" int block=(cst.input_slice+cst.block_size-1)/cst.block_size;\n" -" \n" -" int middle_step=min(SIMD_GROUP_WIDTH,block);\n" -" int outer_step=SIMD_GROUP_WIDTH/middle_step;\n" -" int middle_index=(tiisg) % middle_step;\n" -" int outer_index=(tiisg)/middle_step;\n" -" \n" -" for (int bi= outer_index; bi> 4)-8,(float)(w_int4[i][0] & 15)-8,(float)(w_int4[i][1] >> 4)-8,(float)(w_int4[i][1] & 15)-8);\n" -" FLOAT4 res=w4*scale0[i]+dequant_bias0[i];\n" -" w_dequant[i]=res;\n" -" }\n" -" result0 += FLOAT4(in40*w_dequant);\n" -" w_int4=xy_wt[cst.input_slice+z];\n" -" for (int i=0; i<4; i += 1) {\n" -" FLOAT4 w4=FLOAT4((float)(w_int4[i][0] >> 4)-8,(float)(w_int4[i][0] & 15)-8,(float)(w_int4[i][1] >> 4)-8,(float)(w_int4[i][1] & 15)-8);\n" -" FLOAT4 res=w4*scale1[i]+dequant_bias1[i];\n" -" w_dequant[i]=res;\n" -" }\n" -" \n" -" result1 += FLOAT4(in40*w_dequant);\n" -" \n" -" }\n" -" }\n" -" FLOAT4 res0=simd_sum(result0);\n" -" FLOAT4 res1=simd_sum(result1);\n" -" /* true */\n" -" if (tiisg == 0) {\n" -" xy_out[0]=activate(M4(res0+biasValue0),cst.activation);\n" -" xy_out[1]=activate(M4(res1+biasValue1),cst.activation);\n" -" }\n" -"}\n" "kernel void conv1x1_g1z8(const device M4 *in [[buffer(0)]],\n" " device M4 *out [[buffer(1)]],\n" " constant conv1x1_constants& cst [[buffer(2)]],\n" @@ -2585,151 +1764,6 @@ const char* shader_MetalConvolution1x1_metal = " if (widthSize>3) {xy_out[cst.output_size*cst.batch +3]=activate(M4(result7),cst.activation); }\n" " }\n" "}\n" -"kernel void conv1x1_gemm_16x16_sg(const device M4 *in [[buffer(0)]],\n" -" device M4 *out [[buffer(1)]],\n" -" constant conv1x1_constants& cst [[buffer(2)]],\n" -" const device M4 *wt [[buffer(3)]],\n" -" const device M4 *biasTerms [[buffer(4)]],\n" -" uint3 gid [[threadgroup_position_in_grid]],\n" -" uint tiitg[[thread_index_in_threadgroup]],\n" -" uint sgitg[[simdgroup_index_in_threadgroup]]) {\n" -" /*\n" -" // Read:\n" -" M 0~127 ---> input: [M16,K8]\n" -" M 128~255 ---> input: [K8,N16]\n" -" // Write:\n" -" M 0~255 ---> input: [N2,M2,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[64]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(2,2,4);\n" -" int rx=gid.x;// M/16\n" -" int uz=gid.y;// N/16\n" -" \n" -" int kl=tiitg/16;\n" -" int rcl=tiitg % 16;\n" -" \n" -" // boundary limit\n" -" int idx_n4=(4*uz+rcl/4) input: [M32,K8]\n" -" M 256~383 ---> input: [K8,N16]\n" -" // Write:\n" -" M 0~511 ---> input: [N2,M4,M8,N8]\n" -" */\n" -" threadgroup FLOAT4 sdata[128]={0.f};\n" -" \n" -" INIT_SIMDGROUP_MATRIX(4,2,8);\n" -" \n" -" int rx=gid.x;// M/32\n" -" int uz=gid.y;// N/16\n" -" \n" -" int kl=tiitg % 2;\n" -" int rcl=tiitg/2;\n" -" \n" -" const int size_m=cst.input_size*cst.batch;\n" -" \n" -" // boundary limit\n" -" int idx_n4=(4*uz+rcl/4) +#include +using namespace metal; +typedef enum : int { + None = 0, + ReLU = 1, + ReLU6 = 2, +} conv_activation_type; + +inline ftype4 activate(ftype4 value, conv_activation_type type) { + switch (type) { + case ReLU: + return max(value, (ftype4)0); + case ReLU6: + return clamp(value, (ftype4)0, (ftype4)6); + default: // None + return value; + } +} +struct conv1x1_constants { + int input_size; + int input_slice; + int output_width; + int output_height; + int output_size; + int output_slice; + int output_channel; + int batch; + int block_size; + conv_activation_type activation; + float scale_coef; +}; + +#if MNN_METAL_FLOAT32_COMPUTER +typedef simdgroup_float8x8 simdgroup_T8x8; +typedef float FLOAT; +typedef float2 FLOAT2; +typedef float4 FLOAT4; +typedef float4x4 FLOAT4x4; +#else +typedef simdgroup_half8x8 simdgroup_T8x8; +typedef half FLOAT; +typedef half2 FLOAT2; +typedef half4 FLOAT4; +typedef half4x4 FLOAT4x4; +#endif + +#define SIMD_GROUP_WIDTH 32 +#define CONV_UNROLL (4) +#define CONV_UNROLL_L (8) + +#define INIT_SIMDGROUP_MATRIX(a, b, d) \ + simdgroup_T8x8 sga[a];\ + simdgroup_T8x8 sgb[b];\ + simdgroup_T8x8 sgd[d];\ + for (int i = 0; i < d; i++){\ + sgd[i] = make_filled_simdgroup_matrix(0.f);\ + } + +#define SIMDGROUP_MATRIX_FMA(a, b) \ + for(int j=0; j input: [M16, K8] + ftype 128~255 ---> input: [K8, N16] + // Write: + ftype 0~255 ---> input: [N2, M2, M8, N8] + */ + threadgroup FLOAT4 sdata[256] = {0.f}; + + INIT_SIMDGROUP_MATRIX(2, 2, 4); + + int rx = gid.x;// M/16 + int uz = gid.y;// N/16 + + int kl = tiitg / 16; + int rcl = tiitg % 16; +// int kl = tiitg % 2; +// int rcl = tiitg / 2; + + // boundary limit + int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); + int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); + + auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] + auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] + auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N/4, M, N4] + + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + for (int bi=0; bi> 4) - 8, (float)(w_int40[0] & 15) - 8, (float)(w_int40[1] >> 4) - 8, (float)(w_int40[1] & 15) - 8); + + FLOAT4 res = w40 * scale[rcl % 4] + dequant_bias[rcl % 4]; + ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 0) * 16 + rcl] = res[0]; + ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 1) * 16 + rcl] = res[1]; + ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 2) * 16 + rcl] = res[2]; + ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 3) * 16 + rcl] = res[3]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + + #pragma unroll(4) + for(int i = 0; i < 4; i++) { + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata + 256*i, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64 + 256*i, 8); + simdgroup_barrier(mem_flags::mem_none); + + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128 + 256*i, 16); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136 + 256*i, 16); + + SIMDGROUP_MATRIX_FMA(2, 2); + } + threadgroup_barrier(mem_flags::mem_threadgroup); + } + } + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 4); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if((16 * rx + rcl) < cst.input_size * cst.batch) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } +} + +kernel void conv1x1_gemm_32x16_w4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device uchar2 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + const device ftype4 *dequantScale [[buffer(5)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + /* + // Read: + ftype 0~255 ---> input: [M32, K8] + ftype 256~383 ---> input: [K8, N16] + // Write: + ftype 0~511 ---> input: [N2, M4, M8, N8] + */ + threadgroup FLOAT4 sdata[128] = {0.f}; + + INIT_SIMDGROUP_MATRIX(4, 2, 8); + + int rx = gid.x;// M/32 + int uz = gid.y;// N/16 + + int kl = tiitg % 2; + int rcl = tiitg / 2; + + const int size_m = cst.input_size * cst.batch; + + // boundary limit + int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); + int idx_m0 = (16 * rx + rcl) < size_m ? (16 * rx + rcl) : (size_m - 1); + int idx_m1 = (16 * rx + rcl) + size_m / 2 < size_m ? (16 * rx + rcl) + size_m / 2: (size_m - 1); + + auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] + auto xy_in0 = in + idx_m0 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] + auto xy_in1 = in + idx_m1 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] + + auto xy_out0 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m0;// [N/4, M, N4] + auto xy_out1 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m1;// [N/4, M, N4] + + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); + FLOAT4 res = w4 * scale[rcl % 4] + dequant_bias[rcl % 4]; + // sdata[32 + 2* rcl + kl] = res; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 0) * 16 + rcl] = res[0]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 1) * 16 + rcl] = res[1]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 2) * 16 + rcl] = res[2]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 3) * 16 + rcl] = res[3]; + + threadgroup_barrier(mem_flags::mem_threadgroup); + + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); + simdgroup_load(sga[2], ((const threadgroup FLOAT*)sdata) + 128, 8); + simdgroup_load(sga[3], ((const threadgroup FLOAT*)sdata) + 192, 8); + + // simdgroup_load(sgb[0], (const threadgroup FLOAT*)sdata + 128, 8, 0, true); + // simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 192, 8, 0, true); + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 256, 16); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 264, 16); + + SIMDGROUP_MATRIX_FMA(4, 2); + threadgroup_barrier(mem_flags::mem_threadgroup); + + xy_in0 += 2 * cst.input_size * cst.batch; + xy_in1 += 2 * cst.input_size * cst.batch; + + } + } + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if((16 * rx + rcl) < size_m) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out0[0] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out0[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } + if((16 * rx + rcl) + size_m / 2 < size_m) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out1[0] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out1[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } +} + +kernel void conv1x1_gemm_16x32_w4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device uchar2 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + const device ftype4 *dequantScale [[buffer(5)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + /* + // Read: + ftype 0~127 ---> input: [M16, K8] + ftype 128~383 ---> input: [K8, N32] + // Write: + ftype 0~511 ---> input: [N2, N2, M2, M8, N8] + */ + threadgroup FLOAT4 sdata[128] = {0.f}; + + INIT_SIMDGROUP_MATRIX(2, 4, 8); + + int rx = gid.x;// M/16 + int uz = gid.y;// N/32 + + int kl = tiitg % 2; + int rcl = tiitg / 2; + + // boundary limit + int idx_n40 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); + int idx_n41 = (4 * uz + rcl / 4) + cst.output_slice / 2 < cst.output_slice ? (4 * uz + rcl / 4) + cst.output_slice / 2 : (cst.output_slice - 1); + + int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); + + auto xy_wt0 = wt + (idx_n40 * cst.input_slice + 0) * 4 + (rcl % 4);// [N2, N/8, K/4, N4, K4] + auto xy_wt1 = wt + (idx_n41 * cst.input_slice + 0) * 4 + (rcl % 4);// [N2, N/8, K/4, N4, K4] + + auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] + auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N2, N/8, M, N4] + + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); + FLOAT4 res = w4 * scale0[rcl % 4] + dequant_bias0[rcl % 4]; + // sdata[32 + 2* rcl + kl] = res; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 32 + rcl] = res[0]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 32 + rcl] = res[1]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 32 + rcl] = res[2]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 32 + rcl] = res[3]; + } + { + uchar2 w_int4 = xy_wt1[4*z]; // [N/4, K/4, N4, K4] + FLOAT4 w4 = FLOAT4((float)(w_int4[0] >> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); + FLOAT4 res = w4 * scale1[rcl % 4] + dequant_bias1[rcl % 4]; + // sdata[32 + 2* rcl + kl] = res; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 32 + 16 + rcl] = res[0]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 32 + 16 + rcl] = res[1]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 32 + 16 + rcl] = res[2]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 32 + 16 + rcl] = res[3]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); + + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128, 32); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136, 32); + simdgroup_load(sgb[2], ((const threadgroup FLOAT*)sdata) + 144, 32); + simdgroup_load(sgb[3], ((const threadgroup FLOAT*)sdata) + 152, 32); + + SIMDGROUP_MATRIX_FMA(2, 4); + threadgroup_barrier(mem_flags::mem_threadgroup); + + xy_in0 += 2 * cst.input_size * cst.batch; + + } + } + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if((16 * rx + rcl) < cst.input_size * cst.batch) { + if(4 * uz + 2 * kl < cst.output_slice) { + xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl])), cst.activation); + } + if(4 * uz + 2 * kl + 1 < cst.output_slice) { + xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + if(cst.output_slice / 2 + 4 * uz + 2 * kl < cst.output_slice) { + xy_out[cst.output_slice / 2 * cst.output_size * cst.batch] = activate(ftype4(sdata[((kl + 2) * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[cst.output_slice / 2 + 4 * uz + 2 * kl])), cst.activation); + } + if(cst.output_slice / 2 + 4 * uz + 2 * kl + 1 < cst.output_slice) { + xy_out[(cst.output_slice / 2 + 1) * cst.output_size * cst.batch] = activate(ftype4(sdata[((kl + 2) * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[cst.output_slice / 2 + 4 * uz + 2 * kl + 1])), cst.activation); + } + } +} + + +kernel void conv1x1_gemm_32x64_w4_sg(const device ftype2 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device uchar2 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + const device ftype4 *dequantScale [[buffer(5)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + /* + // Read: + ftype 0~255 ---> input: [M32, K8] + ftype 256~767 ---> weight: [K8, N64] + // Write: + ftype 0~2047 ---> input: [M2, N2, N2, N2, M2, M8, N8] + */ + threadgroup FLOAT4 sdata[512] = {0.f}; + + INIT_SIMDGROUP_MATRIX(2, 4, 8); + + int rx = gid.x;// M/32 + int uz = gid.y;// N/64 + + int kl = tiitg % 2;// 0~1 -> inner K + int rcl = tiitg / 2;// 0~63 + int ko = rcl % 2;// 0~1 -> outter K + int ml = rcl / 2;// 0~31 -> m + int ni = rcl % 4;// 0~3 -> inner N + int no = rcl / 4;// 0~15 -> outter N + + /** input: + threadgroup: [M32, K8] + each thread: K2 + layout: [K/4, M, K4] -> [K/8, K2, M/32, M32, K2, K2] + index : [K/8, ko, rx, ml, kl, K2] + */ + /** weight: + threadgroup: [K8, N64] + each thread: K4 + layout: [N/4, K/4, N4, K4] -> [N/64, N16, K/8, K2, N4, K4] + index : [uz, no, K/8, kl, ni, K4] + */ + /** output: + threadgroup: [M32, N64] + each thread: M4N4 + layout: [N/4, M, N4] -> [N/16, N4, M, N4] + index : [uz*4+(2*ko+kl), N4, idx_m, N4] + */ + + // boundary limit + + int idx_n40 = (uz * 16 + no) < cst.output_slice ? (uz * 16 + no) : (cst.output_slice - 1); + int idx_m = (rx * 32 + ml) < cst.input_size * cst.batch ? (rx * 32 + ml) : (cst.input_size * cst.batch - 1); + + auto xy_wt0 = wt + ((idx_n40 * cst.input_slice / 2 + 0) * 2 + kl) * 4 + ni;// [N/4, K/4, N4, K4] + + auto xy_in0 = in + ((0 * 2 + ko) * cst.input_size * cst.batch + idx_m) * 2 + kl;// [K/4, M, K2, K2] + auto xy_out = out + ((4 * uz + 2 * ko + kl) * 4 + 0) * cst.output_size * cst.batch + idx_m;// [N2, N/8, M, N4] + + const int idx_sa = ml * 8 + ko * 4 + kl * 2; + const int idx_sb = 256 + (kl * 4 + 0) * 64 + rcl; + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); + FLOAT4 res = w4 * scale0[ni] + dequant_bias0[ni]; + // sdata[32 + 2* rcl + kl] = res; + ((threadgroup FLOAT*)sdata)[idx_sb] = res[0]; + ((threadgroup FLOAT*)sdata)[idx_sb + 64] = res[1]; + ((threadgroup FLOAT*)sdata)[idx_sb + 128] = res[2]; + ((threadgroup FLOAT*)sdata)[idx_sb + 192] = res[3]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + + + const threadgroup FLOAT * sdata_a = (const threadgroup FLOAT*)sdata + 16*8*(sgitg/2); + const threadgroup FLOAT * sdata_b = (const threadgroup FLOAT*)sdata + 32*8 + 32*(sgitg%2); + + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata_a, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata_a) + 64, 8); + + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata_b) + 0, 64); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata_b) + 8, 64); + simdgroup_load(sgb[2], ((const threadgroup FLOAT*)sdata_b) + 16, 64); + simdgroup_load(sgb[3], ((const threadgroup FLOAT*)sdata_b) + 24, 64); + + SIMDGROUP_MATRIX_FMA(2, 4); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + xy_in0 += 4 * cst.input_size * cst.batch; + } + } + + threadgroup FLOAT * sdata_c = (threadgroup FLOAT*)sdata + 512*sgitg; + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata_c, 8); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // sdata [M2, N2, N2, N2, M2, M8, N8] + // index [ml/16, ko, kl, N2, (ml/8)%2, ml%8, N2, N4] + if((rx * 32 + ml) < cst.input_size * cst.batch) { + if((4 * uz + 2 * ko + kl) * 4 < cst.output_slice) { + xy_out[0] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 0) * 16 + ml % 16) * 2] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4])), cst.activation); + } + if((4 * uz + 2 * ko + kl) * 4 + 1 < cst.output_slice) { + xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 0) * 16 + ml % 16) * 2 + 1] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 1])), cst.activation); + } + if((4 * uz + 2 * ko + kl) * 4 + 2 < cst.output_slice) { + xy_out[cst.output_size * cst.batch * 2] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 1) * 16 + ml % 16) * 2] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 2])), cst.activation); + } + if((4 * uz + 2 * ko + kl) * 4 + 3 < cst.output_slice) { + xy_out[cst.output_size * cst.batch * 3] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 1) * 16 + ml % 16) * 2 + 1] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 3])), cst.activation); + } + } +} +)metal"; + +const char* gConv1x1SgMatrix = R"metal( +#include +#include +using namespace metal; + +typedef enum : int { + None = 0, + ReLU = 1, + ReLU6 = 2, +} conv_activation_type; + +inline ftype4 activate(ftype4 value, conv_activation_type type) { + switch (type) { + case ReLU: + return max(value, (ftype4)0); + case ReLU6: + return clamp(value, (ftype4)0, (ftype4)6); + default: // None + return value; + } +} +struct conv1x1_constants { + int input_size; + int input_slice; + int output_width; + int output_height; + int output_size; + int output_slice; + int output_channel; + int batch; + int block_size; + conv_activation_type activation; + float scale_coef; +}; + +#if MNN_METAL_FLOAT32_COMPUTER +typedef simdgroup_float8x8 simdgroup_T8x8; +typedef float FLOAT; +typedef float2 FLOAT2; +typedef float4 FLOAT4; +typedef float4x4 FLOAT4x4; +#else +typedef simdgroup_half8x8 simdgroup_T8x8; +typedef half FLOAT; +typedef half2 FLOAT2; +typedef half4 FLOAT4; +typedef half4x4 FLOAT4x4; +#endif + + +#define SIMD_GROUP_WIDTH 32 +#define CONV_UNROLL (4) +#define CONV_UNROLL_L (8) + +#define INIT_SIMDGROUP_MATRIX(a, b, d) \ + simdgroup_T8x8 sga[a];\ + simdgroup_T8x8 sgb[b];\ + simdgroup_T8x8 sgd[d];\ + for (int i = 0; i < d; i++){\ + sgd[i] = make_filled_simdgroup_matrix(0.f);\ + } + +#define SIMDGROUP_MATRIX_FMA(a, b) \ + for(int j=0; j input: [M16, K8] + ftype 128~255 ---> input: [K8, N16] + // Write: + ftype 0~255 ---> input: [N2, M2, M8, N8] + */ + threadgroup FLOAT4 sdata[64] = {0.f}; + + INIT_SIMDGROUP_MATRIX(2, 2, 4); + int rx = gid.x;// M/16 + int uz = gid.y;// N/16 + + int kl = tiitg / 16; + int rcl = tiitg % 16; + + // boundary limit + int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); + int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); + + auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] + auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] + auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N/4, M, N4] + + for (int z = kl; z < cst.input_slice; z += 2) { + sdata[2* rcl + kl] = FLOAT4(*xy_in0); + xy_in0 += 2 * cst.input_size * cst.batch; + + FLOAT4 w4 = FLOAT4(xy_wt[4 * z]); // [N/4, K/4, N4, K4] + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 16 + rcl] = w4[0]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 16 + rcl] = w4[1]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 16 + rcl] = w4[2]; + ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 16 + rcl] = w4[3]; + + threadgroup_barrier(mem_flags::mem_threadgroup); + + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128, 16); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136, 16); + + SIMDGROUP_MATRIX_FMA(2, 2); + threadgroup_barrier(mem_flags::mem_threadgroup); + } + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 4); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if((16 * rx + rcl) < cst.input_size * cst.batch) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } +} + + +kernel void conv1x1_gemm_32x16_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device ftype4 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + /* + // Read: + ftype 0~255 ---> input: [M32, K8] + ftype 256~383 ---> input: [K8, N16] + // Write: + ftype 0~511 ---> input: [N2, M4, M8, N8] + */ + threadgroup FLOAT4 sdata[128] = {0.f}; + + INIT_SIMDGROUP_MATRIX(4, 2, 8); + + int rx = gid.x;// M/32 + int uz = gid.y;// N/16 + + int kl = tiitg % 2; + int rcl = tiitg / 2; + + const int size_m = cst.input_size * cst.batch; + + // boundary limit + int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); + int idx_m0 = (16 * rx + rcl) < size_m ? (16 * rx + rcl) : (size_m - 1); + int idx_m1 = (16 * rx + rcl) + size_m / 2 < size_m ? (16 * rx + rcl) + size_m / 2: (size_m - 1); + + auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] + auto xy_in0 = in + idx_m0 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] + auto xy_in1 = in + idx_m1 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] + + auto xy_out0 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m0;// [N/4, M, N4] + auto xy_out1 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m1;// [N/4, M, N4] + + for (int z = kl; z < cst.input_slice; z += 2) { + sdata[2* rcl + kl] = (FLOAT4)*xy_in0; + sdata[32 + 2* rcl + kl] = (FLOAT4)*xy_in1; + + FLOAT4 w4 = FLOAT4(xy_wt[4*z]); // [N/4, K/4, N4, K4] + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 0) * 16 + rcl] = w4[0]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 1) * 16 + rcl] = w4[1]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 2) * 16 + rcl] = w4[2]; + ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 3) * 16 + rcl] = w4[3]; + threadgroup_barrier(mem_flags::mem_threadgroup); + + simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); + simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); + simdgroup_load(sga[2], ((const threadgroup FLOAT*)sdata) + 128, 8); + simdgroup_load(sga[3], ((const threadgroup FLOAT*)sdata) + 192, 8); + + simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 256, 16); + simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 264, 16); + + SIMDGROUP_MATRIX_FMA(4, 2); + threadgroup_barrier(mem_flags::mem_threadgroup); + + xy_in0 += 2 * cst.input_size * cst.batch; + xy_in1 += 2 * cst.input_size * cst.batch; + + } + + SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if((16 * rx + rcl) < size_m) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out0[0] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out0[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } + if((16 * rx + rcl) + size_m / 2 < size_m) { + if((4 * uz + 2 * kl) < cst.output_slice) { + xy_out1[0] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); + } + if((4 * uz + 2 * kl + 1) < cst.output_slice) { + xy_out1[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); + } + } +} + +)metal"; + + +const char* gConv1x1SgReduce = R"metal( +#include +#include +using namespace metal; +typedef enum : int { + None = 0, + ReLU = 1, + ReLU6 = 2, +} conv_activation_type; + +inline ftype4 activate(ftype4 value, conv_activation_type type) { + switch (type) { + case ReLU: + return max(value, (ftype4)0); + case ReLU6: + return clamp(value, (ftype4)0, (ftype4)6); + default: // None + return value; + } +} + +struct conv1x1_constants { + int input_size; + int input_slice; + int output_width; + int output_height; + int output_size; + int output_slice; + int output_channel; + int batch; + int block_size; + conv_activation_type activation; + float scale_coef; +}; + +#if MNN_METAL_FLOAT32_COMPUTER +typedef simdgroup_float8x8 simdgroup_T8x8; +typedef float FLOAT; +typedef float2 FLOAT2; +typedef float4 FLOAT4; +typedef float4x4 FLOAT4x4; +#else +typedef simdgroup_half8x8 simdgroup_T8x8; +typedef half FLOAT; +typedef half2 FLOAT2; +typedef half4 FLOAT4; +typedef half4x4 FLOAT4x4; +#endif + + +#define SIMD_GROUP_WIDTH 32 +#define CONV_UNROLL (4) +#define CONV_UNROLL_L (8) + +kernel void conv1x1_z4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device ftype4x4 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + uint3 gid[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + if ((int)gid.x >= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return; + + int rx = gid.x; + int uz = gid.y; + auto xy_wt = wt + uz * cst.input_slice; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; + auto biasValue = FLOAT4(biasTerms[uz]); + FLOAT4 result0 = 0; + + for (int z = tiisg; z < cst.input_slice; z+=SIMD_GROUP_WIDTH) { + auto xy_in = xy_in0 + z * cst.input_size * cst.batch; + auto in40 = *xy_in; + auto w = xy_wt[z]; + + result0 += FLOAT4(in40 * w); + } + result0 = simd_sum(result0); + + *xy_out = activate(ftype4(result0 + biasValue), cst.activation); +} +)metal"; + +const char* gConv1x1W4SgReduce = R"metal( +#include +#include +using namespace metal; +typedef enum : int { + None = 0, + ReLU = 1, + ReLU6 = 2, +} conv_activation_type; + +inline ftype4 activate(ftype4 value, conv_activation_type type) { + switch (type) { + case ReLU: + return max(value, (ftype4)0); + case ReLU6: + return clamp(value, (ftype4)0, (ftype4)6); + default: // None + return value; + } +} + +namespace MNN { + typedef struct uchar4x2 { + private: + uchar2 v[4]; + public: + uchar4x2(uchar2 a) { + v[0] = a; v[1] = a; v[2] = a; v[3] = a; + } + uchar4x2(uchar2 a, uchar2 b, uchar2 c, uchar2 d) { + v[0] = a; v[1] = b; v[2] = c; v[3] = d; + } + + inline thread uchar2& operator[] (const int index) { + return v[index]; + } + inline device uchar2& operator[] (const int index) device { + return v[index]; + } + inline threadgroup uchar2& operator[] (const int index) threadgroup { + return v[index]; + } + + inline const thread uchar2& operator[] (const int index) const { + return v[index]; + } + inline const device uchar2& operator[] (const int index) const device { + return v[index]; + } + inline const threadgroup uchar2& operator[] (const int index) const threadgroup { + return v[index]; + } + + inline explicit operator half4x2() const { + return half4x2( half2(v[0]), half2(v[1]), half2(v[2]), half2(v[3]) ); + } + inline explicit operator half4x2() const device { + return half4x2( half2(v[0]), half2(v[1]), half2(v[2]), half2(v[3]) ); + } + inline explicit operator half4x2() const threadgroup { + return half4x2( half2(v[0]), half2(v[1]), half2(v[2]), half2(v[3]) ); + } + + inline explicit operator float4x2() const { + return float4x2( float2(v[0]), float2(v[1]), float2(v[2]), float2(v[3]) ); + } + inline explicit operator float4x2() const device { + return float4x2( float2(v[0]), float2(v[1]), float2(v[2]), float2(v[3]) ); + } + inline explicit operator float4x2() const threadgroup { + return float4x2( float2(v[0]), float2(v[1]), float2(v[2]), float2(v[3]) ); + } + } uchar4x2; +} + +struct conv1x1_constants { + int input_size; + int input_slice; + int output_width; + int output_height; + int output_size; + int output_slice; + int output_channel; + int batch; + int block_size; + conv_activation_type activation; + float scale_coef; +}; + +#if MNN_METAL_FLOAT32_COMPUTER +typedef simdgroup_float8x8 simdgroup_T8x8; +typedef float FLOAT; +typedef float2 FLOAT2; +typedef float4 FLOAT4; +typedef float4x4 FLOAT4x4; +#else +typedef simdgroup_half8x8 simdgroup_T8x8; +typedef half FLOAT; +typedef half2 FLOAT2; +typedef half4 FLOAT4; +typedef half4x4 FLOAT4x4; +#endif + +#define SIMD_GROUP_WIDTH 32 +#define CONV_UNROLL (4) +#define CONV_UNROLL_L (8) + + +kernel void conv1x1_gemv_g8_w4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device MNN::uchar4x2 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + const device ftype4 *dequantScale [[buffer(5)]], + uint3 gid[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + // each threadgroup contain 2 simdgroup + // each simdgroup compute 4 data + int uz = gid.x * 2 + sgitg; + if(uz >= cst.output_slice) { + return; + } + + int rx = gid.y; + auto xy_wt = wt + uz * cst.input_slice; + auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; + auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; + auto biasValue = FLOAT4(biasTerms[uz]); + FLOAT4 result0 = FLOAT4(0); + + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + + int middle_step = min(SIMD_GROUP_WIDTH, block); + int outer_step = SIMD_GROUP_WIDTH / middle_step; + int middle_index = (tiisg) % middle_step; + int outer_index = (tiisg) / middle_step; + + for (int bi= outer_index; bi> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); + FLOAT4 res = w4 * scale[i] + dequant_bias[i]; + w_dequant[i] = res; + } + + result0 += FLOAT4(in40 * w_dequant); + + } + } + + FLOAT4 res = simd_sum(result0); + /* true */ + if (tiisg == 0) { + xy_out[0] = activate(ftype4(res + biasValue), cst.activation); + } +} + +kernel void conv1x1_gemv_g16_w4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant conv1x1_constants& cst [[buffer(2)]], + const device MNN::uchar4x2 *wt [[buffer(3)]], + const device ftype4 *biasTerms [[buffer(4)]], + const device ftype4 *dequantScale [[buffer(5)]], + uint3 gid[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + // each threadgroup contain 2 simdgroup + // each simdgroup compute 8 data + int uz = 2 * (gid.x * 2 + sgitg); + if(uz >= cst.output_slice) { + return; + } + + auto xy_wt = wt + uz * cst.input_slice; + auto xy_in0 = in; + auto xy_out = out + (int)gid.z * cst.output_size + uz; + auto biasValue0 = FLOAT4(biasTerms[uz]); + auto biasValue1 = FLOAT4(biasTerms[uz + 1]); + + FLOAT4 result0 = FLOAT4(0); + FLOAT4 result1 = FLOAT4(0); + + int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; + + int middle_step = min(SIMD_GROUP_WIDTH, block); + int outer_step = SIMD_GROUP_WIDTH / middle_step; + int middle_index = (tiisg) % middle_step; + int outer_index = (tiisg) / middle_step; + + for (int bi= outer_index; bi> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); + FLOAT4 res = w4 * scale0[i] + dequant_bias0[i]; + w_dequant[i] = res; + } + result0 += FLOAT4(in40 * w_dequant); + + w_int4 = xy_wt[cst.input_slice + z]; + for (int i = 0; i < 4; i += 1) { + FLOAT4 w4 = FLOAT4((float)(w_int4[i][0] >> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); + FLOAT4 res = w4 * scale1[i] + dequant_bias1[i]; + w_dequant[i] = res; + } + + result1 += FLOAT4(in40 * w_dequant); + + } + } + + FLOAT4 res0 = simd_sum(result0); + FLOAT4 res1 = simd_sum(result1); + + /* true */ + if (tiisg == 0) { + xy_out[0] = activate(ftype4(res0 + biasValue0), cst.activation); + xy_out[1] = activate(ftype4(res1 + biasValue1), cst.activation); + + } +} +)metal"; + + +#endif + diff --git a/source/backend/metal/LayerNormSimdGroupShader.hpp b/source/backend/metal/LayerNormSimdGroupShader.hpp new file mode 100644 index 000000000..8072394ff --- /dev/null +++ b/source/backend/metal/LayerNormSimdGroupShader.hpp @@ -0,0 +1,255 @@ +// +// layerNormSimdGroupShader.hpp +// MNN +// +// Created by MNN on b'2024/12/30'. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#if MNN_METAL_ENABLED + +const char* gLayerNormSgReduce = R"metal( +#include +#include +using namespace metal; +struct layernorm_constants { + int inside; + int outside; + float eps; + int has_gamma_beta; +}; + +#define SIMD_GROUP_WIDTH 32 +#define CONV_UNROLL (4) +#define CONV_UNROLL_L (8) + +kernel void layernorm_x1_sg(const device ftype *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float *gamma [[buffer(3)]], + const device float *beta [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + if ((int)gid.x >= cst.inside || (int)gid.y >= cst.outside) { + return; + } + auto in_data = in + gid.y * cst.inside; + auto out_data = out + gid.y * cst.inside; + + float mean; + float sum = 0.0f; + float square_sum = 0.0f; + + for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { + sum += in_data[i]; + } + sum = simd_sum(sum); + mean = sum / cst.inside; + + for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { + float dis = (in_data[i] - mean); + square_sum += dis * dis; + } + square_sum = simd_sum(square_sum); + + if(tiisg == 0) { + float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); + + float norm = var * ((float)in_data[gid.x] - mean); + if(cst.has_gamma_beta) { + out_data[gid.x] = (ftype)(norm * gamma[gid.x] + beta[gid.x]); + } else { + out_data[gid.x] = (ftype)(norm); + } + } +} + +kernel void layernorm_x4_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float4 *gamma [[buffer(3)]], + const device float4 *beta [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + if ((int)gid.x >= cst.inside/4 || (int)gid.y >= cst.outside) { + return; + } + auto in_data = in + gid.y * cst.inside/4; + auto out_data = out + gid.y * cst.inside/4; + + float mean; + float sum = 0.0f; + float square_sum = 0.0f; + + for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { + sum += in_data[i].x; + sum += in_data[i].y; + sum += in_data[i].z; + sum += in_data[i].w; + } + sum = simd_sum(sum); + mean = sum / cst.inside; + + for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { + float dis = (in_data[i].x - mean); + square_sum += dis * dis; + dis = (in_data[i].y - mean); + square_sum += dis * dis; + dis = (in_data[i].z - mean); + square_sum += dis * dis; + dis = (in_data[i].w - mean); + square_sum += dis * dis; + } + square_sum = simd_sum(square_sum); + + if(tiisg == 0) { + float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); + + float4 norm = var * ((float4)in_data[gid.x] - mean); + if(cst.has_gamma_beta) { + out_data[gid.x] = (ftype4)(norm * gamma[gid.x] + beta[gid.x]); + } else { + out_data[gid.x] = (ftype4)(norm); + } + } +} + + +kernel void layernorm_x1_rms_sg(const device ftype *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float *gamma [[buffer(3)]], + const device float *beta [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + if ((int)gid.x >= cst.inside || (int)gid.y >= cst.outside) { + return; + } + auto in_data = in + gid.y * cst.inside; + auto out_data = out + gid.y * cst.inside; + + float square_sum = 0.0f; + + for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { + float dis = in_data[i]; + square_sum += dis * dis; + } + + square_sum = simd_sum(square_sum); + + if(tiisg == 0) { + float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); + + float norm = var * ((float)in_data[gid.x]); + if(cst.has_gamma_beta) { + out_data[gid.x] = (ftype)(norm * gamma[gid.x] + beta[gid.x]); + } else { + out_data[gid.x] = (ftype)(norm); + } + } +} + +kernel void layernorm_x4_rms_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float4 *gamma [[buffer(3)]], + const device float4 *beta [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + if ((int)gid.x >= cst.inside/4 || (int)gid.y >= cst.outside) { + return; + } + + int in_idx = gid.x; + int out_idx = gid.y; + + auto in_data = in + out_idx * cst.inside/4; + auto out_data = out + out_idx * cst.inside/4; + + float4 square_sum = 0.0f; + float square_sum_all = 0.0f; + for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { + float4 data = float4(in_data[i]); + square_sum += data * data; + } + square_sum_all += (square_sum[0] + square_sum[1] + square_sum[2] + square_sum[3]); + square_sum_all = simd_sum(square_sum_all); + + if(tiisg == 0) { + float var = 1.0 / sqrt(square_sum_all / cst.inside + cst.eps); + + float4 norm = var * ((float4)in_data[in_idx]); + if(cst.has_gamma_beta) { + out_data[in_idx] = (ftype4)(norm * gamma[in_idx] + beta[in_idx]); + } else { + out_data[in_idx] = (ftype4)(norm); + } + } +} + +kernel void layernorm_x16_rms_sg(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float4 *gamma [[buffer(3)]], + const device float4 *beta [[buffer(4)]], + uint3 gid [[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + int in_idx = gid.x * 4; + int out_idx = gid.y; + + auto in_data = in + out_idx * cst.inside/4; + auto out_data = out + out_idx * cst.inside/4; + + float4 square_sum = 0.0f; + float square_sum_all = 0.0f; + for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { + float4 data = float4(in_data[i]); + square_sum += data * data; + } + square_sum_all += (square_sum[0] + square_sum[1] + square_sum[2] + square_sum[3]); + square_sum_all = simd_sum(square_sum_all); + float var = 1.0 / sqrt(square_sum_all / cst.inside + cst.eps); + + if(tiisg == 0) { + float4 norm = var * ((float4)in_data[in_idx]); + if(cst.has_gamma_beta) { + out_data[in_idx] = (ftype4)(norm * gamma[in_idx] + beta[in_idx]); + } else { + out_data[in_idx] = (ftype4)(norm); + } + } + if(tiisg == 1 && in_idx + 1 < cst.inside/4) { + float4 norm = var * ((float4)in_data[in_idx+1]); + if(cst.has_gamma_beta) { + out_data[in_idx+1] = (ftype4)(norm * gamma[in_idx+1] + beta[in_idx+1]); + } else { + out_data[in_idx+1] = (ftype4)(norm); + } + } + if(tiisg == 2 && in_idx + 2 < cst.inside/4) { + float4 norm = var * ((float4)in_data[in_idx+2]); + if(cst.has_gamma_beta) { + out_data[in_idx+2] = (ftype4)(norm * gamma[in_idx+2] + beta[in_idx+2]); + } else { + out_data[in_idx+2] = (ftype4)(norm); + } + } + if(tiisg == 3 && in_idx + 3 < cst.inside/4) { + float4 norm = var * ((float4)in_data[in_idx+3]); + if(cst.has_gamma_beta) { + out_data[in_idx+3] = (ftype4)(norm * gamma[in_idx+3] + beta[in_idx+3]); + } else { + out_data[in_idx+3] = (ftype4)(norm); + } + } +} + +)metal"; + +#endif + diff --git a/source/backend/metal/MNNMetalContext.mm b/source/backend/metal/MNNMetalContext.mm index 2fe8a7199..f785be18e 100644 --- a/source/backend/metal/MNNMetalContext.mm +++ b/source/backend/metal/MNNMetalContext.mm @@ -24,8 +24,8 @@ @interface MNNMetalContext () @property (strong, nonatomic) id device; @property (assign, nonatomic) BOOL isIphone; // private -@property (strong, nonatomic) NSMutableDictionary> *cachesFp32; -@property (strong, nonatomic) NSMutableDictionary> *cachesFp16; +@property (strong, nonatomic) NSDictionary> *cachesFp32; +@property (strong, nonatomic) NSDictionary> *cachesFp16; @end @implementation MNNMetalContext @@ -95,11 +95,15 @@ + (BOOL)isIphone{ - (BOOL) initWithSharedContext:(const MNNMetalSharedContext*)context dev:(id)device { MNN_ASSERT(nullptr != context); _device = context->device; - _cachesFp16 = [NSMutableDictionary dictionary]; - _cachesFp32 = [NSMutableDictionary dictionary]; + NSMutableDictionary* tmp_cachesFp16 = [NSMutableDictionary dictionary]; + NSMutableDictionary* tmp_cachesFp32 = [NSMutableDictionary dictionary]; _isIphone = self.class.isIphone; - createLibrary(_device, _cachesFp16, true); - createLibrary(_device, _cachesFp32, false); + createLibrary(_device, tmp_cachesFp16, true); + createLibrary(_device, tmp_cachesFp32, false); + _cachesFp16 = [NSDictionary dictionaryWithDictionary:tmp_cachesFp16]; + _cachesFp32 = [NSDictionary dictionaryWithDictionary:tmp_cachesFp32]; + tmp_cachesFp16 = nil; + tmp_cachesFp32 = nil; return nil != _device; } @@ -141,15 +145,19 @@ - (MTLResourceOptions)optionForAccess:(MNN::MetalAccess)access { - (id)pipelineWithSourceOption:(NSString *)source name:(NSString *)name options:(MTLCompileOptions *)options { NSError *err = nil; auto library = [_device newLibraryWithSource:source options:options error:&err]; + if (err) { + NSLog(@"Warning: pipelineWithSource error: %@, source is: %@", err, source); + } if (nil == library) { - if (err) { - NSLog(@"Warning: pipelineWithSource error: %@", err); - } return nil; } id function = [library newFunctionWithName:name]; - NSError *error = nil; - id result = [_device newComputePipelineStateWithFunction:function error:&error]; + if (nil == function) { + NSLog(@"Warning: Create function failed: %@", name); + return nil; + } + err = nil; + id result = [_device newComputePipelineStateWithFunction:function error:&err]; return result; } diff --git a/source/backend/metal/MetalAttention.mm b/source/backend/metal/MetalAttention.mm index 18db999e6..04c6b4190 100644 --- a/source/backend/metal/MetalAttention.mm +++ b/source/backend/metal/MetalAttention.mm @@ -13,6 +13,7 @@ #import "MNNMetalContext.h" #import "MetalAttentionShader.hpp" #include "MNN_generated.h" +#include "core/OpCommonUtils.hpp" #if MNN_METAL_ENABLED #ifdef MNN_SUPPORT_TRANSFORMER_FUSE @@ -41,15 +42,16 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { private: void _init(); - void reallocKVCache(int history_len); + void reallocKVCache(); void compilerShader(const std::vector &inputs); bool mKVCache; std::shared_ptr mCache; float mScale; const int mExpandChunk = 64; - bool mIsDecode = false; + bool mShortSeq = false; std::shared_ptr mTempQK, mTempSoftMax; int mNumHead = 0, mHeadDim = 0, mValueH = 0, mKvNumHead = 0; + int mSeqLen; id mKernel_softmax = nil; id mKernel_qk = nil; @@ -62,6 +64,7 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { id mParamCopy; private: + KVMeta* mMeta; bool mQkSimdReduce = false; bool mQkSimdMatrix = false; bool mSftmSimdReduce = false; @@ -87,6 +90,8 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { mCache.reset(new SharedCache); auto mtbn = static_cast(backend()); auto context = (__bridge MNNMetalContext *)mtbn->context(); + mMeta = (KVMeta*)(mtbn->getRuntime()->pMeta); + mParamQKV = [context newDeviceBuffer:sizeof(Param) access:CPUWriteOnly]; mParamSoftmax = [context newDeviceBuffer:4 * sizeof(int) access:CPUWriteOnly]; mParamCopy = [context newDeviceBuffer:5 * sizeof(int) access:CPUWriteOnly]; @@ -94,63 +99,94 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { mTempSoftMax.reset(Tensor::createDevice({0, 0})); } -void AttentionBufExecution::reallocKVCache(int history_len) { - /* - when kv-cache - decoding: past_len > max_len, realloc and copy past_len cache - prefill : max_len == 0 (first prefill), realloc and copy history_len cache - prefill : max_len > 0 (not first prefill) && past_len >= max_len, realloc and copy history_len cache. copy current prompt in copy shader(not this function) - prefill : max_len > 0 (not first prefill) && past_len < max_len, not realloc and no need copy history_len cache, just copy current prompt in copy shader(not this function) - - */ +void AttentionBufExecution::reallocKVCache() { if (!mKVCache) { return; } + auto kv_seq_len = mMeta->previous + mMeta->add - mMeta->remove + mMeta->computeReverseSize(); - if (mIsDecode && mCache->mPastLength < mCache->mMaxLength) { - return; - } - - // not first prefill (do reuse_kvcache) and total past_len < max_len - if(!mIsDecode && mCache->mMaxLength > 0 && mCache->mPastLength < mCache->mMaxLength && history_len != 0) { - return; - } auto mtbn = static_cast(backend()); int byte = 4; if(mtbn->useFp16InsteadFp32()) { byte = 2; } - bool needCopy = history_len > 0; - - size_t old_size = mKvNumHead * history_len * mHeadDim * byte; - size_t old_piece_size = history_len * byte; - size_t old_piece_stride = mCache->mMaxLength * byte; - - mCache->mMaxLength = mCache->mPastLength + mExpandChunk; - // past_key: [1, numhead, headdim, maxlen] - auto new_key = Tensor::createDevice({mCache->mMaxLength, mKvNumHead, mHeadDim}); - // past_value: [1, numhead, maxlen, headdim] - auto new_value = Tensor::createDevice({mKvNumHead, mHeadDim, mCache->mMaxLength}); - size_t size = mKvNumHead * mCache->mMaxLength * mHeadDim * byte; - backend()->onAcquireBuffer(new_key, Backend::STATIC); - backend()->onAcquireBuffer(new_value, Backend::STATIC); - if (needCopy) { - auto newKeyBuf = MetalBackend::getBuffer(new_key); - auto new_key_ptr = (uint8_t*)[newKeyBuf.first contents] + newKeyBuf.second; - auto keyBuf = MetalBackend::getBuffer(mCache->mPastKey.get()); - auto key_ptr = (uint8_t*)[keyBuf.first contents] + keyBuf.second;; - ::memcpy(new_key_ptr, key_ptr, old_size); + + auto start = mCache->mPastLength - mMeta->remove; + // latest length larger than maxLen + if (kv_seq_len > mCache->mMaxLength) { + + auto copy_len = mCache->mPastLength - mMeta->remove + mMeta->computeReverseSize(); + bool needCopy = copy_len > 0; - auto newValueBuf = MetalBackend::getBuffer(new_value); - auto new_value_ptr = (uint8_t*)[newValueBuf.first contents] + newValueBuf.second; + size_t old_size = mKvNumHead * start * mHeadDim * byte; + size_t old_piece_size = start * byte; + size_t old_piece_stride = mCache->mMaxLength * byte; + + mCache->mMaxLength = kv_seq_len + mExpandChunk; + // past_key: [1, numhead, headdim, maxlen] + auto new_key = Tensor::createDevice({mCache->mMaxLength, mKvNumHead, mHeadDim}); + // past_value: [1, numhead, maxlen, headdim] + auto new_value = Tensor::createDevice({mKvNumHead, mHeadDim, mCache->mMaxLength}); + size_t size = mKvNumHead * mCache->mMaxLength * mHeadDim * byte; + auto res = backend()->onAcquireBuffer(new_key, Backend::STATIC); + res = res && backend()->onAcquireBuffer(new_value, Backend::STATIC); + if(!res) { + MNN_ERROR("attition kv cache realloc memory error:%d\n", res); + } + if (needCopy) { + auto newKeyBuf = MetalBackend::getBuffer(new_key); + auto new_key_ptr = (uint8_t*)[newKeyBuf.first contents] + newKeyBuf.second; + auto keyBuf = MetalBackend::getBuffer(mCache->mPastKey.get()); + auto key_ptr = (uint8_t*)[keyBuf.first contents] + keyBuf.second;; + ::memcpy(new_key_ptr, key_ptr, old_size); + + auto newValueBuf = MetalBackend::getBuffer(new_value); + auto new_value_ptr = (uint8_t*)[newValueBuf.first contents] + newValueBuf.second; + auto valueBuf = MetalBackend::getBuffer(mCache->mPastValue.get()); + auto value_ptr = (uint8_t*)[valueBuf.first contents] + valueBuf.second; + for(int i = 0; i < mKvNumHead * mHeadDim; i++) { + ::memcpy(new_value_ptr + i * mCache->mMaxLength * byte, value_ptr + i * old_piece_stride, old_piece_size); + } + } + mCache->mPastLength = (int)start; + + mCache->mPastKey.reset(new_key); + mCache->mPastValue.reset(new_value); + } + + // Remove + { + if (0 == mMeta->n_reserve) { + mCache->mPastLength = start; + return; + } + + auto keyBuf = MetalBackend::getBuffer(mCache->mPastKey.get()); + auto key_ptr = (uint8_t*)[keyBuf.first contents] + keyBuf.second; auto valueBuf = MetalBackend::getBuffer(mCache->mPastValue.get()); auto value_ptr = (uint8_t*)[valueBuf.first contents] + valueBuf.second; - for(int i = 0; i < mKvNumHead * mHeadDim; i++) { - ::memcpy(new_value_ptr + i * mCache->mMaxLength * byte, value_ptr + i * old_piece_stride, old_piece_size); + + // TODO: need to ensure reserve info is sorted + for (int n = 0; n < mMeta->n_reserve; ++n) { + auto begin = mMeta->reserve[2 * n]; + auto length = mMeta->reserve[2 * n + 1]; + // past_key : [mCache->mPastLength, mKvNumHead, mHeadDim] + // past_value : [mKvNumHead, mHeadDim, mCache->mMaxLength] + + auto copy_src_index = mCache->mPastLength - mMeta->remove + begin; + auto copy_dst_index = start; + for(int i = 0; i < length; i++) { + ::memcpy(key_ptr + (copy_dst_index + i) * mKvNumHead * mHeadDim * byte, key_ptr + (copy_src_index + i) * mKvNumHead * mHeadDim * byte, mKvNumHead * mHeadDim * byte); + } + for(int j = 0; j < mKvNumHead * mHeadDim; j++) { + for(int i = 0; i < length; i++) { + ::memcpy(value_ptr + (j * mCache->mMaxLength + copy_dst_index + i) * byte, value_ptr + (j * mCache->mMaxLength + copy_src_index + i) * byte, byte); + } + } + start += length; } + mCache->mPastLength = (int)start; } - mCache->mPastKey.reset(new_key); - mCache->mPastValue.reset(new_value); } void AttentionBufExecution::compilerShader(const std::vector &inputs) { @@ -173,7 +209,7 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { } // QK matmul total thread is large - mUseHeadNum2 = mIsDecode && mCache->mKv_seq_len > 1024; + mUseHeadNum2 = mShortSeq && mCache->mKv_seq_len > 1024; if(mUseHeadNum2) { qkKeys.emplace_back("HEAD_NUM_2"); } @@ -252,7 +288,25 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { MNN_ASSERT(nil != mKernel_copy); if(mSftmSimdReduce) { - mKernel_softmax = [context pipelineWithName:@"softmax_plane_sg" fp16:mtbn->useFp16InsteadFp32()]; + // basic marco info + std::string ftype = "float"; + if (mtbn->useFp16InsteadFp32()) { + ftype = "half"; + } + + MTLCompileOptions *option = [[MTLCompileOptions alloc] init]; + auto dic = [NSMutableDictionary dictionaryWithCapacity:0]; + option.preprocessorMacros = @{ + @"ftype" : @(ftype.c_str()), + }; + std::vector keys = {"softmax_sg_reduce", ftype, "softmax_plane_sg"}; + + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = mtbn->makeComputePipelineWithSourceOption(gSoftmaxSgReduce, "softmax_plane_sg", option); + rt->insertPipeline(keys, pipeline); + } + mKernel_softmax = pipeline; } else { mKernel_softmax = [context pipelineWithName:@"softmax_plane" fp16:mtbn->useFp16InsteadFp32()]; } @@ -268,67 +322,47 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { auto mtbn = static_cast(backend()); auto context = (__bridge MNNMetalContext *)mtbn->context(); auto shape = query->shape(); - int seq_len = shape[1]; + mSeqLen = shape[1]; mNumHead = shape[2]; mHeadDim = shape[3]; mScale = 1.0 / sqrt(mHeadDim); - mIsDecode = seq_len == 1; - - int history_len = mCache->mPastLength; - // first prefill set history_len to 0 - if(!mIsDecode && mask->length(2) == mask->length(3)) { - history_len = 0; - } - if (!mIsDecode) { - mCache->mPastLength = mask->length(3); - } - mCache->mKv_seq_len = mCache->mPastLength; - if(mIsDecode){ - mCache->mKv_seq_len = mCache->mPastLength + 1; - } + // currently only mSeqLen=1 means short_seq + mShortSeq = mSeqLen == 1; mKvNumHead = key->shape()[2]; + int group_size = mNumHead / mKvNumHead; + + mCache->mPastLength = mMeta->previous; + // kv-cache realloc function + reallocKVCache(); + + mCache->mKv_seq_len = mCache->mPastLength + mSeqLen; auto rt = (MetalRuntime*)mtbn->runtime(); bool supportSimdReduce = rt->supportSimdGroupReduce(); bool supportSimdMatrix = rt->supportSimdGroupMatrix(); // decode and thread number not too large - mQkSimdReduce = supportSimdReduce && seq_len == 1; + mQkSimdReduce = supportSimdReduce && mSeqLen == 1; // loop_k can divide 8, thus avoid branch - mQkSimdMatrix = supportSimdMatrix && seq_len >= 16 && mHeadDim % 8 == 0; + mQkSimdMatrix = supportSimdMatrix && mSeqLen >= 16 && mHeadDim % 8 == 0; mSftmSimdReduce = supportSimdReduce; - mQkvSimdReduce = supportSimdReduce && seq_len == 1 && mHeadDim * mNumHead < mCache->mKv_seq_len * 32; - mQkvSimdMatrix = supportSimdMatrix && seq_len >= 16; + mQkvSimdReduce = supportSimdReduce && mSeqLen == 1 && mHeadDim * mNumHead < mCache->mKv_seq_len * 32; + mQkvSimdMatrix = supportSimdMatrix && mSeqLen >= 16; // start to compile attention shaders compilerShader(inputs); - int group_size = mNumHead / mKvNumHead; - - // kv-cache realloc function - reallocKVCache(history_len); - // temp tensor alloc memory bool needMalloc = mTempQK->length(0) != mNumHead; - if (mIsDecode) { - if (mTempQK->length(1) != mCache->mMaxLength) { - needMalloc = true; - } - mTempQK->setLength(0, mNumHead); - mTempQK->setLength(1, mCache->mMaxLength); - mTempSoftMax->setLength(0, mNumHead); - mTempSoftMax->setLength(1, mCache->mMaxLength); - - } else { - if (mTempQK->length(1) != seq_len * mCache->mPastLength) { - needMalloc = true; - } - mTempQK->setLength(0, mNumHead); - mTempQK->setLength(1, seq_len * mCache->mPastLength); - mTempSoftMax->setLength(0, mNumHead); - mTempSoftMax->setLength(1, seq_len * mCache->mPastLength); + if (mTempQK->length(1) != mSeqLen * mCache->mMaxLength) { + needMalloc = true; } + mTempQK->setLength(0, mNumHead); + mTempQK->setLength(1, mSeqLen * mCache->mMaxLength); + mTempSoftMax->setLength(0, mNumHead); + mTempSoftMax->setLength(1, mSeqLen * mCache->mMaxLength); + if (needMalloc) { auto res = backend()->onAcquireBuffer(mTempQK.get(), Backend::STATIC) && backend()->onAcquireBuffer(mTempSoftMax.get(), Backend::STATIC); if (!res) { @@ -345,18 +379,12 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { param->key_seq_len = mCache->mKv_seq_len; param->head_num = mNumHead; param->group = group_size; - param->query_seq_len = seq_len; + param->query_seq_len = mSeqLen; param->max_kv_len = mCache->mMaxLength; } // For softmax parameter - int inside, outside; - if (mIsDecode) { - inside = 1; - outside = mNumHead; - } else { - inside = 1; - outside = seq_len * mNumHead; - } + int inside = 1; + int outside = mSeqLen * mNumHead; int axis = mCache->mKv_seq_len; { auto softmax = (int*)mParamSoftmax.contents; @@ -370,32 +398,16 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { // Run Copy Kernel { auto copyp = (int*)mParamCopy.contents; + /* + Key -> K-Cache : [mCache->mKv_seq_len, mKvNumHead, mHeadDim] -> [mCache->mKv_seq_len, mKvNumHead, mHeadDim] + Value -> V-Cache : [mCache->mKv_seq_len, mKvNumHead, mHeadDim] -> [mKvNumHead, mHeadDim, mCache->mMaxLength (fill when decode)] + */ copyp[0] = mKvNumHead * mHeadDim; - - int copy_line; - if(mIsDecode) { - /* - each decode fill one kv_seq. - Key -> K-Cache : [1, mKvNumHead, mHeadDim] -> [mCache->mKv_seq_len + 1, mKvNumHead, mHeadDim] - Value -> V-Cache : [1, mKvNumHead, mHeadDim] -> [mKvNumHead, mHeadDim, mCache->mKv_seq_len + 1] - */ - copyp[1] = 1; - copyp[2] = mCache->mMaxLength; - copyp[3] = (mCache->mKv_seq_len - 1) * copyp[0]; - copyp[4] = mCache->mKv_seq_len - 1; - copy_line = 1; - } else { - /* - first time copy. - Key -> K-Cache : [mCache->mKv_seq_len, mKvNumHead, mHeadDim] -> [mCache->mKv_seq_len, mKvNumHead, mHeadDim] - Value -> V-Cache : [mCache->mKv_seq_len, mKvNumHead, mHeadDim] -> [mKvNumHead, mHeadDim, mCache->mMaxLength (fill when decode)] - */ - copyp[1] = seq_len; - copyp[2] = mCache->mMaxLength; - copyp[3] = history_len * copyp[0]; - copyp[4] = history_len; - copy_line = seq_len; - } + copyp[1] = mSeqLen; + copyp[2] = mCache->mMaxLength; + copyp[3] = mCache->mPastLength * copyp[0]; + copyp[4] = mCache->mPastLength; + int copy_line = mSeqLen; id pipeline = mKernel_copy; [encoder setComputePipelineState:pipeline]; @@ -414,7 +426,7 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { // Run QK Kernel { id pipeline; - if (mIsDecode) { + if (mShortSeq) { pipeline = mKernel_qk; } else { pipeline = mKernelPrefill_qk; @@ -432,13 +444,13 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { } std::pair gl; if(mQkSimdReduce) { - gl = std::make_pair(MTLSizeMake(seq_len, decode_grid_y, mCache->mKv_seq_len), MTLSizeMake(32, 1, 1)); + gl = std::make_pair(MTLSizeMake(mSeqLen, decode_grid_y, mCache->mKv_seq_len), MTLSizeMake(32, 1, 1)); } else if(mQkSimdMatrix) { - gl = std::make_pair(MTLSizeMake(UP_DIV(seq_len, 16), UP_DIV(mCache->mKv_seq_len, 16), mNumHead), MTLSizeMake(32, 1, 1)); - } else if(mIsDecode){ - gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(seq_len, decode_grid_y, mCache->mKv_seq_len)]; + gl = std::make_pair(MTLSizeMake(UP_DIV(mSeqLen, 16), UP_DIV(mCache->mKv_seq_len, 16), mNumHead), MTLSizeMake(32, 1, 1)); + } else if(mShortSeq){ + gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(mSeqLen, decode_grid_y, mCache->mKv_seq_len)]; } else { - gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(seq_len, mNumHead, mCache->mKv_seq_len)]; + gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(mSeqLen, mNumHead, mCache->mKv_seq_len)]; } [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; @@ -464,7 +476,7 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { // Run QKV Kernel { id pipeline; - if (mIsDecode) { + if (mShortSeq) { pipeline = mKernel_qkv; } else { pipeline = mKernelPrefill_qkv; @@ -476,21 +488,17 @@ virtual bool onClone(Backend* bn, const Op* op, Execution** dst) override { [encoder setBuffer:mParamQKV offset:0 atIndex:3]; std::pair gl; if(mQkvSimdReduce) { - gl = std::make_pair(MTLSizeMake(seq_len, mNumHead, mHeadDim), MTLSizeMake(32, 1, 1)); + gl = std::make_pair(MTLSizeMake(mSeqLen, mNumHead, mHeadDim), MTLSizeMake(32, 1, 1)); } else if(mQkvSimdMatrix){ - gl = std::make_pair(MTLSizeMake(UP_DIV(seq_len, 16), UP_DIV(mHeadDim, 16), mNumHead), MTLSizeMake(32, 1, 1)); + gl = std::make_pair(MTLSizeMake(UP_DIV(mSeqLen, 16), UP_DIV(mHeadDim, 16), mNumHead), MTLSizeMake(32, 1, 1)); } else { - gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(seq_len, mNumHead, mHeadDim)]; + gl = [context computeBestGroupAndLocal:pipeline threads:MTLSizeMake(mSeqLen, mNumHead, mHeadDim)]; } [encoder dispatchThreadgroups:gl.first threadsPerThreadgroup:gl.second]; } // Update status - if(mIsDecode){ - mCache->mPastLength += 1; - mCache->mKv_seq_len = mCache->mPastLength + 1; - } - + mCache->mPastLength += mSeqLen; return; } diff --git a/source/backend/metal/MetalAttentionShader.hpp b/source/backend/metal/MetalAttentionShader.hpp index 9209be406..b566de835 100644 --- a/source/backend/metal/MetalAttentionShader.hpp +++ b/source/backend/metal/MetalAttentionShader.hpp @@ -631,6 +631,54 @@ kernel void decode_qkv(const device T* input0 [[buffer(0)]], } )metal"; +const char* gSoftmaxSgReduce = R"metal( +#include +using namespace metal; +struct softmax_shape { + int inside_size; + int axis_length; + int outside_size; + int flat_length; +}; +#define SIMD_GROUP_WIDTH 32 + +kernel void softmax_plane_sg(const device ftype *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant softmax_shape& s [[buffer(2)]], + uint2 gid[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]] + ) { + // threadgroup contain one simdgroup + // simdgroup compute axis data + if ((int)gid.x >= s.inside_size || (int)gid.y >= s.outside_size) return; + + auto axis_off = gid.y * s.axis_length * s.inside_size + gid.x; + auto axis_in = in + axis_off; + auto axis_out = out + axis_off; + + // get max + float max1 = -INFINITY; + for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { + max1 = max(max1, float(axis_in[i * s.inside_size])); + } + max1 = simd_max(max1); + + // get sum + float sum1 = 0; + for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { + sum1 += exp(float(axis_in[i * s.inside_size]) - float(max1)); + } + sum1 = simd_sum(sum1); + + // output + for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { + axis_out[i * s.inside_size] = ftype(exp(float(axis_in[i * s.inside_size]) - float(max1)) / sum1); + } +} + +)metal"; + #endif/* MNN_SUPPORT_TRANSFORMER_FUSE */ #endif diff --git a/source/backend/metal/MetalBackend.hpp b/source/backend/metal/MetalBackend.hpp index 1e6135a1c..643e2eb88 100644 --- a/source/backend/metal/MetalBackend.hpp +++ b/source/backend/metal/MetalBackend.hpp @@ -152,6 +152,8 @@ class MetalBackend : public Backend { */ static void addCreator(OpType type, Creator *creator); static void setTensor(const MNN::Tensor* tensor, id encoder, int index); + static void setMem(const MemChunk& chunk, id encoder, int index); + static uint8_t* getMemPtr(const MemChunk& chunk); static std::pair, int> getBuffer(const MNN::Tensor* tensor); size_t getTensorSizeInBytes(const Tensor* tensor) const; virtual bool onSelectDynamicAllocator(int index, int maxIndex) override; diff --git a/source/backend/metal/MetalBackend.mm b/source/backend/metal/MetalBackend.mm index 649c75b34..f4f62dc2c 100644 --- a/source/backend/metal/MetalBackend.mm +++ b/source/backend/metal/MetalBackend.mm @@ -252,9 +252,12 @@ MemChunk chunk() override { } } void MetalBackend::_resetDynamicMemory() const { - mCurrentAllocator->apply(); + mRuntime->pCurrentStatus = mCurrentAllocator->apply(); + if (NO_ERROR != mRuntime->pCurrentStatus) { + return; + } if (nullptr != mBufferPoolShapeImmutable.get()) { - mBufferPoolShapeImmutable->apply(); + mRuntime->pCurrentStatus = mBufferPoolShapeImmutable->apply(); } } @@ -801,6 +804,13 @@ static void _execute(id encoder, const MetalBackend::C void MetalBackend::setTensor(const MNN::Tensor* tensor, id encoder, int index) { [encoder setBuffer:((MetalRuntimeAllocator::MetalBufferAlloc *)tensor->deviceId())->getBuffer() offset:TensorUtils::getDescribe(tensor)->extra.offset atIndex:index]; } +void MetalBackend::setMem(const MemChunk& chunk, id encoder, int index) { + [encoder setBuffer:((MetalRuntimeAllocator::MetalBufferAlloc *)chunk.first)->getBuffer() offset:chunk.second atIndex:index]; +} +uint8_t* MetalBackend::getMemPtr(const MemChunk& chunk) { + return (uint8_t*)((MetalRuntimeAllocator::MetalBufferAlloc *)chunk.first)->getBuffer().contents + chunk.second; +} + std::pair, int> MetalBackend::getBuffer(const MNN::Tensor* tensor) { return std::make_pair(((MetalRuntimeAllocator::MetalBufferAlloc *)tensor->deviceId())->getBuffer(), TensorUtils::getDescribe(tensor)->extra.offset); } @@ -866,7 +876,11 @@ static void _execute(id encoder, const MetalBackend::C auto ctx = (__bridge MNNMetalContext *)context(); auto source = [[NSString alloc] initWithUTF8String:csource]; auto name = [[NSString alloc] initWithUTF8String:cname]; - return [ctx pipelineWithSourceOption:source name:name options:options]; + auto pipeline = [ctx pipelineWithSourceOption:source name:name options:options]; + if (nil == pipeline) { + mRuntime->pCurrentStatus = NOT_SUPPORT; + } + return pipeline; } void MetalRuntime::setCommandQueue(id queue, bool userSync) { mQueue = queue; @@ -880,7 +894,9 @@ static void _execute(id encoder, const MetalBackend::C return iter->second; } void MetalRuntime::insertPipeline(const std::vector& keys, id pipeline) const { - mCachePipeine.insert(std::make_pair(keys, pipeline)); + if (nil != pipeline) { + mCachePipeine.insert(std::make_pair(keys, pipeline)); + } } void MetalRuntime::setGpuMode(const int mode_num) { diff --git a/source/backend/metal/MetalConvolution.mm b/source/backend/metal/MetalConvolution.mm index d9dabb4bf..6ecc8f12d 100755 --- a/source/backend/metal/MetalConvolution.mm +++ b/source/backend/metal/MetalConvolution.mm @@ -211,6 +211,7 @@ return new MetalConvolutionWinograd(backend, op); } if (MetalConvolution1x1::isValid(conv, input)) { + return new MetalConvolution1x1(backend, op); } } diff --git a/source/backend/metal/MetalConvolution1x1.mm b/source/backend/metal/MetalConvolution1x1.mm index 7bdf0a893..c70bf1987 100644 --- a/source/backend/metal/MetalConvolution1x1.mm +++ b/source/backend/metal/MetalConvolution1x1.mm @@ -9,6 +9,7 @@ #import "backend/metal/MetalConvolution1x1.hpp" #import "core/Macro.h" #import "backend/metal/MetalBackend.hpp" +#import "ConvSimdGroupShader.hpp" #if MNN_METAL_ENABLED @@ -96,6 +97,25 @@ param->activation = mActivationType; param->scale_coef = mScaleCoef; + // basic marco info + std::string ftype2 = "float2"; + std::string ftype4 = "float4"; + std::string ftype4x4 = "float4x4"; + if (backend->useFp16InsteadFp32()) { + ftype2 = "half2"; + ftype4 = "half4"; + ftype4x4 = "half4x4"; + } + + MTLCompileOptions *option = [[MTLCompileOptions alloc] init]; + auto dic = [NSMutableDictionary dictionaryWithCapacity:0]; + option.preprocessorMacros = @{ + @"ftype2" : @(ftype2.c_str()), + @"ftype4" : @(ftype4.c_str()), + @"ftype4x4" : @(ftype4x4.c_str()), + @"MNN_METAL_FLOAT32_COMPUTER": @"1" + }; + MetalRuntime* rt = (MetalRuntime *)backend->runtime(); if (mDequantScaleBias.get()) { NSUInteger gid_x = UP_DIV(ow * oh, 4); @@ -103,36 +123,81 @@ NSUInteger gid_z = ob; std::string name = "conv1x1_g1z4_w8"; mPipeline = [context pipelineWithName:@"conv1x1_g1z4_w8" fp16:backend->useFp16InsteadFp32()]; + if (mDequantBits == 4) { if(rt->supportSimdGroupReduce() && ob * ow * oh == 1) { + std::vector baseKeys = {"conv1x1_w4_sg_reduce", ftype4, "MNN_METAL_FLOAT32_COMPUTER"}; + // unrool c for avoid memory exceed if(oc > 16384 && oc_4 % 2 == 0) { - mPipeline = [context pipelineWithName:@"conv1x1_gemv_g16_w4_sg" fp16:backend->useFp16InsteadFp32()]; - -// MNN_PRINT("g16 ic: %d oc: %d\n", input->channel(), oc); + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemv_g16_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgReduce, "conv1x1_gemv_g16_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(oc, 16), 1, 1), MTLSizeMake(64, 1, 1)); } else { - mPipeline = [context pipelineWithName:@"conv1x1_gemv_g8_w4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemv_g8_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgReduce, "conv1x1_gemv_g8_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; // MNN_PRINT("g8 ic: %d oc: %d\n", input->channel(), oc); mThreads = std::make_pair(MTLSizeMake(UP_DIV(oc, 8), 1, 1), MTLSizeMake(64, 1, 1)); } return NO_ERROR; } else if(rt->supportSimdGroupMatrix() && ob * ow * oh > 8 && oc > 8 && ic_4 % 8 == 0) { + std::vector baseKeys = {"conv1x1_w4_sg_matrix", ftype4, "MNN_METAL_FLOAT32_COMPUTER"}; + // Generally threadgroup memory >= 16KB auto smem_size = [[context device] maxThreadgroupMemoryLength]; // choose different tile for different computation if(ob * ow * oh >= 128 && oc >= 512 && ob * ow * oh * oc > 512 * 2048 && smem_size >= 8192) { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_32x64_w4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_32x64_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgMatrix, "conv1x1_gemm_32x64_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 32), UP_DIV(oc, 64), 1), MTLSizeMake(128, 1, 1)); } else if(ob * ow * oh >= 32 && ob * ow * oh * oc > 128 * 2048) { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_32x16_w4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_32x16_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgMatrix, "conv1x1_gemm_32x16_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 32), UP_DIV(oc, 16), 1), MTLSizeMake(32, 1, 1)); } else if(oc > 512 && ob * ow * oh * oc > 128 * 2048) { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_16x32_w4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_16x32_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgMatrix, "conv1x1_gemm_16x32_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 16), UP_DIV(oc, 32), 1), MTLSizeMake(32, 1, 1)); } else { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_16x16_w4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_16x16_w4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1W4SgMatrix, "conv1x1_gemm_16x16_w4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; // MNN_PRINT("gemm M: %d N: %d\n", ob * ow * oh, oc); mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 16), UP_DIV(oc, 16), 1), MTLSizeMake(32, 1, 1)); } @@ -166,24 +231,47 @@ } if(rt->supportSimdGroupMatrix()) { + std::vector baseKeys = {"conv1x1_float_sg_matrix", ftype4, "MNN_METAL_FLOAT32_COMPUTER"}; // total computation not too small if(ob * ow * oh >= 16 && ic_4 >= 4 && ic_4 % 2 == 0 && oc_4 >= 4 && ob * ow * oh * ic_4 * oc_4 >= 64 * 64 * 64) { // Enough threads if(ob * ow * oh * oc_4 / ic_4 >= 1024) { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_32x16_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_32x16_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1SgMatrix, "conv1x1_gemm_32x16_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 32), UP_DIV(oc, 16), 1), MTLSizeMake(32, 1, 1)); } else { - mPipeline = [context pipelineWithName:@"conv1x1_gemm_16x16_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_gemm_16x16_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1SgMatrix, "conv1x1_gemm_16x16_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(ob * ow * oh, 16), UP_DIV(oc, 16), 1), MTLSizeMake(32, 1, 1)); } return NO_ERROR; } } if(rt->supportSimdGroupReduce()) { + std::vector baseKeys = {"conv1x1_float_sg_reduce", ftype4, "MNN_METAL_FLOAT32_COMPUTER"}; // do input_channel reduce auto magic_num = 4.0; // total threads pretty small and loop pretty large if(ic_4 >= 32 && ic_4 % 2 == 0 && 1.0 * ob * ow * oh * oc_4 / ic_4 < magic_num) { - mPipeline = [context pipelineWithName:@"conv1x1_z4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("conv1x1_z4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gConv1x1SgReduce, "conv1x1_z4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(ow * oh, oc_4, ob), MTLSizeMake(32, 1, 1)); return NO_ERROR; } diff --git a/source/backend/metal/MetalLayerNorm.mm b/source/backend/metal/MetalLayerNorm.mm index 25591d5b9..8c454bd22 100755 --- a/source/backend/metal/MetalLayerNorm.mm +++ b/source/backend/metal/MetalLayerNorm.mm @@ -9,6 +9,7 @@ #import "backend/metal/MetalLayerNorm.hpp" #import "backend/metal/MNNMetalContext.h" #import "backend/metal/MetalBackend.hpp" +#import "LayerNormSimdGroupShader.hpp" #if MNN_METAL_ENABLED namespace MNN { @@ -77,26 +78,78 @@ bool parallel = (mInside > 32) && ((mInside & 3) == 0); auto inside = parallel ? mInside/4 : mInside; - if(((MetalRuntime *)backend->runtime())->supportSimdGroupReduce()) { + auto rt = (MetalRuntime *)backend->runtime(); + if(rt->supportSimdGroupReduce()) { + // basic marco info + std::string ftype = "float"; + std::string ftype4 = "float4"; + if (backend->useFp16InsteadFp32()) { + ftype = "half"; + ftype4 = "half4"; + } + + MTLCompileOptions *option = [[MTLCompileOptions alloc] init]; + auto dic = [NSMutableDictionary dictionaryWithCapacity:0]; + option.preprocessorMacros = @{ + @"ftype" : @(ftype.c_str()), + @"ftype4" : @(ftype4.c_str()), + }; + std::vector baseKeys = {"layernorm_sg_reduce", ftype}; + if(RMSNorm) { if(parallel) { if(inside >= 16 && inside * mOutside >= 2048) { - mPipeline = [context pipelineWithName:@"layernorm_x16_rms_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("layernorm_x16_rms_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gLayerNormSgReduce, "layernorm_x16_rms_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(UP_DIV(inside, 4), mOutside, 1), MTLSizeMake(32, 1, 1)); } else { - mPipeline = [context pipelineWithName:@"layernorm_x4_rms_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("layernorm_x4_rms_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gLayerNormSgReduce, "layernorm_x4_rms_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(inside, mOutside, 1), MTLSizeMake(32, 1, 1)); } - } else { - mPipeline = [context pipelineWithName:@"layernorm_x1_rms_sg" fp16:backend->useFp16InsteadFp32()]; + } else { + auto keys = baseKeys; + keys.emplace_back("layernorm_x1_rms_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gLayerNormSgReduce, "layernorm_x1_rms_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(inside, mOutside, 1), MTLSizeMake(32, 1, 1)); } } else { if(parallel) { - mPipeline = [context pipelineWithName:@"layernorm_x4_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("layernorm_x4_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gLayerNormSgReduce, "layernorm_x4_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(inside, mOutside, 1), MTLSizeMake(32, 1, 1)); } else { - mPipeline = [context pipelineWithName:@"layernorm_x1_sg" fp16:backend->useFp16InsteadFp32()]; + auto keys = baseKeys; + keys.emplace_back("layernorm_x1_sg"); + auto pipeline = rt->findPipeline(keys); + if (nil == pipeline) { + pipeline = backend->makeComputePipelineWithSourceOption(gLayerNormSgReduce, "layernorm_x1_sg", option); + rt->insertPipeline(keys, pipeline); + } + mPipeline = pipeline; mThreads = std::make_pair(MTLSizeMake(inside, mOutside, 1), MTLSizeMake(32, 1, 1)); } } diff --git a/source/backend/metal/MetalPReLU.hpp b/source/backend/metal/MetalPReLU.hpp index 8f3145831..afa7bf4e2 100644 --- a/source/backend/metal/MetalPReLU.hpp +++ b/source/backend/metal/MetalPReLU.hpp @@ -11,17 +11,18 @@ #import "MetalExecution.hpp" #if MNN_METAL_ENABLED +#include "core/BufferAllocator.hpp" namespace MNN { class MetalPReLU : public MetalExecution { public: MetalPReLU(Backend *backend, const float *slope, int count); - virtual ~MetalPReLU() = default; + virtual ~MetalPReLU(); virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; private: - id mSlope; + MemChunk mSlope; id mShape; id mPipeline; std::pair mThreads; diff --git a/source/backend/metal/MetalPReLU.mm b/source/backend/metal/MetalPReLU.mm index 15fae170b..07d3081a7 100755 --- a/source/backend/metal/MetalPReLU.mm +++ b/source/backend/metal/MetalPReLU.mm @@ -16,15 +16,30 @@ MetalPReLU::MetalPReLU(Backend *backend, const float *slope, int count) : MetalExecution(backend) { auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); - mSlope = [context newDeviceBuffer:UP_DIV(count, 4) * 4 * sizeof(float) bytes:slope access:CPUWriteOnly]; + auto mtbn = static_cast(backend); + auto bufferAlloc = mtbn->getStaticBufferPool(); + mSlope = bufferAlloc->alloc(UP_DIV(count, 4) * 4 * sizeof(float)); + if (nullptr == mSlope.first) { + mValid = false; + return; + } + auto buffer = ((MetalRuntimeAllocator::MetalBufferAlloc*)mSlope.first)->getBuffer(); + auto ptr = MetalBackend::getMemPtr(mSlope); + ::memset(ptr, 0, UP_DIV(count, 4) * 4 * sizeof(float)); + ::memcpy(ptr, slope, count * sizeof(float)); mShareChannel = 1 == count; if (!mShareChannel) { mShape = [context newDeviceBuffer:3 * sizeof(int) access:CPUWriteOnly]; } - auto mtbn = static_cast(backend); mPipeline = [context pipelineWithName:mShareChannel ? @"prelu" : @"prelu_slopes" fp16:mtbn->useFp16InsteadFp32()]; } - +MetalPReLU::~MetalPReLU() { + if (nullptr != mSlope.first) { + auto mtbn = static_cast(backend()); + auto bufferAlloc = mtbn->getStaticBufferPool(); + bufferAlloc->free(mSlope); + } +} ErrorCode MetalPReLU::onResize(const std::vector &inputs, const std::vector &outputs) { auto backend = static_cast(this->backend()); auto context = (__bridge MNNMetalContext *)backend->context(); @@ -44,9 +59,9 @@ void MetalPReLU::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { auto input = inputs[0], output = outputs[0]; [encoder setComputePipelineState:mPipeline]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; - [encoder setBuffer:mSlope offset:0 atIndex:2]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(output, encoder, 1); + MetalBackend::setMem(mSlope, encoder, 2); if (!mShareChannel) { [encoder setBuffer:mShape offset:0 atIndex:3]; } diff --git a/source/backend/metal/MetalRaster.mm b/source/backend/metal/MetalRaster.mm index 7c6f67b1a..6c2a1e256 100644 --- a/source/backend/metal/MetalRaster.mm +++ b/source/backend/metal/MetalRaster.mm @@ -300,6 +300,9 @@ kernel void main0(device int4 *out [[buffer(0)]], auto outputDes = TensorUtils::getDescribe(output); auto des = outputDes; mNeedZero = !TensorUtils::regionIsFull(output); + if (outputDes->dimensionFormat == MNN_DATA_FORMAT_NC4HW4 && output->length(1) % 4 != 0) { + mNeedZero = true; + } auto context = (__bridge MNNMetalContext *)static_cast(backend())->context(); auto mtbn = static_cast(backend()); auto bufferAlloc = mtbn->getStaticBufferPool(); diff --git a/source/backend/metal/MetalScale.hpp b/source/backend/metal/MetalScale.hpp index a4d00b330..2ffb80554 100644 --- a/source/backend/metal/MetalScale.hpp +++ b/source/backend/metal/MetalScale.hpp @@ -13,18 +13,19 @@ #import "MNN_generated.h" #if MNN_METAL_ENABLED +#include "core/BufferAllocator.hpp" namespace MNN { class MetalScale : public MetalExecution { public: MetalScale(Backend *backend, const Scale *scale); - virtual ~MetalScale() = default; + virtual ~MetalScale(); virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; virtual void onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) override; private: - id mScale; - id mBias; + MemChunk mScaleBias; + size_t mBiasOffset = 0; id mConst; id mPipeline; std::pair mThreads; diff --git a/source/backend/metal/MetalScale.mm b/source/backend/metal/MetalScale.mm index 4dc1fe5d4..8c377e14c 100755 --- a/source/backend/metal/MetalScale.mm +++ b/source/backend/metal/MetalScale.mm @@ -15,16 +15,33 @@ namespace MNN { MetalScale::MetalScale(Backend *backend, const Scale *scale) : MetalExecution(backend) { - auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); auto mtbn = static_cast(backend); + auto bufferAlloc = mtbn->getStaticBufferPool(); + auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); auto channel4 = UP_DIV(scale->channels(), 4) * 4; - mScale = [context newDeviceBuffer:channel4 * sizeof(float) bytes:scale->scaleData()->data() access:CPUWriteOnly]; - mBias = scale->biasData() - ? [context newDeviceBuffer:channel4 * sizeof(float) bytes:scale->biasData()->data() access:CPUWriteOnly] - : [context newDeviceBuffer:channel4 * sizeof(float) access:CPUTransparent]; + mBiasOffset = channel4 / 4; + mScaleBias = bufferAlloc->alloc(2 * channel4 * sizeof(float)); + if (mScaleBias.first == nullptr) { + mValid = false; + return; + } + auto scalePtr = MetalBackend::getMemPtr(mScaleBias); + ::memset(scalePtr, 0, 2 * channel4 * sizeof(float)); + ::memcpy(scalePtr, scale->scaleData()->data(), scale->channels() * sizeof(float)); + auto biasPtr = scalePtr + channel4 * sizeof(float); + if (nullptr != scale->biasData()) { + ::memcpy(biasPtr, scale->biasData()->data(), scale->channels() * sizeof(float)); + } mConst = [context newDeviceBuffer:4 * sizeof(int) access:CPUWriteOnly]; mPipeline = [context pipelineWithName:@"scale_ca" fp16:mtbn->useFp16InsteadFp32()]; } +MetalScale::~MetalScale() { + auto mtbn = static_cast(backend()); + auto bufferAlloc = mtbn->getStaticBufferPool(); + if (nullptr != mScaleBias.first) { + bufferAlloc->free(mScaleBias); + } +} ErrorCode MetalScale::onResize(const std::vector &inputs, const std::vector &outputs) { auto backend = static_cast(this->backend()); @@ -39,6 +56,7 @@ ((int *)mConst.contents)[0] = w*h; ((int *)mConst.contents)[1] = z; ((int *)mConst.contents)[2] = output->batch(); + ((int *)mConst.contents)[3] = mBiasOffset; mThreads = [context computeBestGroupAndLocal:mPipeline threads:MTLSizeMake(w*h, z * outputs[0]->batch(), 1)]; return NO_ERROR; } @@ -46,11 +64,10 @@ void MetalScale::onEncode(const std::vector &inputs, const std::vector &outputs, id encoder) { auto input = inputs[0], output = outputs[0]; [encoder setComputePipelineState:mPipeline]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; - [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; + MetalBackend::setTensor(input, encoder, 0); + MetalBackend::setTensor(output, encoder, 1); [encoder setBuffer:mConst offset:0 atIndex:2]; - [encoder setBuffer:mScale offset:0 atIndex:3]; - [encoder setBuffer:mBias offset:0 atIndex:4]; + MetalBackend::setMem(mScaleBias, encoder, 3); [encoder dispatchThreadgroups:mThreads.first threadsPerThreadgroup:mThreads.second]; } diff --git a/source/backend/metal/MetalUnary.mm b/source/backend/metal/MetalUnary.mm index 83766fe92..f4ce7655d 100755 --- a/source/backend/metal/MetalUnary.mm +++ b/source/backend/metal/MetalUnary.mm @@ -14,7 +14,7 @@ #if MNN_METAL_ENABLED namespace MNN { -static const char* gBinaryTemplate = R"metal( +static const char* gUnaryTemplate = R"metal( #include #include using namespace metal; @@ -162,7 +162,7 @@ kernel void main0(const device T *in [[buffer(0)]], \ @"T" : T, @"FUNC" : kernel, }; - pipeline = mtbn->makeComputePipelineWithSourceOption(gBinaryTemplate, "main0", compileOptions); + pipeline = mtbn->makeComputePipelineWithSourceOption(gUnaryTemplate, "main0", compileOptions); mtbn->runtime()->insertPipeline(keys, pipeline); } if (nil == pipeline) { diff --git a/source/backend/metal/shader/MetalConvolution.metal b/source/backend/metal/shader/MetalConvolution.metal index f4302184c..fcce1f789 100644 --- a/source/backend/metal/shader/MetalConvolution.metal +++ b/source/backend/metal/shader/MetalConvolution.metal @@ -222,7 +222,7 @@ kernel void conv_s1d1p0_w2(const device ftype4 *in [[buffer(0)]], wt4 = z_wt[z * cst.kernel_size + y * cst.kernel_x + x]; result0 += FLOAT4(in4_0 * wt4); } - in4_0 = z_in[z * cst.input_size + y * cst.input_width + cst.kernel_x]; + in4_0 = z_in[z * cst.input_size * cst.batch + y * cst.input_width + cst.kernel_x]; result1 += FLOAT4(in4_0 * wt4); } } @@ -336,16 +336,16 @@ kernel void conv_z4(const device ftype4 *in [[buffer(0)]], auto x_wt = z_wt + y * cst.kernel_x + x; auto in4 = z_in[ y * dilation_h + x * cst.dilation_x]; /* true */ result0 += FLOAT4(in4 * *x_wt); - if (valids[0]) { x_wt += ws; result1 += FLOAT4(in4 * *x_wt); } - if (valids[1]) { x_wt += ws; result2 += FLOAT4(in4 * *x_wt); } - if (valids[2]) { x_wt += ws; result3 += FLOAT4(in4 * *x_wt); } + if (valids.x) { x_wt += ws; result1 += FLOAT4(in4 * *x_wt); } + if (valids.y) { x_wt += ws; result2 += FLOAT4(in4 * *x_wt); } + if (valids.z) { x_wt += ws; result3 += FLOAT4(in4 * *x_wt); } } } } /* true */ *z_out = activate(ftype4(result0 + FLOAT4(biasTerms[uz[0]])), cst.activation); - if (valids[0]) { z_out += cst.output_size; *z_out = activate(ftype4(result1 + FLOAT4(biasTerms[uz[1]])), cst.activation); } - if (valids[1]) { z_out += cst.output_size; *z_out = activate(ftype4(result2 + FLOAT4(biasTerms[uz[2]])), cst.activation); } - if (valids[2]) { z_out += cst.output_size; *z_out = activate(ftype4(result3 + FLOAT4(biasTerms[uz[3]])), cst.activation); } + if (valids.x) { z_out += cst.output_size * cst.batch; *z_out = activate(ftype4(result1 + FLOAT4(biasTerms[uz[1]])), cst.activation); } + if (valids.y) { z_out += cst.output_size * cst.batch; *z_out = activate(ftype4(result2 + FLOAT4(biasTerms[uz[2]])), cst.activation); } + if (valids.z) { z_out += cst.output_size * cst.batch; *z_out = activate(ftype4(result3 + FLOAT4(biasTerms[uz[3]])), cst.activation); } } diff --git a/source/backend/metal/shader/MetalConvolution1x1.metal b/source/backend/metal/shader/MetalConvolution1x1.metal index 090b57415..37b1996be 100644 --- a/source/backend/metal/shader/MetalConvolution1x1.metal +++ b/source/backend/metal/shader/MetalConvolution1x1.metal @@ -1,26 +1,6 @@ #define CONV_UNROLL (4) #define CONV_UNROLL_L (8) -#define INIT_SIMDGROUP_MATRIX(a, b, d) \ - simdgroup_T8x8 sga[a];\ - simdgroup_T8x8 sgb[b];\ - simdgroup_T8x8 sgd[d];\ - for (int i = 0; i < d; i++){\ - sgd[i] = make_filled_simdgroup_matrix(0.f);\ - } - -#define SIMDGROUP_MATRIX_FMA(a, b) \ - for(int j=0; j 3) {xy_out[3] = activate(ftype4(result3), cst.activation); } } -kernel void conv1x1_z4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4x4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - if ((int)gid.x >= cst.output_size || (int)gid.y >= cst.output_slice || (int)gid.z >= cst.batch) return; - - int rx = gid.x; - int uz = gid.y; - auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; - auto biasValue = FLOAT4(biasTerms[uz]); - FLOAT4 result0 = 0; - int computeSize = min(cst.output_size - rx, CONV_UNROLL); - - for (auto z = tiisg; z < cst.input_slice; z+SIMD_GROUP_WIDTH) { - auto in40 = *xy_in0; - auto w = xy_wt[z]; - - result0 += FLOAT4(in40 * w); - xy_in0 += cst.input_size * cst.batch; - } - result0 = simd_sum(result0); - - *xy_out = activate(ftype4(result0 + biasValue), cst.activation); -} - kernel void conv1x1_g1z4_w8(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], @@ -154,445 +103,6 @@ kernel void conv1x1_g1z4_w8(const device ftype4 *in [[buffer(0)]], } -kernel void conv1x1_gemm_16x16_w4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device uchar2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~127 ---> input: [M16, K8] - ftype 128~255 ---> input: [K8, N16] - // Write: - ftype 0~255 ---> input: [N2, M2, M8, N8] - */ - threadgroup FLOAT4 sdata[256] = {0.f}; - - INIT_SIMDGROUP_MATRIX(2, 2, 4); - - int rx = gid.x;// M/16 - int uz = gid.y;// N/16 - - int kl = tiitg / 16; - int rcl = tiitg % 16; -// int kl = tiitg % 2; -// int rcl = tiitg / 2; - - // boundary limit - int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); - int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); - - auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] - auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] - auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N/4, M, N4] - - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - for (int bi=0; bi> 4) - 8, (float)(w_int40[0] & 15) - 8, (float)(w_int40[1] >> 4) - 8, (float)(w_int40[1] & 15) - 8); - - FLOAT4 res = w40 * scale[rcl % 4] + dequant_bias[rcl % 4]; - ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 0) * 16 + rcl] = res[0]; - ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 1) * 16 + rcl] = res[1]; - ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 2) * 16 + rcl] = res[2]; - ((threadgroup FLOAT*)sdata)[256 * i + 128 + (kl * 4 + 3) * 16 + rcl] = res[3]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - - #pragma unroll(4) - for(int i = 0; i < 4; i++) { - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata + 256*i, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64 + 256*i, 8); - simdgroup_barrier(mem_flags::mem_none); - - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128 + 256*i, 16); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136 + 256*i, 16); - - SIMDGROUP_MATRIX_FMA(2, 2); - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - } - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 4); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if((16 * rx + rcl) < cst.input_size * cst.batch) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } -} - -kernel void conv1x1_gemm_32x16_w4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device uchar2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~255 ---> input: [M32, K8] - ftype 256~383 ---> input: [K8, N16] - // Write: - ftype 0~511 ---> input: [N2, M4, M8, N8] - */ - threadgroup FLOAT4 sdata[128] = {0.f}; - - INIT_SIMDGROUP_MATRIX(4, 2, 8); - - int rx = gid.x;// M/32 - int uz = gid.y;// N/16 - - int kl = tiitg % 2; - int rcl = tiitg / 2; - - const int size_m = cst.input_size * cst.batch; - - // boundary limit - int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); - int idx_m0 = (16 * rx + rcl) < size_m ? (16 * rx + rcl) : (size_m - 1); - int idx_m1 = (16 * rx + rcl) + size_m / 2 < size_m ? (16 * rx + rcl) + size_m / 2: (size_m - 1); - - auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] - auto xy_in0 = in + idx_m0 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] - auto xy_in1 = in + idx_m1 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] - - auto xy_out0 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m0;// [N/4, M, N4] - auto xy_out1 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m1;// [N/4, M, N4] - - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); - FLOAT4 res = w4 * scale[rcl % 4] + dequant_bias[rcl % 4]; - // sdata[32 + 2* rcl + kl] = res; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 0) * 16 + rcl] = res[0]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 1) * 16 + rcl] = res[1]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 2) * 16 + rcl] = res[2]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 3) * 16 + rcl] = res[3]; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); - simdgroup_load(sga[2], ((const threadgroup FLOAT*)sdata) + 128, 8); - simdgroup_load(sga[3], ((const threadgroup FLOAT*)sdata) + 192, 8); - - // simdgroup_load(sgb[0], (const threadgroup FLOAT*)sdata + 128, 8, 0, true); - // simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 192, 8, 0, true); - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 256, 16); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 264, 16); - - SIMDGROUP_MATRIX_FMA(4, 2); - threadgroup_barrier(mem_flags::mem_threadgroup); - - xy_in0 += 2 * cst.input_size * cst.batch; - xy_in1 += 2 * cst.input_size * cst.batch; - - } - } - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if((16 * rx + rcl) < size_m) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out0[0] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out0[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } - if((16 * rx + rcl) + size_m / 2 < size_m) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out1[0] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out1[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } -} - -kernel void conv1x1_gemm_16x32_w4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device uchar2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~127 ---> input: [M16, K8] - ftype 128~383 ---> input: [K8, N32] - // Write: - ftype 0~511 ---> input: [N2, N2, M2, M8, N8] - */ - threadgroup FLOAT4 sdata[128] = {0.f}; - - INIT_SIMDGROUP_MATRIX(2, 4, 8); - - int rx = gid.x;// M/16 - int uz = gid.y;// N/32 - - int kl = tiitg % 2; - int rcl = tiitg / 2; - - // boundary limit - int idx_n40 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); - int idx_n41 = (4 * uz + rcl / 4) + cst.output_slice / 2 < cst.output_slice ? (4 * uz + rcl / 4) + cst.output_slice / 2 : (cst.output_slice - 1); - - int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); - - auto xy_wt0 = wt + (idx_n40 * cst.input_slice + 0) * 4 + (rcl % 4);// [N2, N/8, K/4, N4, K4] - auto xy_wt1 = wt + (idx_n41 * cst.input_slice + 0) * 4 + (rcl % 4);// [N2, N/8, K/4, N4, K4] - - auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] - auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N2, N/8, M, N4] - - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); - FLOAT4 res = w4 * scale0[rcl % 4] + dequant_bias0[rcl % 4]; - // sdata[32 + 2* rcl + kl] = res; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 32 + rcl] = res[0]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 32 + rcl] = res[1]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 32 + rcl] = res[2]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 32 + rcl] = res[3]; - } - { - uchar2 w_int4 = xy_wt1[4*z]; // [N/4, K/4, N4, K4] - FLOAT4 w4 = FLOAT4((float)(w_int4[0] >> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); - FLOAT4 res = w4 * scale1[rcl % 4] + dequant_bias1[rcl % 4]; - // sdata[32 + 2* rcl + kl] = res; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 32 + 16 + rcl] = res[0]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 32 + 16 + rcl] = res[1]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 32 + 16 + rcl] = res[2]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 32 + 16 + rcl] = res[3]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); - - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128, 32); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136, 32); - simdgroup_load(sgb[2], ((const threadgroup FLOAT*)sdata) + 144, 32); - simdgroup_load(sgb[3], ((const threadgroup FLOAT*)sdata) + 152, 32); - - SIMDGROUP_MATRIX_FMA(2, 4); - threadgroup_barrier(mem_flags::mem_threadgroup); - - xy_in0 += 2 * cst.input_size * cst.batch; - - } - } - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if((16 * rx + rcl) < cst.input_size * cst.batch) { - if(4 * uz + 2 * kl < cst.output_slice) { - xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl])), cst.activation); - } - if(4 * uz + 2 * kl + 1 < cst.output_slice) { - xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - if(cst.output_slice / 2 + 4 * uz + 2 * kl < cst.output_slice) { - xy_out[cst.output_slice / 2 * cst.output_size * cst.batch] = activate(ftype4(sdata[((kl + 2) * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[cst.output_slice / 2 + 4 * uz + 2 * kl])), cst.activation); - } - if(cst.output_slice / 2 + 4 * uz + 2 * kl + 1 < cst.output_slice) { - xy_out[(cst.output_slice / 2 + 1) * cst.output_size * cst.batch] = activate(ftype4(sdata[((kl + 2) * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[cst.output_slice / 2 + 4 * uz + 2 * kl + 1])), cst.activation); - } - } -} - - -kernel void conv1x1_gemm_32x64_w4_sg(const device ftype2 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device uchar2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~255 ---> input: [M32, K8] - ftype 256~767 ---> weight: [K8, N64] - // Write: - ftype 0~2047 ---> input: [M2, N2, N2, N2, M2, M8, N8] - */ - threadgroup FLOAT4 sdata[512] = {0.f}; - - INIT_SIMDGROUP_MATRIX(2, 4, 8); - - int rx = gid.x;// M/32 - int uz = gid.y;// N/64 - - int kl = tiitg % 2;// 0~1 -> inner K - int rcl = tiitg / 2;// 0~63 - int ko = rcl % 2;// 0~1 -> outter K - int ml = rcl / 2;// 0~31 -> m - int ni = rcl % 4;// 0~3 -> inner N - int no = rcl / 4;// 0~15 -> outter N - - /** input: - threadgroup: [M32, K8] - each thread: K2 - layout: [K/4, M, K4] -> [K/8, K2, M/32, M32, K2, K2] - index : [K/8, ko, rx, ml, kl, K2] - */ - /** weight: - threadgroup: [K8, N64] - each thread: K4 - layout: [N/4, K/4, N4, K4] -> [N/64, N16, K/8, K2, N4, K4] - index : [uz, no, K/8, kl, ni, K4] - */ - /** output: - threadgroup: [M32, N64] - each thread: M4N4 - layout: [N/4, M, N4] -> [N/16, N4, M, N4] - index : [uz*4+(2*ko+kl), N4, idx_m, N4] - */ - - // boundary limit - - int idx_n40 = (uz * 16 + no) < cst.output_slice ? (uz * 16 + no) : (cst.output_slice - 1); - int idx_m = (rx * 32 + ml) < cst.input_size * cst.batch ? (rx * 32 + ml) : (cst.input_size * cst.batch - 1); - - auto xy_wt0 = wt + ((idx_n40 * cst.input_slice / 2 + 0) * 2 + kl) * 4 + ni;// [N/4, K/4, N4, K4] - - auto xy_in0 = in + ((0 * 2 + ko) * cst.input_size * cst.batch + idx_m) * 2 + kl;// [K/4, M, K2, K2] - auto xy_out = out + ((4 * uz + 2 * ko + kl) * 4 + 0) * cst.output_size * cst.batch + idx_m;// [N2, N/8, M, N4] - - const int idx_sa = ml * 8 + ko * 4 + kl * 2; - const int idx_sb = 256 + (kl * 4 + 0) * 64 + rcl; - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - for (int bi=0; bi> 4) - 8, (float)(w_int4[0] & 15) - 8, (float)(w_int4[1] >> 4) - 8, (float)(w_int4[1] & 15) - 8); - FLOAT4 res = w4 * scale0[ni] + dequant_bias0[ni]; - // sdata[32 + 2* rcl + kl] = res; - ((threadgroup FLOAT*)sdata)[idx_sb] = res[0]; - ((threadgroup FLOAT*)sdata)[idx_sb + 64] = res[1]; - ((threadgroup FLOAT*)sdata)[idx_sb + 128] = res[2]; - ((threadgroup FLOAT*)sdata)[idx_sb + 192] = res[3]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - - - const threadgroup FLOAT * sdata_a = (const threadgroup FLOAT*)sdata + 16*8*(sgitg/2); - const threadgroup FLOAT * sdata_b = (const threadgroup FLOAT*)sdata + 32*8 + 32*(sgitg%2); - - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata_a, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata_a) + 64, 8); - - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata_b) + 0, 64); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata_b) + 8, 64); - simdgroup_load(sgb[2], ((const threadgroup FLOAT*)sdata_b) + 16, 64); - simdgroup_load(sgb[3], ((const threadgroup FLOAT*)sdata_b) + 24, 64); - - SIMDGROUP_MATRIX_FMA(2, 4); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - xy_in0 += 4 * cst.input_size * cst.batch; - } - } - - threadgroup FLOAT * sdata_c = (threadgroup FLOAT*)sdata + 512*sgitg; - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata_c, 8); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // sdata [M2, N2, N2, N2, M2, M8, N8] - // index [ml/16, ko, kl, N2, (ml/8)%2, ml%8, N2, N4] - if((rx * 32 + ml) < cst.input_size * cst.batch) { - if((4 * uz + 2 * ko + kl) * 4 < cst.output_slice) { - xy_out[0] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 0) * 16 + ml % 16) * 2] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4])), cst.activation); - } - if((4 * uz + 2 * ko + kl) * 4 + 1 < cst.output_slice) { - xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 0) * 16 + ml % 16) * 2 + 1] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 1])), cst.activation); - } - if((4 * uz + 2 * ko + kl) * 4 + 2 < cst.output_slice) { - xy_out[cst.output_size * cst.batch * 2] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 1) * 16 + ml % 16) * 2] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 2])), cst.activation); - } - if((4 * uz + 2 * ko + kl) * 4 + 3 < cst.output_slice) { - xy_out[cst.output_size * cst.batch * 3] = activate(ftype4(sdata[(((ml/16 * 4 + 2 * ko + kl) * 2 + 1) * 16 + ml % 16) * 2 + 1] + FLOAT4(biasTerms[(4 * uz + 2 * ko + kl) * 4 + 3])), cst.activation); - } - } -} - kernel void conv1x1_g1z4_w4(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], @@ -648,144 +158,6 @@ kernel void conv1x1_g1z4_w4(const device ftype4 *in [[buffer(0)]], if (computeSize > 3) {xy_out[3] = activate(ftype4(result3), cst.activation); } } -kernel void conv1x1_gemv_g8_w4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device MNN::uchar4x2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - // each threadgroup contain 2 simdgroup - // each simdgroup compute 4 data - int uz = gid.x * 2 + sgitg; - if(uz >= cst.output_slice) { - return; - } - - int rx = gid.y; - auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in + (int)gid.z * cst.input_size + rx + 0; - auto xy_out = out + (int)gid.z * cst.output_size + uz * cst.output_size * cst.batch + rx; - auto biasValue = FLOAT4(biasTerms[uz]); - FLOAT4 result0 = FLOAT4(0); - - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - - int middle_step = min(SIMD_GROUP_WIDTH, block); - int outer_step = SIMD_GROUP_WIDTH / middle_step; - int middle_index = (tiisg) % middle_step; - int outer_index = (tiisg) / middle_step; - - for (int bi= outer_index; bi> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); - FLOAT4 res = w4 * scale[i] + dequant_bias[i]; - w_dequant[i] = res; - } - - result0 += FLOAT4(in40 * w_dequant); - - } - } - - FLOAT4 res = simd_sum(result0); - /* true */ - if (tiisg == 0) { - xy_out[0] = activate(ftype4(res + biasValue), cst.activation); - } -} - - - - -kernel void conv1x1_gemv_g16_w4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device MNN::uchar4x2 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - const device ftype4 *dequantScale [[buffer(5)]], - uint3 gid[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - // each threadgroup contain 2 simdgroup - // each simdgroup compute 8 data - int uz = 2 * (gid.x * 2 + sgitg); - if(uz >= cst.output_slice) { - return; - } - - auto xy_wt = wt + uz * cst.input_slice; - auto xy_in0 = in; - auto xy_out = out + (int)gid.z * cst.output_size + uz; - auto biasValue0 = FLOAT4(biasTerms[uz]); - auto biasValue1 = FLOAT4(biasTerms[uz + 1]); - - FLOAT4 result0 = FLOAT4(0); - FLOAT4 result1 = FLOAT4(0); - - int block = (cst.input_slice + cst.block_size - 1) / cst.block_size; - - int middle_step = min(SIMD_GROUP_WIDTH, block); - int outer_step = SIMD_GROUP_WIDTH / middle_step; - int middle_index = (tiisg) % middle_step; - int outer_index = (tiisg) / middle_step; - - for (int bi= outer_index; bi> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); - FLOAT4 res = w4 * scale0[i] + dequant_bias0[i]; - w_dequant[i] = res; - } - result0 += FLOAT4(in40 * w_dequant); - - w_int4 = xy_wt[cst.input_slice + z]; - for (int i = 0; i < 4; i += 1) { - FLOAT4 w4 = FLOAT4((float)(w_int4[i][0] >> 4) - 8, (float)(w_int4[i][0] & 15) - 8, (float)(w_int4[i][1] >> 4) - 8, (float)(w_int4[i][1] & 15) - 8); - FLOAT4 res = w4 * scale1[i] + dequant_bias1[i]; - w_dequant[i] = res; - } - - result1 += FLOAT4(in40 * w_dequant); - - } - } - - FLOAT4 res0 = simd_sum(result0); - FLOAT4 res1 = simd_sum(result1); - - /* true */ - if (tiisg == 0) { - xy_out[0] = activate(ftype4(res0 + biasValue0), cst.activation); - xy_out[1] = activate(ftype4(res1 + biasValue1), cst.activation); - - } -} - kernel void conv1x1_g1z8(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant conv1x1_constants& cst [[buffer(2)]], @@ -1034,159 +406,3 @@ kernel void conv1x1_w4c2(const device ftype4 *in [[buffer(0)]], if (widthSize > 3) {xy_out[cst.output_size * cst.batch +3] = activate(ftype4(result7), cst.activation); } } } - -kernel void conv1x1_gemm_16x16_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~127 ---> input: [M16, K8] - ftype 128~255 ---> input: [K8, N16] - // Write: - ftype 0~255 ---> input: [N2, M2, M8, N8] - */ - threadgroup FLOAT4 sdata[64] = {0.f}; - - INIT_SIMDGROUP_MATRIX(2, 2, 4); - int rx = gid.x;// M/16 - int uz = gid.y;// N/16 - - int kl = tiitg / 16; - int rcl = tiitg % 16; - - // boundary limit - int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); - int idx_m = (16 * rx + rcl) < cst.input_size * cst.batch ? (16 * rx + rcl) : (cst.input_size * cst.batch - 1); - - auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] - auto xy_in0 = in + idx_m + cst.input_size * cst.batch * kl;// [K/4, M, K4] - auto xy_out = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m;// [N/4, M, N4] - - for (int z = kl; z < cst.input_slice; z += 2) { - sdata[2* rcl + kl] = FLOAT4(*xy_in0); - xy_in0 += 2 * cst.input_size * cst.batch; - - FLOAT4 w4 = FLOAT4(xy_wt[4 * z]); // [N/4, K/4, N4, K4] - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 0) * 16 + rcl] = w4[0]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 1) * 16 + rcl] = w4[1]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 2) * 16 + rcl] = w4[2]; - ((threadgroup FLOAT*)sdata)[128 + (kl * 4 + 3) * 16 + rcl] = w4[3]; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 128, 16); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 136, 16); - - SIMDGROUP_MATRIX_FMA(2, 2); - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 4); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if((16 * rx + rcl) < cst.input_size * cst.batch) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out[0] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } -} - - -kernel void conv1x1_gemm_32x16_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant conv1x1_constants& cst [[buffer(2)]], - const device ftype4 *wt [[buffer(3)]], - const device ftype4 *biasTerms [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - /* - // Read: - ftype 0~255 ---> input: [M32, K8] - ftype 256~383 ---> input: [K8, N16] - // Write: - ftype 0~511 ---> input: [N2, M4, M8, N8] - */ - threadgroup FLOAT4 sdata[128] = {0.f}; - - INIT_SIMDGROUP_MATRIX(4, 2, 8); - - int rx = gid.x;// M/32 - int uz = gid.y;// N/16 - - int kl = tiitg % 2; - int rcl = tiitg / 2; - - const int size_m = cst.input_size * cst.batch; - - // boundary limit - int idx_n4 = (4 * uz + rcl / 4) < cst.output_slice ? (4 * uz + rcl / 4) : (cst.output_slice - 1); - int idx_m0 = (16 * rx + rcl) < size_m ? (16 * rx + rcl) : (size_m - 1); - int idx_m1 = (16 * rx + rcl) + size_m / 2 < size_m ? (16 * rx + rcl) + size_m / 2: (size_m - 1); - - auto xy_wt = wt + (idx_n4 * cst.input_slice + 0) * 4 + rcl % 4;// [N/4, K/4, N4, K4] - auto xy_in0 = in + idx_m0 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] - auto xy_in1 = in + idx_m1 + cst.input_size * cst.batch * kl;// [K/4, M2, M/2, K4] - - auto xy_out0 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m0;// [N/4, M, N4] - auto xy_out1 = out + (4 * uz + 2 * kl) * cst.output_size * cst.batch + idx_m1;// [N/4, M, N4] - - for (int z = kl; z < cst.input_slice; z += 2) { - sdata[2* rcl + kl] = (FLOAT4)*xy_in0; - sdata[32 + 2* rcl + kl] = (FLOAT4)*xy_in1; - - FLOAT4 w4 = FLOAT4(xy_wt[4*z]); // [N/4, K/4, N4, K4] - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 0) * 16 + rcl] = w4[0]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 1) * 16 + rcl] = w4[1]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 2) * 16 + rcl] = w4[2]; - ((threadgroup FLOAT*)sdata)[256 + (kl * 4 + 3) * 16 + rcl] = w4[3]; - threadgroup_barrier(mem_flags::mem_threadgroup); - - simdgroup_load(sga[0], (const threadgroup FLOAT*)sdata, 8); - simdgroup_load(sga[1], ((const threadgroup FLOAT*)sdata) + 64, 8); - simdgroup_load(sga[2], ((const threadgroup FLOAT*)sdata) + 128, 8); - simdgroup_load(sga[3], ((const threadgroup FLOAT*)sdata) + 192, 8); - - simdgroup_load(sgb[0], ((const threadgroup FLOAT*)sdata) + 256, 16); - simdgroup_load(sgb[1], ((const threadgroup FLOAT*)sdata) + 264, 16); - - SIMDGROUP_MATRIX_FMA(4, 2); - threadgroup_barrier(mem_flags::mem_threadgroup); - - xy_in0 += 2 * cst.input_size * cst.batch; - xy_in1 += 2 * cst.input_size * cst.batch; - - } - - SIMDGROUP_MATRIX_STORE((threadgroup FLOAT*)sdata, 8); - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if((16 * rx + rcl) < size_m) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out0[0] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out0[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } - if((16 * rx + rcl) + size_m / 2 < size_m) { - if((4 * uz + 2 * kl) < cst.output_slice) { - xy_out1[0] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 0] + FLOAT4(biasTerms[4 * uz + 2 * kl + 0])), cst.activation); - } - if((4 * uz + 2 * kl + 1) < cst.output_slice) { - xy_out1[cst.output_size * cst.batch] = activate(ftype4(sdata[(kl * 32 + 16 + rcl) * 2 + 1] + FLOAT4(biasTerms[4 * uz + 2 * kl + 1])), cst.activation); - } - } -} diff --git a/source/backend/metal/shader/MetalDefine.metal b/source/backend/metal/shader/MetalDefine.metal index 353aba06e..bcf7aa462 100644 --- a/source/backend/metal/shader/MetalDefine.metal +++ b/source/backend/metal/shader/MetalDefine.metal @@ -5,7 +5,6 @@ using namespace metal; // Macro // ––––––––––––––––––––––––––––––––––––––––––––––––––– -#define SIMD_GROUP_WIDTH 32 // setting SIMD group size is 32 #define UP_DIV(x, y) ( ((x) + (y) - 1) / (y) ) #define ROUND_UP(x, y) ( ((x) + (y) - 1) / (y) * (y) ) @@ -56,7 +55,6 @@ typedef float3x4 FLOAT3x4; typedef float4x2 FLOAT4x2; typedef float4x3 FLOAT4x3; typedef float4x4 FLOAT4x4; -typedef simdgroup_float8x8 simdgroup_T8x8; #else typedef half FLOAT; typedef half2 FLOAT2; @@ -71,7 +69,6 @@ typedef half3x4 FLOAT3x4; typedef half4x2 FLOAT4x2; typedef half4x3 FLOAT4x3; typedef half4x4 FLOAT4x4; -typedef simdgroup_half8x8 simdgroup_T8x8; #endif namespace MNN { diff --git a/source/backend/metal/shader/MetalLayerNorm.metal b/source/backend/metal/shader/MetalLayerNorm.metal index 5ed361a5b..119e6ac06 100644 --- a/source/backend/metal/shader/MetalLayerNorm.metal +++ b/source/backend/metal/shader/MetalLayerNorm.metal @@ -40,48 +40,6 @@ kernel void layernorm_x1(const device ftype *in [[buffer(0)]], } } -kernel void layernorm_x1_sg(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - constant layernorm_constants& cst [[buffer(2)]], - const device float *gamma [[buffer(3)]], - const device float *beta [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - if ((int)gid.x >= cst.inside || (int)gid.y >= cst.outside) { - return; - } - auto in_data = in + gid.y * cst.inside; - auto out_data = out + gid.y * cst.inside; - - float mean; - float sum = 0.0f; - float square_sum = 0.0f; - - for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { - sum += in_data[i]; - } - sum = simd_sum(sum); - mean = sum / cst.inside; - - for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { - float dis = (in_data[i] - mean); - square_sum += dis * dis; - } - square_sum = simd_sum(square_sum); - - if(tiisg == 0) { - float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); - - float norm = var * ((float)in_data[gid.x] - mean); - if(cst.has_gamma_beta) { - out_data[gid.x] = (ftype)(norm * gamma[gid.x] + beta[gid.x]); - } else { - out_data[gid.x] = (ftype)(norm); - } - } -} - kernel void layernorm_x4(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant layernorm_constants& cst [[buffer(2)]], @@ -126,57 +84,6 @@ kernel void layernorm_x4(const device ftype4 *in [[buffer(0)]], } } -kernel void layernorm_x4_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant layernorm_constants& cst [[buffer(2)]], - const device float4 *gamma [[buffer(3)]], - const device float4 *beta [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - if ((int)gid.x >= cst.inside/4 || (int)gid.y >= cst.outside) { - return; - } - auto in_data = in + gid.y * cst.inside/4; - auto out_data = out + gid.y * cst.inside/4; - - float mean; - float sum = 0.0f; - float square_sum = 0.0f; - - for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { - sum += in_data[i].x; - sum += in_data[i].y; - sum += in_data[i].z; - sum += in_data[i].w; - } - sum = simd_sum(sum); - mean = sum / cst.inside; - - for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { - float dis = (in_data[i].x - mean); - square_sum += dis * dis; - dis = (in_data[i].y - mean); - square_sum += dis * dis; - dis = (in_data[i].z - mean); - square_sum += dis * dis; - dis = (in_data[i].w - mean); - square_sum += dis * dis; - } - square_sum = simd_sum(square_sum); - - if(tiisg == 0) { - float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); - - float4 norm = var * ((float4)in_data[gid.x] - mean); - if(cst.has_gamma_beta) { - out_data[gid.x] = (ftype4)(norm * gamma[gid.x] + beta[gid.x]); - } else { - out_data[gid.x] = (ftype4)(norm); - } - } -} - kernel void layernorm_x1_rms(const device ftype *in [[buffer(0)]], device ftype *out [[buffer(1)]], constant layernorm_constants& cst [[buffer(2)]], @@ -205,41 +112,6 @@ kernel void layernorm_x1_rms(const device ftype *in [[buffer(0)]], } } -kernel void layernorm_x1_rms_sg(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - constant layernorm_constants& cst [[buffer(2)]], - const device float *gamma [[buffer(3)]], - const device float *beta [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - if ((int)gid.x >= cst.inside || (int)gid.y >= cst.outside) { - return; - } - auto in_data = in + gid.y * cst.inside; - auto out_data = out + gid.y * cst.inside; - - float square_sum = 0.0f; - - for(int i = tiisg; i < cst.inside; i+=SIMD_GROUP_WIDTH) { - float dis = in_data[i]; - square_sum += dis * dis; - } - - square_sum = simd_sum(square_sum); - - if(tiisg == 0) { - float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); - - float norm = var * ((float)in_data[gid.x]); - if(cst.has_gamma_beta) { - out_data[gid.x] = (ftype)(norm * gamma[gid.x] + beta[gid.x]); - } else { - out_data[gid.x] = (ftype)(norm); - } - } -} - kernel void layernorm_x4_rms(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant layernorm_constants& cst [[buffer(2)]], @@ -273,100 +145,3 @@ kernel void layernorm_x4_rms(const device ftype4 *in [[buffer(0)]], out_data[gid.x] = (ftype4)(norm); } } - -kernel void layernorm_x4_rms_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant layernorm_constants& cst [[buffer(2)]], - const device float4 *gamma [[buffer(3)]], - const device float4 *beta [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - if ((int)gid.x >= cst.inside/4 || (int)gid.y >= cst.outside) { - return; - } - - int in_idx = gid.x; - int out_idx = gid.y; - - auto in_data = in + out_idx * cst.inside/4; - auto out_data = out + out_idx * cst.inside/4; - - float4 square_sum = 0.0f; - float square_sum_all = 0.0f; - for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { - float4 data = float4(in_data[i]); - square_sum += data * data; - } - square_sum_all += (square_sum[0] + square_sum[1] + square_sum[2] + square_sum[3]); - square_sum_all = simd_sum(square_sum_all); - - if(tiisg == 0) { - float var = 1.0 / sqrt(square_sum_all / cst.inside + cst.eps); - - float4 norm = var * ((float4)in_data[in_idx]); - if(cst.has_gamma_beta) { - out_data[in_idx] = (ftype4)(norm * gamma[in_idx] + beta[in_idx]); - } else { - out_data[in_idx] = (ftype4)(norm); - } - } -} - -kernel void layernorm_x16_rms_sg(const device ftype4 *in [[buffer(0)]], - device ftype4 *out [[buffer(1)]], - constant layernorm_constants& cst [[buffer(2)]], - const device float4 *gamma [[buffer(3)]], - const device float4 *beta [[buffer(4)]], - uint3 gid [[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - int in_idx = gid.x * 4; - int out_idx = gid.y; - - auto in_data = in + out_idx * cst.inside/4; - auto out_data = out + out_idx * cst.inside/4; - - float4 square_sum = 0.0f; - float square_sum_all = 0.0f; - for(int i = tiisg; i < cst.inside/4; i+=SIMD_GROUP_WIDTH) { - float4 data = float4(in_data[i]); - square_sum += data * data; - } - square_sum_all += (square_sum[0] + square_sum[1] + square_sum[2] + square_sum[3]); - square_sum_all = simd_sum(square_sum_all); - float var = 1.0 / sqrt(square_sum_all / cst.inside + cst.eps); - - if(tiisg == 0) { - float4 norm = var * ((float4)in_data[in_idx]); - if(cst.has_gamma_beta) { - out_data[in_idx] = (ftype4)(norm * gamma[in_idx] + beta[in_idx]); - } else { - out_data[in_idx] = (ftype4)(norm); - } - } - if(tiisg == 1 && in_idx + 1 < cst.inside/4) { - float4 norm = var * ((float4)in_data[in_idx+1]); - if(cst.has_gamma_beta) { - out_data[in_idx+1] = (ftype4)(norm * gamma[in_idx+1] + beta[in_idx+1]); - } else { - out_data[in_idx+1] = (ftype4)(norm); - } - } - if(tiisg == 2 && in_idx + 2 < cst.inside/4) { - float4 norm = var * ((float4)in_data[in_idx+2]); - if(cst.has_gamma_beta) { - out_data[in_idx+2] = (ftype4)(norm * gamma[in_idx+2] + beta[in_idx+2]); - } else { - out_data[in_idx+2] = (ftype4)(norm); - } - } - if(tiisg == 3 && in_idx + 3 < cst.inside/4) { - float4 norm = var * ((float4)in_data[in_idx+3]); - if(cst.has_gamma_beta) { - out_data[in_idx+3] = (ftype4)(norm * gamma[in_idx+3] + beta[in_idx+3]); - } else { - out_data[in_idx+3] = (ftype4)(norm); - } - } -} diff --git a/source/backend/metal/shader/MetalScale.metal b/source/backend/metal/shader/MetalScale.metal index ead56d11e..824f7c974 100644 --- a/source/backend/metal/shader/MetalScale.metal +++ b/source/backend/metal/shader/MetalScale.metal @@ -2,17 +2,20 @@ struct scale_shape { int size; int steps; int batch; + int offset; }; kernel void scale_ca(const device ftype4 *in [[buffer(0)]], device ftype4 *out [[buffer(1)]], constant scale_shape &s [[buffer(2)]], - const device float4 *scales [[buffer(3)]], - const device float4 *biasTerms [[buffer(4)]], + const device float4 *scalesbias[[buffer(3)]], uint2 gid [[thread_position_in_grid]]) { if ((int)gid.x >= s.size || (int)gid.y >= s.steps * s.batch) return; int z = gid.y / s.batch; + int offset = s.offset; + float4 scale = scalesbias[z]; + float4 bias = scalesbias[z+offset]; out[int(gid.y) * s.size + int(gid.x)] = - in [int(gid.y) * s.size + int(gid.x)] * ftype4(scales[z]) + ftype4(biasTerms[z]); + (ftype4)((float4)in[int(gid.y) * s.size + int(gid.x)] * scale + bias); } diff --git a/source/backend/metal/shader/MetalSoftmax.metal b/source/backend/metal/shader/MetalSoftmax.metal index 7bed37c00..98e6db6d2 100644 --- a/source/backend/metal/shader/MetalSoftmax.metal +++ b/source/backend/metal/shader/MetalSoftmax.metal @@ -17,42 +17,6 @@ static inline float4 softmax_filter(float4 value, int z, int limit) { return select(0, value, z * 4 + int4(0, 1, 2, 3) < limit); } - -kernel void softmax_plane_sg(const device ftype *in [[buffer(0)]], - device ftype *out [[buffer(1)]], - constant softmax_shape& s [[buffer(2)]], - uint2 gid[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]] - ) { - // threadgroup contain one simdgroup - // simdgroup compute axis data - if ((int)gid.x >= s.inside_size || (int)gid.y >= s.outside_size) return; - - auto axis_off = gid.y * s.axis_length * s.inside_size + gid.x; - auto axis_in = in + axis_off; - auto axis_out = out + axis_off; - - // get max - float max1 = -INFINITY; - for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { - max1 = max(max1, float(axis_in[i * s.inside_size])); - } - max1 = simd_max(max1); - - // get sum - float sum1 = 0; - for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { - sum1 += exp(float(axis_in[i * s.inside_size]) - float(max1)); - } - sum1 = simd_sum(sum1); - - // output - for (int i = tiisg; i < s.axis_length; i+=SIMD_GROUP_WIDTH) { - axis_out[i * s.inside_size] = ftype(exp(float(axis_in[i * s.inside_size]) - float(max1)) / sum1); - } -} - kernel void softmax_plane(const device ftype *in [[buffer(0)]], device ftype *out [[buffer(1)]], constant softmax_shape& s [[buffer(2)]], diff --git a/source/backend/nnapi/execution/NNAPIUnary.cpp b/source/backend/nnapi/execution/NNAPIUnary.cpp index 09599917a..7cccbae37 100644 --- a/source/backend/nnapi/execution/NNAPIUnary.cpp +++ b/source/backend/nnapi/execution/NNAPIUnary.cpp @@ -40,7 +40,7 @@ ErrorCode NNAPIUnary::onResize(const std::vector &inputs, const std::v {UnaryOpOperation_FLOOR, ANEURALNETWORKS_FLOOR}, {UnaryOpOperation_ROUND, -1}, {UnaryOpOperation_SIGN, -1}, - {UnaryOpOperation_SIGMOID, -1}, + {UnaryOpOperation_SIGMOID, ANEURALNETWORKS_LOGISTIC}, {UnaryOpOperation_LOG1P, -1}, {UnaryOpOperation_SQUARE, -1}, {UnaryOpOperation_NEG, ANEURALNETWORKS_NEG}, diff --git a/source/backend/opencl/core/OpenCLGemmTune.cpp b/source/backend/opencl/core/OpenCLGemmTune.cpp index 90a0d7400..be1c6ec04 100644 --- a/source/backend/opencl/core/OpenCLGemmTune.cpp +++ b/source/backend/opencl/core/OpenCLGemmTune.cpp @@ -204,17 +204,61 @@ std::vector getGemmParams(const std::vector &gemmSize, const return tunedGemmParams[info]; } + auto getMaxDivisor = [](uint32_t num) -> uint32_t { + std::vector divisors = {128, 64, 32}; + for (const auto& divisor : divisors) { + if (num % divisor == 0) { + return divisor; + } + } + return 16; + }; + // top gpu device and large computation + if(runtime->getGpuLevel() >= MEDIUM){ + // total computation + auto compute_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 256.0 * gemmSize[1] / 256.0 * gemmSize[2] / 256.0; + auto thread_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 256.0 * gemmSize[1] / 256.0; + + // each dimension is even + bool is_even = gemmSize[0] >= 256 && gemmSize[1] >= 128 && gemmSize[2] >= 128; + is_even |= gemmSize[1] >= 128 && gemmSize[2] >= 128 && gemmSize[4] >= 4; + bool is_div = gemmSize[0] % 64 == 0 && gemmSize[1] % 32 == 0; + if(compute_ratio >= 1.0 && thread_ratio >= 1.0 && is_even && is_div) { + int maxDivsorM = getMaxDivisor(gemmSize[0]); + int maxDivsorN = getMaxDivisor(gemmSize[1]); + maxDivsorM = maxDivsorM > 64 ? 64 : maxDivsorM; + maxDivsorN = maxDivsorN > 32 ? 32 : maxDivsorN; + std::vector params_prefer = {16, 2, 16, 16, 64, 8, 8, 32, 0, 0, 0, 0, 4, 4}; + params_prefer[2] = maxDivsorM / 4; + params_prefer[3] = maxDivsorM / 4; + params_prefer[4] = maxDivsorM; + params_prefer[5] = maxDivsorN / 4; + params_prefer[6] = maxDivsorN / 4; + params_prefer[7] = maxDivsorN; + + return params_prefer; + } + } if(runtime->getGpuLevel() == TOP && (runtime->getCLTuneLevel() == None || runtime->getCLTuneLevel() == Fast)) { // total computation - auto compute_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 1024.0 * gemmSize[1] / 1024.0 * gemmSize[2] / 1024.0; - auto thread_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 1024.0 * gemmSize[1] / 1024.0; + auto compute_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 512.0 * gemmSize[1] / 512.0 * gemmSize[2] / 512.0; + auto thread_ratio = 1.0 * gemmSize[4] * gemmSize[0] / 512.0 * gemmSize[1] / 512.0; // each dimension is even - bool is_even = gemmSize[0] >= 512 && gemmSize[1] >= 256; - bool is_div = gemmSize[0] % 128 == 0 && gemmSize[1] % 128 == 0; - if(compute_ratio >= 2.0 && thread_ratio >= 2.0 && is_even && is_div) { - return {16, 2, 16, 16, 128, 16, 16, 128, 0, 0, 0, 0, 8, 8}; + bool is_even = gemmSize[0] >= 512 && gemmSize[1] >= 256 && gemmSize[2] >= 256; + is_even |= gemmSize[1] >= 128 && gemmSize[2] >= 128 && gemmSize[4] >= 4; + bool is_div = gemmSize[0] % 64 == 0 && gemmSize[1] % 64 == 0; + if(compute_ratio >= 1.0 && thread_ratio >= 1.0 && is_even && is_div) { + int maxDivsorM = getMaxDivisor(gemmSize[0]); + int maxDivsorN = getMaxDivisor(gemmSize[1]); + std::vector params_prefer = {16, 2, 16, 16, 128, 16, 16, 128, 0, 0, 0, 0, 8, 8}; + params_prefer[4] = maxDivsorM; + params_prefer[7] = maxDivsorN; + params_prefer[12] = maxDivsorM / 16; + params_prefer[13] = maxDivsorN / 16; + + return params_prefer; } } std::vector tuneLwsRes; @@ -232,15 +276,6 @@ std::vector getGemmParams(const std::vector &gemmSize, const } if(runtime->getCLTuneLevel() == None) { - auto getMaxDivisor = [](uint32_t num) -> uint32_t { - std::vector divisors = {128, 64, 32}; - for (const auto& divisor : divisors) { - if (num % divisor == 0) { - return divisor; - } - } - return 16; - }; float multiNum = 1.0 * gemmSize[0] / 512.0 * gemmSize[1] / 512.0 * gemmSize[2] / 512.0; int maxDivsorM = getMaxDivisor(gemmSize[0]); int maxDivsorN = getMaxDivisor(gemmSize[1]); diff --git a/source/backend/opencl/core/runtime/OpenCLRuntime.cpp b/source/backend/opencl/core/runtime/OpenCLRuntime.cpp index 9fc109e59..b21c7859b 100644 --- a/source/backend/opencl/core/runtime/OpenCLRuntime.cpp +++ b/source/backend/opencl/core/runtime/OpenCLRuntime.cpp @@ -66,25 +66,25 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const const std::string deviceName = mFirstGPUDevicePtr->getInfo(); mDeviceName = deviceName; const std::string deviceVersion = mFirstGPUDevicePtr->getInfo(); - std::map maliArMap { - {"Mali-T860", MIDGARD}, - {"Mali-T880", MIDGARD}, - {"Mali-G31", BIFROST}, - {"Mali-G51", BIFROST}, - {"Mali-G52", BIFROST}, - {"Mali-G71", BIFROST}, - {"Mali-G72", BIFROST}, - {"Mali-G76", BIFROST}, - {"Mali-G57", VALHALL}, - {"Mali-G68", VALHALL}, - {"Mali-G77", VALHALL}, - {"Mali-G78", VALHALL}, - {"Mali-G310", VALHALL}, - {"Mali-G510", VALHALL}, - {"Mali-G610", VALHALL}, - {"Mali-G615", VALHALL}, - {"Mali-G710", VALHALL}, - {"Mali-G715", VALHALL}, + std::map> maliArMap { + {"Mali-T860", {MIDGARD, LOW}}, + {"Mali-T880", {MIDGARD, LOW}}, + {"Mali-G31", {BIFROST, LOW}}, + {"Mali-G51", {BIFROST, LOW}}, + {"Mali-G52", {BIFROST, LOW}}, + {"Mali-G71", {BIFROST, LOW}}, + {"Mali-G72", {BIFROST, LOW}}, + {"Mali-G76", {BIFROST, MEDIUM}}, + {"Mali-G57", {VALHALL, LOW}}, + {"Mali-G68", {VALHALL, LOW}}, + {"Mali-G77", {VALHALL, MEDIUM}}, + {"Mali-G78", {VALHALL, MEDIUM}}, + {"Mali-G310", {VALHALL, LOW}}, + {"Mali-G510", {VALHALL, LOW}}, + {"Mali-G610", {VALHALL, LOW}}, + {"Mali-G615", {VALHALL, LOW}}, + {"Mali-G710", {VALHALL, TOP}}, + {"Mali-G715", {VALHALL, TOP}}, }; const std::string deviceVendor = mFirstGPUDevicePtr->getInfo(); @@ -136,9 +136,11 @@ OpenCLRuntime::OpenCLRuntime(const BackendConfig::PrecisionMode precision, const } else if (deviceName.find("Mali") != std::string::npos) { mGpuType = MALI; if(maliArMap.find(deviceName) != maliArMap.end()){ - mMaliAr = maliArMap[deviceName]; + mMaliAr = maliArMap[deviceName].first; + mGpuLevel = maliArMap[deviceName].second; }else{ mMaliAr = VALHALL; + mGpuLevel = UNDEFINED; } } else if (deviceVendor.find("Advanced Micro Devices") != std::string::npos) { // Radeon series GPU is main product of Advanced Micro Devices (AMD) diff --git a/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp b/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp index 2c6f4a237..e252418b9 100644 --- a/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/AttentionBufExecution.cpp @@ -113,7 +113,7 @@ ErrorCode AttentionBufExecution::longPrefillResize(const std::vector & mKVCacheCLManager->setArgs(seq_len, numHead, kvNumHead, headDim); mKVCacheCLManager->allocKVCache(); mKv_seq_len = mKVCacheCLManager->kvLength(); - int max_len = ROUND_UP(mKVCacheCLManager->maxLength(), 4); + mKeyValueMaxlen = ROUND_UP(mKVCacheCLManager->maxLength(), 4); mAlignQ = 128; mAlignKV = 128; mAlignHDK = 4; @@ -173,7 +173,7 @@ ErrorCode AttentionBufExecution::longPrefillResize(const std::vector & int tile[4] = {mAlignQ, mAlignKV, mAlignHDK, mAlignHDN}; int shape[4] = {seq_len, mKv_seq_len, numHead, headDim}; - int param[4] = {group_size, batch, max_len, 0}; + int param[4] = {group_size, batch, 0, 0}; mKernel_rearrange = runtime->buildKernel("attention_buf", "rearrange_qkv", buildOption, inputs[0], outputs[0]); auto maxWorkGroupSize = static_cast(runtime->getMaxWorkGroupSize(mKernel_rearrange)); @@ -197,13 +197,18 @@ ErrorCode AttentionBufExecution::longPrefillResize(const std::vector & ret |= mKernel_rearrange->get().setArg(index++, tile); ret |= mKernel_rearrange->get().setArg(index++, shape); ret |= mKernel_rearrange->get().setArg(index++, param); + ret |= mKernel_rearrange->get().setArg(index++, mKeyValueMaxlen); MNN_CHECK_CL_SUCCESS(ret, "setArg rearrange_qkv"); mLocalWorkSizeRearrg = localWS3DDefault(mGlobalWorkSizeRearrg, maxWorkGroupSize, runtime, "rearrange_qkv", mKernel_rearrange).first; mGlobalWorkSizeRearrg[0] = ROUND_UP(mGlobalWorkSizeRearrg[0], std::max((uint32_t)1, mLocalWorkSizeRearrg[0])); mGlobalWorkSizeRearrg[1] = ROUND_UP(mGlobalWorkSizeRearrg[1], std::max((uint32_t)1, mLocalWorkSizeRearrg[1])); mGlobalWorkSizeRearrg[2] = ROUND_UP(mGlobalWorkSizeRearrg[2], std::max((uint32_t)1, mLocalWorkSizeRearrg[2])); - mOpenCLBackend->recordKernel3d(mKernel_rearrange, mGlobalWorkSizeRearrg, mLocalWorkSizeRearrg); + mRgUpdateInfo.update_kernel_args.push_back({0, 9, sizeof(cl_mem), &(*(mKVCacheCLManager->key()))()}); + mRgUpdateInfo.update_kernel_args.push_back({0, 10, sizeof(cl_mem), &(*(mKVCacheCLManager->value()))()}); + mRgUpdateInfo.update_kernel_args.push_back({0, 14, sizeof(mKeyValueMaxlen), &mKeyValueMaxlen}); + mOpRecordUpdateInfo.emplace_back(&mRgUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_rearrange, mGlobalWorkSizeRearrg, mLocalWorkSizeRearrg, &mRgUpdateInfo); } // mask rearaange @@ -643,7 +648,10 @@ ErrorCode AttentionBufExecution::onResize(const std::vector &inputs, c mGlobalWorkSizeRearrg[0] = ROUND_UP(mGlobalWorkSizeRearrg[0], std::max((uint32_t)1, mLocalWorkSizeRearrg[0])); mGlobalWorkSizeRearrg[1] = ROUND_UP(mGlobalWorkSizeRearrg[1], std::max((uint32_t)1, mLocalWorkSizeRearrg[1])); mGlobalWorkSizeRearrg[2] = ROUND_UP(mGlobalWorkSizeRearrg[2], std::max((uint32_t)1, mLocalWorkSizeRearrg[2])); - mOpenCLBackend->recordKernel3d(mKernel_rearrange, mGlobalWorkSizeRearrg, mLocalWorkSizeRearrg); + mRgUpdateInfo.update_kernel_args.push_back({0, 4, sizeof(cl_mem), &(*(mKVCacheCLManager->key()))()}); + mRgUpdateInfo.update_kernel_args.push_back({0, 6, sizeof(mKeyValueMaxlen), &mKeyValueMaxlen}); + mOpRecordUpdateInfo.emplace_back(&mRgUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_rearrange, mGlobalWorkSizeRearrg, mLocalWorkSizeRearrg, &mRgUpdateInfo); } { // matmul qk @@ -677,7 +685,10 @@ ErrorCode AttentionBufExecution::onResize(const std::vector &inputs, c mGlobalWorkSizeQk[0] = ROUND_UP(mGlobalWorkSizeQk[0], std::max((uint32_t)1, mLocalWorkSizeQk[0])); mGlobalWorkSizeQk[1] = ROUND_UP(mGlobalWorkSizeQk[1], std::max((uint32_t)1, mLocalWorkSizeQk[1])); mGlobalWorkSizeQk[2] = ROUND_UP(mGlobalWorkSizeQk[2], std::max((uint32_t)1, mLocalWorkSizeQk[2])); - mOpenCLBackend->recordKernel3d(mKernel_qk, mGlobalWorkSizeQk, mLocalWorkSizeQk); + mQkUpdateInfo.update_kernel_args.push_back({0, 4, sizeof(cl_mem), &(*(mKVCacheCLManager->key()))()}); + mQkUpdateInfo.update_kernel_args.push_back({0, 10, sizeof(mKeyValueMaxlen), &mKeyValueMaxlen}); + mOpRecordUpdateInfo.emplace_back(&mQkUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_qk, mGlobalWorkSizeQk, mLocalWorkSizeQk, &mQkUpdateInfo); } { // softmax @@ -745,7 +756,10 @@ ErrorCode AttentionBufExecution::onResize(const std::vector &inputs, c mGlobalWorkSizeRearrgV[0] = ROUND_UP(mGlobalWorkSizeRearrgV[0], std::max((uint32_t)1, mLocalWorkSizeRearrgV[0])); mGlobalWorkSizeRearrgV[1] = ROUND_UP(mGlobalWorkSizeRearrgV[1], std::max((uint32_t)1, mLocalWorkSizeRearrgV[1])); mGlobalWorkSizeRearrgV[2] = ROUND_UP(mGlobalWorkSizeRearrgV[2], std::max((uint32_t)1, mLocalWorkSizeRearrgV[2])); - mOpenCLBackend->recordKernel3d(mKernel_rearrangeV, mGlobalWorkSizeRearrgV, mLocalWorkSizeRearrgV); + mRgVUpdateInfo.update_kernel_args.push_back({0, 4, sizeof(cl_mem), &(*(mKVCacheCLManager->value()))()}); + mRgVUpdateInfo.update_kernel_args.push_back({0, 6, sizeof(mKeyValueMaxlen), &mKeyValueMaxlen}); + mOpRecordUpdateInfo.emplace_back(&mRgVUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_rearrangeV, mGlobalWorkSizeRearrgV, mLocalWorkSizeRearrgV, &mRgVUpdateInfo); } // qk * value { @@ -775,7 +789,10 @@ ErrorCode AttentionBufExecution::onResize(const std::vector &inputs, c mGlobalWorkSizeQkv[0] = ROUND_UP(mGlobalWorkSizeQkv[0], std::max((uint32_t)1, mLocalWorkSizeQkv[0])); mGlobalWorkSizeQkv[1] = ROUND_UP(mGlobalWorkSizeQkv[1], std::max((uint32_t)1, mLocalWorkSizeQkv[1])); mGlobalWorkSizeQkv[2] = ROUND_UP(mGlobalWorkSizeQkv[2], std::max((uint32_t)1, mLocalWorkSizeQkv[2])); - mOpenCLBackend->recordKernel3d(mKernel_qkv, mGlobalWorkSizeQkv, mLocalWorkSizeQkv); + mQkvUpdateInfo.update_kernel_args.push_back({0, 4, sizeof(cl_mem), &(*(mKVCacheCLManager->value()))()}); + mQkvUpdateInfo.update_kernel_args.push_back({0, 8, sizeof(mKeyValueMaxlen), &mKeyValueMaxlen}); + mOpRecordUpdateInfo.emplace_back(&mQkvUpdateInfo); + mOpenCLBackend->recordKernel3d(mKernel_qkv, mGlobalWorkSizeQkv, mLocalWorkSizeQkv, &mQkvUpdateInfo); } } else { mKv_seq_len = mKVCacheCLManager->kvLength() + 1; @@ -1016,7 +1033,7 @@ ErrorCode AttentionBufExecution::onResize(const std::vector &inputs, c return NO_ERROR; } -ErrorCode AttentionBufExecution::DecodeSetArgs(const std::vector &inputs, const std::vector &outputs){ +ErrorCode AttentionBufExecution::UpdateArgs(const std::vector &inputs, const std::vector &outputs){ auto query = inputs[0]; auto key = inputs[1]; auto value = inputs[2]; @@ -1030,7 +1047,72 @@ ErrorCode AttentionBufExecution::DecodeSetArgs(const std::vector &inpu int headDim = shape[3]; int group_size = numHead / kvNumHead; float scale = 1.0 / sqrt(headDim); + auto mask_shape = mask->shape(); + int mask_seqlen = mask_shape[2]; + int mask_kvlen = mask_shape[3]; + // prefill + if(mIsDecode == false){ + // reset mPastLength + mKVCacheCLManager->setArgs(mask_kvlen, numHead, kvNumHead, headDim); + // key value static memory has been changed, need reset args + if(mKeyValueMaxlen != ROUND_UP(mKVCacheCLManager->maxLength(), 4)){ + mKeyValueMaxlen = ROUND_UP(mKVCacheCLManager->maxLength(), 4); + #ifndef ENABLE_OPENCL_TIME_PROFILER + if(mOpenCLBackend->isUseRecordQueue()){ + if(mLongPrefill){ + mRgUpdateInfo.update_kernel_args[0].arg_value = &(*(mKVCacheCLManager->key()))(); + mQkUpdateInfo.update_kernel_args[0].arg_value = &(*(mKVCacheCLManager->key()))(); + mRgVUpdateInfo.update_kernel_args[0].arg_value = &(*(mKVCacheCLManager->value()))(); + mQkvUpdateInfo.update_kernel_args[0].arg_value = &(*(mKVCacheCLManager->value()))(); + }else{ + mRgUpdateInfo.update_kernel_args[0].arg_value = &(*(mKVCacheCLManager->key()))(); + } + } else { + #endif + if(mLongPrefill){ + // rearrange key value + cl_int ret = CL_SUCCESS; + ret |= mKernel_rearrange->get().setArg(9, *mKVCacheCLManager->key()); + ret |= mKernel_rearrange->get().setArg(10, *mKVCacheCLManager->value()); + ret |= mKernel_rearrange->get().setArg(14, mKeyValueMaxlen); + MNN_CHECK_CL_SUCCESS(ret, "reSetArg rearrange_k"); + }else{ + { + // rearrange key + cl_int ret = CL_SUCCESS; + ret |= mKernel_rearrange->get().setArg(4, *mKVCacheCLManager->key()); + ret |= mKernel_rearrange->get().setArg(6, mKeyValueMaxlen); + MNN_CHECK_CL_SUCCESS(ret, "reSetArg rearrange_k"); + } + { + // matmul qk + cl_int ret = CL_SUCCESS; + ret |= mKernel_qk->get().setArg(4, *mKVCacheCLManager->key()); + ret |= mKernel_qk->get().setArg(10, mKeyValueMaxlen); + MNN_CHECK_CL_SUCCESS(ret, "reSetArg matmul_qk_decode"); + } + { + cl_int ret = CL_SUCCESS; + ret |= mKernel_rearrangeV->get().setArg(4, *mKVCacheCLManager->value()); + ret |= mKernel_rearrangeV->get().setArg(6, mKeyValueMaxlen); + MNN_CHECK_CL_SUCCESS(ret, "reSetArg rearrange_v"); + } + // qk * value + { + cl_int ret = CL_SUCCESS; + ret |= mKernel_qkv->get().setArg(4, *mKVCacheCLManager->value()); + ret |= mKernel_qkv->get().setArg(8, mKeyValueMaxlen); + MNN_CHECK_CL_SUCCESS(ret, "reSetArg matmul_qkv_decode"); + } + } + #ifndef ENABLE_OPENCL_TIME_PROFILER + } + #endif + } + return NO_ERROR; + } + // Decode mKv_seq_len = mKVCacheCLManager->kvLength() + 1; mKeyValueMaxlen = ROUND_UP(mKVCacheCLManager->maxLength(), 4); mKVCacheCLManager->addKvLength(); @@ -1127,8 +1209,8 @@ ErrorCode AttentionBufExecution::onExecute(const std::vector &inputs, #endif if(mIsDecode){ mKVCacheCLManager->reallocKVCache(); - DecodeSetArgs(inputs, outputs); } + UpdateArgs(inputs, outputs); #ifdef ENABLE_OPENCL_TIME_PROFILER if(mLongPrefill) { cl::Event event0, event1, event2, event3, event4, event5, event6; diff --git a/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp b/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp index 0f617981c..6b684301d 100644 --- a/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp +++ b/source/backend/opencl/execution/buffer/AttentionBufExecution.hpp @@ -62,7 +62,7 @@ class AttentionBufExecution : public CommonExecution { AttentionBufExecution(const MNN::Op *op, Backend *backend, bool kv_cache); AttentionBufExecution(std::shared_ptr manager, const MNN::Op *op, Backend *backend); ErrorCode longPrefillResize(const std::vector &inputs, const std::vector &outputs); - ErrorCode DecodeSetArgs(const std::vector &inputs, const std::vector &outputs); + ErrorCode UpdateArgs(const std::vector &inputs, const std::vector &outputs); virtual ~AttentionBufExecution() = default; virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; diff --git a/source/backend/opencl/execution/buffer/ConvBufExecution.cpp b/source/backend/opencl/execution/buffer/ConvBufExecution.cpp index 7f67f9ddf..784ace22d 100644 --- a/source/backend/opencl/execution/buffer/ConvBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/ConvBufExecution.cpp @@ -557,9 +557,9 @@ ErrorCode ConvBufExecution::onResize(const std::vector &inputs, const mKernel.resize(conv_block_num); - std::vector> kernel(actual_kernel); - std::vector> globalWorkSize(actual_kernel); - std::vector> localWorkSize(actual_kernel); + std::shared_ptr kernel[total_kernel]; + std::vector globalWorkSize[total_kernel]; + std::vector localWorkSize[total_kernel]; std::pair min_cost(INT_MAX, 0);//(min_time, min_index) for(int knl_idx = 0; knl_idx < actual_kernel; knl_idx++) { std::set buildOption = mResource->mBuildOptions; diff --git a/source/backend/opencl/execution/buffer/ConvBufExecution.hpp b/source/backend/opencl/execution/buffer/ConvBufExecution.hpp index 60e03a36c..6b1fbf3cf 100644 --- a/source/backend/opencl/execution/buffer/ConvBufExecution.hpp +++ b/source/backend/opencl/execution/buffer/ConvBufExecution.hpp @@ -36,6 +36,7 @@ struct ConvBufResource { bool mConv1x1Opt = false; bool mConv1x1C8Opt = false; bool mConv1x1Local = false; + float mCoef = 1.0f; /* 0 -> not use 1 -> use small tile diff --git a/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp b/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp index ce9df36b9..9fa9a8f52 100644 --- a/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp +++ b/source/backend/opencl/execution/buffer/ConvBufLowMemoryExecution.cpp @@ -32,48 +32,102 @@ void ConvBufLowMemoryExecution::getInfoFromOpLowMemory(std::shared_ptralpha.get(); int totalCount = quanCommon->alpha.size(); + int soSize = 1; if (quanCommon->asymmetric) { + soSize = 2; totalCount /= 2; + mResource->mBuildOptions.emplace("-DASYMMETRIC"); } int numAlpha = mResource->mOutputChannel; mResource->mBlockSize = totalCount / numAlpha; // set mDequantScale mDequantOffset int numAlphaPack = ROUND_UP(numAlpha, 4); - - mResource->dequantScaleOffset.reset(Tensor::createDevice({mResource->mBlockSize, numAlphaPack, 2})); + + mResource->dequantScaleOffset.reset(Tensor::createDevice({ROUND_UP(mResource->mBlockSize, 4), numAlphaPack, soSize})); mOpenCLBackend->onAcquireBuffer(mResource->dequantScaleOffset.get(), Backend::STATIC); cl::Buffer &dequantScaleOffsetBuffer = openCLBuffer(mResource->dequantScaleOffset.get()); // transfer data from src in cpu to dst in gpu int fpBytes = mOpenCLBackend->fpBytes(); cl_int resBias, resScaleOffset; - int mapSize = mResource->mBlockSize * numAlphaPack * sizeof(int32_t) * 2; + int mapSize = mResource->mBlockSize * numAlphaPack * fpBytes * soSize; void * dequantScaleOffsetBufferMap = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(dequantScaleOffsetBuffer, true, CL_MAP_WRITE, 0, mapSize, nullptr, nullptr, &resScaleOffset); - // mBlockSize % 4 need equal 0 - if (dequantScaleOffsetBufferMap != nullptr && resScaleOffset == CL_SUCCESS) { - if (quanCommon->asymmetric) { + float coef = 1.0; + if(fpBytes == 2) { + float max_data = 0.0f; + if (quanCommon->asymmetric){ for (int i = 0; i < numAlpha; ++i) { auto srcZ = dequantAlpha + i * mResource->mBlockSize * 2; for(int j = 0; j < mResource->mBlockSize; ++j){ - float o = srcZ[2*j+0]; - float s = srcZ[2*j+1]; - ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2] = s; - ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2 + 1] = o; + float s = fabsf(srcZ[2*j+0]); + float b = fabsf(srcZ[2*j+1]); + float temp = ALIMAX(s, b); + if(temp > max_data) { + max_data = temp; + } } } - } else { + }else{ for (int i = 0; i < numAlpha; ++i) { auto srcZ = dequantAlpha + i * mResource->mBlockSize; for(int j = 0; j < mResource->mBlockSize; ++j){ - ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2] = srcZ[j]; - ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2 + 1] = 0.0f; + float s = fabsf(srcZ[j]); + if(s > max_data) { + max_data = s; + } } } } - } else { - MNN_ERROR("Map error dequantBufferMap == nullptr \n"); - MNN_ASSERT(false); + coef = 1000.0f / max_data; + if (dequantScaleOffsetBufferMap != nullptr && resScaleOffset == CL_SUCCESS) { + if (quanCommon->asymmetric) { + for (int i = 0; i < numAlpha; ++i) { + auto srcZ = dequantAlpha + i * mResource->mBlockSize * 2; + for(int j = 0; j < mResource->mBlockSize; ++j){ + float o = srcZ[2*j+0]; + float s = srcZ[2*j+1]; + ((half_float::half*)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2] = (half_float::half)(s * coef); + ((half_float::half*)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2 + 1] = (half_float::half)(o * coef); + } + } + } else { + for (int i = 0; i < numAlpha; ++i) { + auto srcZ = dequantAlpha + i * mResource->mBlockSize; + for(int j = 0; j < mResource->mBlockSize; ++j){ + ((half_float::half*)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i)] = (half_float::half)(srcZ[j] * coef); + } + } + } + } else { + MNN_ERROR("Map error dequantBufferMap == nullptr \n"); + MNN_ASSERT(false); + } + } else{ + if (dequantScaleOffsetBufferMap != nullptr && resScaleOffset == CL_SUCCESS) { + if (quanCommon->asymmetric) { + for (int i = 0; i < numAlpha; ++i) { + auto srcZ = dequantAlpha + i * mResource->mBlockSize * 2; + for(int j = 0; j < mResource->mBlockSize; ++j){ + float o = srcZ[2*j+0]; + float s = srcZ[2*j+1]; + ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2] = s * coef; + ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i) * 2 + 1] = o * coef; + } + } + } else { + for (int i = 0; i < numAlpha; ++i) { + auto srcZ = dequantAlpha + i * mResource->mBlockSize; + for(int j = 0; j < mResource->mBlockSize; ++j){ + ((float *)dequantScaleOffsetBufferMap)[(j * numAlphaPack + i)] = srcZ[j] * coef; + } + } + } + } else { + MNN_ERROR("Map error dequantBufferMap == nullptr \n"); + MNN_ASSERT(false); + } } + mResource->mCoef = coef; mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(dequantScaleOffsetBuffer, dequantScaleOffsetBufferMap); // set mFilterDataPtr mFilterDataPtr = (void *)quanCommon->weight.get(); @@ -297,6 +351,7 @@ void ConvBufLowMemoryExecution::tuneGeneralCaseLowMemory(Tensor * input, Tensor ret |= kernel[knl_idx]->get().setArg(idx++, UP_DIV(outChannel, 4)); ret |= kernel[knl_idx]->get().setArg(idx++, UP_DIV(height, itemH[knl_idx])); ret |= kernel[knl_idx]->get().setArg(idx++, blockDim); + ret |= kernel[knl_idx]->get().setArg(idx++, static_cast(mResource->mCoef)); MNN_CHECK_CL_SUCCESS(ret, "setArg ConvBufLowMemory Kernel Select"); std::pair, int> retTune; retTune = localWS2DDefault(globalWorkSize[knl_idx], maxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName[knl_idx] + info, kernel[knl_idx]); @@ -343,6 +398,7 @@ void ConvBufLowMemoryExecution::tuneGeneralCaseLowMemory(Tensor * input, Tensor ret |= unit.kernel->get().setArg(idx++, UP_DIV(outChannel, 4)); ret |= unit.kernel->get().setArg(idx++, UP_DIV(height, itemH[min_index])); ret |= unit.kernel->get().setArg(idx++, blockDim); + ret |= unit.kernel->get().setArg(idx++, static_cast(mResource->mCoef)); MNN_CHECK_CL_SUCCESS(ret, "setArg ConvBufLowMemory"); mOpenCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalWorkSize); unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]}; @@ -423,10 +479,12 @@ void ConvBufLowMemoryExecution::useFPWeightGemmLowMemory(Tensor * input, Tensor } ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mResource->dequantScaleOffset.get())); ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mConvGemmWeightTensor.get())); + ret |= unit.kernel->get().setArg(idx++, static_cast(mResource->mInputChannel)); ret |= unit.kernel->get().setArg(idx++, static_cast(inputChannel4Align)); ret |= unit.kernel->get().setArg(idx++, static_cast(outputChannelAlign)); ret |= unit.kernel->get().setArg(idx++, static_cast(outputChannel4Align)); ret |= unit.kernel->get().setArg(idx++, static_cast(blockDim)); + ret |= unit.kernel->get().setArg(idx++, static_cast(mResource->mCoef)); MNN_CHECK_CL_SUCCESS(ret, "setArg inverse_quant_weight"); mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, maxWorkGroupSize, runtime, "inverse_quant_weight", unit.kernel).first; @@ -544,8 +602,8 @@ void ConvBufLowMemoryExecution::tuneGemvLowMemory(Tensor * input, Tensor * outpu buildOption.emplace("-DUSE_IMAGE"); } - int local_size = 1; - if(useLocalMem){ + int local_size = 128; + if(useLocalMem && mOpenCLBackend->getOpenCLRuntime()->getCLTuneLevel() != None && mOpenCLBackend->getOpenCLRuntime()->getCLTuneLevel() != Fast){ int min_time = INT_MAX; for (int ksize = 8; ksize <= 256; ksize*=2) { auto option = buildOption; @@ -572,6 +630,7 @@ void ConvBufLowMemoryExecution::tuneGemvLowMemory(Tensor * input, Tensor * outpu ret |= kernel->get().setArg(idx++, inputChannels); ret |= kernel->get().setArg(idx++, static_cast(blockNum)); ret |= kernel->get().setArg(idx++, static_cast(blockDim)); + ret |= kernel->get().setArg(idx++, static_cast(mResource->mCoef)); MNN_CHECK_CL_SUCCESS(ret, "setArg gemv_conv1x1_buf Kernel Select"); std::pair, int> retTune; int cost_time = get2DUseLocalMemTime(gws, lws, mOpenCLBackend->getOpenCLRuntime(), kernelName + info, kernel); @@ -604,6 +663,7 @@ void ConvBufLowMemoryExecution::tuneGemvLowMemory(Tensor * input, Tensor * outpu ret |= unit.kernel->get().setArg(idx++, static_cast(inputChannels)); ret |= unit.kernel->get().setArg(idx++, static_cast(blockNum)); ret |= unit.kernel->get().setArg(idx++, static_cast(blockDim)); + ret |= unit.kernel->get().setArg(idx++, static_cast(mResource->mCoef)); MNN_CHECK_CL_SUCCESS(ret, "setArg gemv_conv_c4_0_buf"); if(useLocalMem){ mLocalWorkSize = {static_cast(local_size), 1}; @@ -671,6 +731,7 @@ void ConvBufLowMemoryExecution::tuneGemmLowMemory(Tensor * input, Tensor * outpu ret |= unit.kernel->get().setArg(idx++, static_cast(inputChannelAlign)); ret |= unit.kernel->get().setArg(idx++, static_cast(blockNum)); ret |= unit.kernel->get().setArg(idx++, static_cast(blockDim)); + ret |= unit.kernel->get().setArg(idx++, mResource->mCoef); MNN_CHECK_CL_SUCCESS(ret, "setArg gemm_conv1x1_buf"); mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, maxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName, unit.kernel).first; mOpenCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalWorkSize); diff --git a/source/backend/opencl/execution/buffer/RasterBufExecution.cpp b/source/backend/opencl/execution/buffer/RasterBufExecution.cpp index 8db39af02..e666c9bc0 100644 --- a/source/backend/opencl/execution/buffer/RasterBufExecution.cpp +++ b/source/backend/opencl/execution/buffer/RasterBufExecution.cpp @@ -58,9 +58,9 @@ ErrorCode RasterBufExecution::onEncode(const std::vector &____inputs, } mNeedZero = !TensorUtils::regionIsFull(output); mNeedZero = mNeedZero || ((outputShape[3] % 4) != 0 && MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat && !mFast); - bool cancombine = CanCombine(outputs) && (!mFast); - if(cancombine){ - regionNum = 1; + if(mFast == false){ + CanCombine(outputs); + regionNum = mCombineInfo.size(); } mUnits.resize(regionNum); if(mNeedZero) @@ -149,12 +149,11 @@ ErrorCode RasterBufExecution::onEncode(const std::vector &____inputs, return NO_ERROR; } - if(cancombine){ - auto regions = des->regions; - auto slice = regions[0]; - int nums = regions.size(); - int src_offset = regions[1].src.offset - slice.src.offset; - int dst_offset = regions[1].dst.offset - slice.dst.offset; + for(auto& info : mCombineInfo){ + auto slice = info.mRegion; + int nums = info.mCanCombineNum; + int src_offset = info.mSrc_offset; + int dst_offset = info.mDst_offset; std::set buildOptions; auto origin = slice.origin; auto inputShape = tensorShapeFormat(origin); @@ -163,7 +162,6 @@ ErrorCode RasterBufExecution::onEncode(const std::vector &____inputs, Unit &unit = mUnits[kernel_idx++]; unit.kernel = runtime->buildKernel("raster_buf", "raster_direct_buffer", buildOptions, origin, output); - const std::vector gws = {(uint32_t)slice.size[2] * nums, (uint32_t)slice.size[1], (uint32_t)slice.size[0]}; @@ -206,61 +204,6 @@ ErrorCode RasterBufExecution::onEncode(const std::vector &____inputs, unit.localWorkSize = {lws[0], lws[1], lws[2]}; unit.globalWorkSize = {gws[0], gws[1], gws[2]}; mOpenCLBackend->recordKernel3d(unit.kernel, gws, lws); - }else{ - for(auto& slice : des->regions){ - std::set buildOptions; - auto origin = slice.origin; - auto inputShape = tensorShapeFormat(origin); - int src_offset = 0; - int dst_offset = 0; - buildOptions.emplace("-DINPUT_FORMAT=" + std::to_string(TensorUtils::getDescribe(origin)->dimensionFormat)); - buildOptions.emplace("-DOUTPUT_FORMAT=" + std::to_string(outputDes->dimensionFormat)); - - Unit &unit = mUnits[kernel_idx++]; - unit.kernel = runtime->buildKernel("raster_buf", "raster_direct_buffer", buildOptions, origin, output); - const std::vector gws = {(uint32_t)slice.size[2], - (uint32_t)slice.size[1], - (uint32_t)slice.size[0]}; - uint32_t mMaxWorkGroupSize = static_cast(runtime->getMaxWorkGroupSize(unit.kernel)); - - uint32_t idx = 0; - cl_int ret = CL_SUCCESS; - ret |= unit.kernel->get().setArg(idx++, gws[0]); - ret |= unit.kernel->get().setArg(idx++, gws[1]); - ret |= unit.kernel->get().setArg(idx++, gws[2]); - ret |= unit.kernel->get().setArg(idx++, slice.size[2]); - ret |= unit.kernel->get().setArg(idx++, openCLBuffer(origin)); - ret |= unit.kernel->get().setArg(idx++, slice.src.offset); - ret |= unit.kernel->get().setArg(idx++, src_offset); - ret |= unit.kernel->get().setArg(idx++, slice.src.stride[0]); - ret |= unit.kernel->get().setArg(idx++, slice.src.stride[1]); - ret |= unit.kernel->get().setArg(idx++, slice.src.stride[2]); - ret |= unit.kernel->get().setArg(idx++, inputShape[2]); - ret |= unit.kernel->get().setArg(idx++, inputShape[1]); - ret |= unit.kernel->get().setArg(idx++, inputShape[3]); - ret |= unit.kernel->get().setArg(idx++, inputShape[0]); - ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output)); - ret |= unit.kernel->get().setArg(idx++, slice.dst.offset); - ret |= unit.kernel->get().setArg(idx++, dst_offset); - ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[0]); - ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[1]); - ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[2]); - ret |= unit.kernel->get().setArg(idx++, outputShape[2]); - ret |= unit.kernel->get().setArg(idx++, outputShape[1]); - ret |= unit.kernel->get().setArg(idx++, outputShape[3]); - ret |= unit.kernel->get().setArg(idx++, outputShape[0]); - if(ret != CL_SUCCESS) - { - MNN_PRINT("setArg err %d\n", (int)ret); - } - - std::string name = "raster_buffer"; - const std::vector lws = localWS3DDefault(gws, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), name, unit.kernel).first; - - unit.localWorkSize = {lws[0], lws[1], lws[2]}; - unit.globalWorkSize = {gws[0], gws[1], gws[2]}; - mOpenCLBackend->recordKernel3d(unit.kernel, gws, lws); - } } #ifdef LOG_VERBOSE MNN_PRINT("end RasterBufExecution onResize !\n"); @@ -283,42 +226,103 @@ class RasterBufCreator : public OpenCLBackend::Creator { } }; -bool RasterBufExecution::CanCombine(const std::vector &outputs){ +void RasterBufExecution::CanCombine(const std::vector &outputs){ auto des = TensorUtils::getDescribe(outputs[0]); auto regions = des->regions; - if(regions.size() < 2) - return false; - auto origin = regions[0].origin; - const int size0 = regions[0].size[0]; - const int size1 = regions[0].size[1]; - const int size2 = regions[0].size[2]; - const int src_offset = regions[1].src.offset - regions[0].src.offset; - const int dst_offset = regions[1].dst.offset - regions[0].dst.offset; - const int src_sride0 = regions[0].src.stride[0]; - const int src_sride1 = regions[0].src.stride[1]; - const int src_sride2 = regions[0].src.stride[2]; - const int dst_sride0 = regions[0].dst.stride[0]; - const int dst_sride1 = regions[0].dst.stride[1]; - const int dst_sride2 = regions[0].dst.stride[2]; - bool res = true; - for(int i = 1; i < regions.size(); ++i){ - res &= regions[i].origin == origin; - res &= regions[i].size[0] == size0; - res &= regions[i].size[1] == size1; - res &= regions[i].size[2] == size2; - res &= regions[i].src.stride[0] == src_sride0; - res &= regions[i].src.stride[1] == src_sride1; - res &= regions[i].src.stride[2] == src_sride2; - res &= regions[i].dst.stride[0] == dst_sride0; - res &= regions[i].dst.stride[1] == dst_sride1; - res &= regions[i].dst.stride[2] == dst_sride2; - res &= (regions[i].src.offset - regions[i - 1].src.offset) == src_offset; - res &= (regions[i].dst.offset - regions[i - 1].dst.offset) == dst_offset; - if(res == false){ - return res; + Tensor* origin; + int size0, size1, size2, src_offset, dst_offset, last_src_offset, last_dst_offset, src_sride0, src_sride1, src_sride2, dst_sride0, dst_sride1, dst_sride2; + int canCombineNum = 0; + for(auto& slice : des->regions){ + bool res = true; + if(canCombineNum == 0){ + origin = slice.origin; + size0 = slice.size[0]; + size1 = slice.size[1]; + size2 = slice.size[2]; + src_sride0 = slice.src.stride[0]; + src_sride1 = slice.src.stride[1]; + src_sride2 = slice.src.stride[2]; + dst_sride0 = slice.dst.stride[0]; + dst_sride1 = slice.dst.stride[1]; + dst_sride2 = slice.dst.stride[2]; + canCombineNum++; + // push back + mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1)); + } else if(canCombineNum == 1){ + res &= slice.origin == origin; + res &= slice.size[0] == size0; + res &= slice.size[1] == size1; + res &= slice.size[2] == size2; + res &= slice.src.stride[0] == src_sride0; + res &= slice.src.stride[1] == src_sride1; + res &= slice.src.stride[2] == src_sride2; + res &= slice.dst.stride[0] == dst_sride0; + res &= slice.dst.stride[1] == dst_sride1; + res &= slice.dst.stride[2] == dst_sride2; + if(res){ + src_offset = slice.src.offset - last_src_offset; + dst_offset = slice.dst.offset - last_dst_offset; + canCombineNum++; + // change canCombineNum + mCombineInfo.back().mSrc_offset = src_offset; + mCombineInfo.back().mDst_offset = dst_offset; + mCombineInfo.back().mCanCombineNum = canCombineNum; + } else{ + origin = slice.origin; + size0 = slice.size[0]; + size1 = slice.size[1]; + size2 = slice.size[2]; + src_sride0 = slice.src.stride[0]; + src_sride1 = slice.src.stride[1]; + src_sride2 = slice.src.stride[2]; + dst_sride0 = slice.dst.stride[0]; + dst_sride1 = slice.dst.stride[1]; + dst_sride2 = slice.dst.stride[2]; + // recover + canCombineNum = 1; + // push back + mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1)); + } + } else{ + res &= slice.origin == origin; + res &= slice.size[0] == size0; + res &= slice.size[1] == size1; + res &= slice.size[2] == size2; + res &= slice.src.stride[0] == src_sride0; + res &= slice.src.stride[1] == src_sride1; + res &= slice.src.stride[2] == src_sride2; + res &= slice.dst.stride[0] == dst_sride0; + res &= slice.dst.stride[1] == dst_sride1; + res &= slice.dst.stride[2] == dst_sride2; + res &= slice.src.offset - last_src_offset == src_offset; + res &= slice.dst.offset - last_dst_offset == dst_offset; + if(res){ + canCombineNum++; + // change canCombineNum + mCombineInfo.back().mSrc_offset = src_offset; + mCombineInfo.back().mDst_offset = dst_offset; + mCombineInfo.back().mCanCombineNum = canCombineNum; + } else{ + origin = slice.origin; + size0 = slice.size[0]; + size1 = slice.size[1]; + size2 = slice.size[2]; + src_sride0 = slice.src.stride[0]; + src_sride1 = slice.src.stride[1]; + src_sride2 = slice.src.stride[2]; + dst_sride0 = slice.dst.stride[0]; + dst_sride1 = slice.dst.stride[1]; + dst_sride2 = slice.dst.stride[2]; + // recover + canCombineNum = 1; + // push back + mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1)); + + } } + last_src_offset = slice.src.offset; + last_dst_offset = slice.dst.offset; } - return res; } REGISTER_OPENCL_OP_CREATOR(RasterBufCreator, OpType_Raster, BUFFER); diff --git a/source/backend/opencl/execution/buffer/RasterBufExecution.hpp b/source/backend/opencl/execution/buffer/RasterBufExecution.hpp index e704f0716..b19ad2b36 100644 --- a/source/backend/opencl/execution/buffer/RasterBufExecution.hpp +++ b/source/backend/opencl/execution/buffer/RasterBufExecution.hpp @@ -17,18 +17,29 @@ namespace OpenCL { class RasterBufExecution : public CommonExecution { public: + class CanCombineInfo { + public: + CanCombineInfo(const Tensor::InsideDescribe::Region region, const int src_offset, const int dst_offset, const int canCombineNum) : mRegion(region), mSrc_offset(src_offset), mDst_offset(dst_offset), mCanCombineNum(canCombineNum){ + // Do nothing + } + int mSrc_offset; + int mDst_offset; + int mCanCombineNum; + Tensor::InsideDescribe::Region mRegion; + }; RasterBufExecution(const std::vector &inputs, const MNN::Op *op, Backend *backend); virtual ~RasterBufExecution() = default; virtual ErrorCode onEncode(const std::vector &inputs, const std::vector &outputs) override; private: - bool CanCombine(const std::vector &outputs); + void CanCombine(const std::vector &outputs); std::map mTempInput; cl::Buffer *mTempOutput; OpenCLBackend *mOpenCLBackend; bool mNeedZero = false; bool mFast = false; + std::vector mCombineInfo; }; } // namespace OpenCL diff --git a/source/backend/opencl/execution/cl/attention_buf.cl b/source/backend/opencl/execution/cl/attention_buf.cl index 5b7366018..3938ce20b 100644 --- a/source/backend/opencl/execution/cl/attention_buf.cl +++ b/source/backend/opencl/execution/cl/attention_buf.cl @@ -62,7 +62,8 @@ __kernel void rearrange_qkv(GLOBAL_SIZE_3_DIMS __global FLOAT *past_v, // [batch, headNum/group, seqLenKV_4, headDim] __private const int4 tile, // [mTileQ, mTileKV, mTileHDK, mTileHDN] __private const int4 shape,// [seqLenQ, seqLenKV, headNum, headDim] - __private const int4 param // [group, batch, max_len, past_len] + __private const int4 param, // [group, batch, max_len, past_len] + __private const int maxLenKV ) { const int sl = get_global_id(0); // seqLen/4 : max(seqLenPackQ/4, seqLenPackKV/4) const int hd = get_global_id(1); // headDim/4 : max(headDimPackQK/4, headDimPackV/4) @@ -75,7 +76,6 @@ __kernel void rearrange_qkv(GLOBAL_SIZE_3_DIMS const int headDim = shape.w; const int group = param.x; const int batch = param.y; - const int maxLenKV = param.z; const int b = z % batch; const int hn = z / batch; diff --git a/source/backend/opencl/execution/cl/buffer_convert_quant.cl b/source/backend/opencl/execution/cl/buffer_convert_quant.cl index c6df69567..3bad6c053 100644 --- a/source/backend/opencl/execution/cl/buffer_convert_quant.cl +++ b/source/backend/opencl/execution/cl/buffer_convert_quant.cl @@ -224,4 +224,47 @@ __kernel void conv2d_1x1_weight_quant_buffer(GLOBAL_SIZE_2_DIMS #endif } +__kernel void conv2d_1x1_ic_oc_weight_quant_buffer(GLOBAL_SIZE_2_DIMS +#ifdef USE_LOW_BIT_WEIGHT_INT4 + __global const uchar *input_ptr, + __global uchar *output_ptr, //(Ci/packCin， Co/packCout, packCin， packCout) +#else + __global const char *input_ptr, + __global char *output_ptr, //(Ci/packCin， Co/packCout, packCin， packCout) +#endif + __private const int input_channel, + __private const int output_channel, + __private const int icPack, + __private const int ocPack) { + int x = get_global_id(0); // ic / icPack + int y = get_global_id(1); // oc / ocPack + DEAL_NON_UNIFORM_DIM2(x, y); + const int xin = x * icPack; + const int yin = y * ocPack; + const int inputChannelC4 = (input_channel + icPack - 1) / icPack; + const int outputChannelC4 = (output_channel + ocPack - 1) / ocPack; +#ifdef USE_LOW_BIT_WEIGHT_INT4 + const int inputOffset = (yin * input_channel + xin) / 2; + const int outputOffset = ((x * outputChannelC4 + y) * icPack * ocPack) / 2; + for(int i = 0; i < icPack; ++i){ + for(int j = 0; j < ocPack / 2; ++j){ + int index0 = (yin + j * 2) * input_channel + xin + i; + int index1 = (yin + j * 2 + 1) * input_channel + xin + i; + uchar s0 = input_ptr[index0/2]; + uchar s1 = input_ptr[index1/2]; + s0 = (index0 % 2) == 0 ? (s0 & 0xf0) : ((s0 & 0x0f) << 4); + s1 = (index1 % 2) == 0 ? (s1 >> 4) : (s1 & 0x0f); + output_ptr[outputOffset + i * (ocPack / 2) + j] = s0 | s1; + } + } +#else + const int inputOffset = yin * input_channel + xin; + const int outputOffset = (x * outputChannelC4 + y) * icPack * ocPack; + for(int i = 0; i < icPack; ++i){ + for(int j = 0; j < ocPack; ++j){ + output_ptr[outputOffset + i * ocPack + j] = input_ptr[inputOffset + j * input_channel + i]; + } + } +#endif +} diff --git a/source/backend/opencl/execution/cl/conv_2d_int_buf.cl b/source/backend/opencl/execution/cl/conv_2d_int_buf.cl index f482f578d..42b595308 100644 --- a/source/backend/opencl/execution/cl/conv_2d_int_buf.cl +++ b/source/backend/opencl/execution/cl/conv_2d_int_buf.cl @@ -28,7 +28,7 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -43,7 +43,8 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -69,10 +70,14 @@ void conv_2d_int_c4h1w1(GLOBAL_SIZE_2_DIMS const int weight_oc_offset = out_c_blocks * filter_hw.x * filter_hw.y * 4; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); COMPUTE_FLOAT4 scale = (COMPUTE_FLOAT4)(ScaleOffset.s0, ScaleOffset.s2, ScaleOffset.s4, ScaleOffset.s6); COMPUTE_FLOAT4 offset = (COMPUTE_FLOAT4)(ScaleOffset.s1, ScaleOffset.s3, ScaleOffset.s5, ScaleOffset.s7); + #else + COMPUTE_FLOAT4 scale = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + COMPUTE_FLOAT4 offset = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx*kh*kw + kh_start*kw + kw_start, 0] int weight_offset = ((((4*in_c_idx+0)* out_c_blocks + out_c_idx) *filter_hw.x + kh_start)*filter_hw.y + kw_start) * 4; @@ -155,7 +160,7 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -170,7 +175,8 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks,//generate width's num __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -196,10 +202,14 @@ void conv_2d_int_c4h1w2(GLOBAL_SIZE_2_DIMS const int weight_oc_offset = out_c_blocks * filter_hw.x * filter_hw.y * 4; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); COMPUTE_FLOAT4 scale = (COMPUTE_FLOAT4)(ScaleOffset.s0, ScaleOffset.s2, ScaleOffset.s4, ScaleOffset.s6); COMPUTE_FLOAT4 offset = (COMPUTE_FLOAT4)(ScaleOffset.s1, ScaleOffset.s3, ScaleOffset.s5, ScaleOffset.s7); + #else + COMPUTE_FLOAT4 scale = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + COMPUTE_FLOAT4 offset = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx*kh*kw + kh_start*kw + kw_start, 0] int weight_offset = ((((4*in_c_idx+0)* out_c_blocks + out_c_idx) *filter_hw.x + kh_start)*filter_hw.y + 0) * 4; @@ -298,7 +308,7 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -313,7 +323,8 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -343,10 +354,14 @@ void conv_2d_int_c4h1w4(GLOBAL_SIZE_2_DIMS const int weight_oc_offset = out_c_blocks * filter_hw.x * filter_hw.y * 4; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); COMPUTE_FLOAT4 scale = (COMPUTE_FLOAT4)(ScaleOffset.s0, ScaleOffset.s2, ScaleOffset.s4, ScaleOffset.s6); COMPUTE_FLOAT4 offset = (COMPUTE_FLOAT4)(ScaleOffset.s1, ScaleOffset.s3, ScaleOffset.s5, ScaleOffset.s7); + #else + COMPUTE_FLOAT4 scale = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + COMPUTE_FLOAT4 offset = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx*kh*kw + kh_start*kw + kw_start, 0] int weight_offset = ((((4*in_c_idx+0)* out_c_blocks + out_c_idx) *filter_hw.x + kh_start)*filter_hw.y + 0) * 4; @@ -472,7 +487,7 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -487,7 +502,8 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -518,10 +534,14 @@ void conv_2d_int_c4h4w1(GLOBAL_SIZE_2_DIMS const int weight_oc_offset = out_c_blocks * filter_hw.x * filter_hw.y * 4; const int in_hw_size = in_hw.x * in_hw.y; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); COMPUTE_FLOAT4 scale = (COMPUTE_FLOAT4)(ScaleOffset.s0, ScaleOffset.s2, ScaleOffset.s4, ScaleOffset.s6); COMPUTE_FLOAT4 offset = (COMPUTE_FLOAT4)(ScaleOffset.s1, ScaleOffset.s3, ScaleOffset.s5, ScaleOffset.s7); + #else + COMPUTE_FLOAT4 scale = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + COMPUTE_FLOAT4 offset = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx*kh*kw + kh_start*kw + kw_start, 0] const int inp_offset_base = (out_b_idx + in_c_idx*batch) * in_hw.x * in_hw.y * 4; @@ -653,7 +673,7 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -668,7 +688,8 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -707,17 +728,26 @@ void conv_2d_int_c8h4w1(GLOBAL_SIZE_2_DIMS const int weight_ic_offset = out_c_blocks * weight_oc_offset; const int in_hw_size = in_hw.x * in_hw.y; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_0, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #ifdef CHANNEL_BOUNDARY_PROTECT - COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #else - COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #endif COMPUTE_FLOAT4 scale0 = (COMPUTE_FLOAT4)(ScaleOffset0.s0, ScaleOffset0.s2, ScaleOffset0.s4, ScaleOffset0.s6); COMPUTE_FLOAT4 offset0 = (COMPUTE_FLOAT4)(ScaleOffset0.s1, ScaleOffset0.s3, ScaleOffset0.s5, ScaleOffset0.s7); COMPUTE_FLOAT4 scale1 = (COMPUTE_FLOAT4)(ScaleOffset1.s0, ScaleOffset1.s2, ScaleOffset1.s4, ScaleOffset1.s6); COMPUTE_FLOAT4 offset1 = (COMPUTE_FLOAT4)(ScaleOffset1.s1, ScaleOffset1.s3, ScaleOffset1.s5, ScaleOffset1.s7); + #else + COMPUTE_FLOAT4 scale0 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #ifdef CHANNEL_BOUNDARY_PROTECT + COMPUTE_FLOAT4 scale1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #else + COMPUTE_FLOAT4 scale1 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #endif + COMPUTE_FLOAT4 offset0 = 0, offset1 = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx_0*kh*kw + kh_start*kw + kw_start, 0] const int inp_offset_base = (out_b_idx + in_c_idx*batch) * in_hw.x * in_hw.y * 4; @@ -956,7 +986,7 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -971,7 +1001,8 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -1005,17 +1036,26 @@ void conv_2d_int_c8h2w1(GLOBAL_SIZE_2_DIMS const int in_hw_size = in_hw.x * in_hw.y; // weight: [ic/4, oc, 4], loop: ic/4 for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_0, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #ifdef CHANNEL_BOUNDARY_PROTECT - COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #else - COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #endif COMPUTE_FLOAT4 scale0 = (COMPUTE_FLOAT4)(ScaleOffset0.s0, ScaleOffset0.s2, ScaleOffset0.s4, ScaleOffset0.s6); COMPUTE_FLOAT4 offset0 = (COMPUTE_FLOAT4)(ScaleOffset0.s1, ScaleOffset0.s3, ScaleOffset0.s5, ScaleOffset0.s7); COMPUTE_FLOAT4 scale1 = (COMPUTE_FLOAT4)(ScaleOffset1.s0, ScaleOffset1.s2, ScaleOffset1.s4, ScaleOffset1.s6); COMPUTE_FLOAT4 offset1 = (COMPUTE_FLOAT4)(ScaleOffset1.s1, ScaleOffset1.s3, ScaleOffset1.s5, ScaleOffset1.s7); + #else + COMPUTE_FLOAT4 scale0 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #ifdef CHANNEL_BOUNDARY_PROTECT + COMPUTE_FLOAT4 scale1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #else + COMPUTE_FLOAT4 scale1 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #endif + COMPUTE_FLOAT4 offset0 = 0, offset1 = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx_0*kh*kw + kh_start*kw + kw_start, 0] const int inp_offset_base = (out_b_idx + in_c_idx*batch) * in_hw.x * in_hw.y * 4; @@ -1197,7 +1237,7 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT *output, __private const int2 in_hw, @@ -1212,7 +1252,8 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS __private const int out_w_blocks, __private const int out_c_blocks, __private const int out_h_blocks, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int out_c_w_idx = get_global_id(0); //c/4 w const int out_b_h_idx = get_global_id(1); //b h @@ -1250,17 +1291,26 @@ void conv_2d_int_c8h1w4(GLOBAL_SIZE_2_DIMS const int weight_oc_offset = filter_hw.x * filter_hw.y * 4; const int weight_ic_offset = out_c_blocks * weight_oc_offset; for(ushort in_c_idx = 0; in_c_idx < in_c_blocks; in_c_idx++) { - int kindex = (in_c_idx * 4) / blockDim * out_c_blocks * 8; - COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_0, dequantScaleOffset + kindex)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT8 ScaleOffset0 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #ifdef CHANNEL_BOUNDARY_PROTECT - COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #else - COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1, dequantScaleOffset + kindex)); + COMPUTE_FLOAT8 ScaleOffset1 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 8)) / coef); #endif COMPUTE_FLOAT4 scale0 = (COMPUTE_FLOAT4)(ScaleOffset0.s0, ScaleOffset0.s2, ScaleOffset0.s4, ScaleOffset0.s6); COMPUTE_FLOAT4 offset0 = (COMPUTE_FLOAT4)(ScaleOffset0.s1, ScaleOffset0.s3, ScaleOffset0.s5, ScaleOffset0.s7); COMPUTE_FLOAT4 scale1 = (COMPUTE_FLOAT4)(ScaleOffset1.s0, ScaleOffset1.s2, ScaleOffset1.s4, ScaleOffset1.s6); COMPUTE_FLOAT4 offset1 = (COMPUTE_FLOAT4)(ScaleOffset1.s1, ScaleOffset1.s3, ScaleOffset1.s5, ScaleOffset1.s7); + #else + COMPUTE_FLOAT4 scale0 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #ifdef CHANNEL_BOUNDARY_PROTECT + COMPUTE_FLOAT4 scale1 = out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #else + COMPUTE_FLOAT4 scale1 = CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1, dequantScaleOffset + (in_c_idx * 4) / blockDim * out_c_blocks * 4)) / coef); + #endif + COMPUTE_FLOAT4 offset0 = 0, offset1 = 0; + #endif //weights NC4HW4 [1, 4*icC4, ocC4*kh*kw, 1] xic4 //index: [0, 4*in_c_idx, out_c_idx_0*kh*kw + kh_start*kw + kw_start, 0] int weight_offset = ((((4*in_c_idx+0)* out_c_blocks + out_c_idx_0) *filter_hw.x + kh_start)*filter_hw.y + 0) * 4; diff --git a/source/backend/opencl/execution/cl/gemm_conv1x1_buf.cl b/source/backend/opencl/execution/cl/gemm_conv1x1_buf.cl index b8c503107..bdfaa22c7 100644 --- a/source/backend/opencl/execution/cl/gemm_conv1x1_buf.cl +++ b/source/backend/opencl/execution/cl/gemm_conv1x1_buf.cl @@ -24,12 +24,14 @@ __kernel void inverse_quant_weight(GLOBAL_SIZE_DIM2 __global const uchar *weight, #endif #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global FLOAT* output, + __private const int inputChannel, __private const int inputChannel4Align, __private const int outputChannelAlign, __private const int outputChannel4Align, - __private const int blockDim){ + __private const int blockDim, + __private const float coef){ const int x = get_global_id(0); //ic const int y = get_global_id(1); //oc @@ -40,13 +42,16 @@ __kernel void inverse_quant_weight(GLOBAL_SIZE_DIM2 const int oc = y << 3; const int output_offset = ic * outputChannelAlign + oc; - int kindex = (ic / blockDim) * outputChannel4Align * 2; + #ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 ScaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + kindex + oc * 2)); + COMPUTE_FLOAT16 ScaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + ((ic / blockDim) * outputChannel4Align + oc) * 2)) / coef); scale = ScaleOffset.s02468ace; offset = ScaleOffset.s13579bdf; } + #else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (ic / blockDim) * outputChannel4Align + oc)) / coef); + #endif COMPUTE_FLOAT8 weights0, weights1, weights2, weights3; { #ifdef USE_IMAGE @@ -55,34 +60,51 @@ __kernel void inverse_quant_weight(GLOBAL_SIZE_DIM2 uchar16 charWeightsInt40 = vload16(x, weight + y * inputChannel4Align * 4); #endif char8 charWeights0; + #ifdef ASYMMETRIC UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s0123); weights0 = CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s4567); - weights1 = CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + weights1 = ic + 1 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + + UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s89ab); + weights2 = ic + 2 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + + UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.scdef); + weights3 = ic + 3 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + #else + UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s0123); + weights0 = CONVERT_COMPUTE_FLOAT8(charWeights0) * scale; + + UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s4567); + weights1 = ic + 1 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale; UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.s89ab); - weights2 = CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + weights2 = ic + 2 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale; UCHAR4_TO_CHAR8(charWeights0, charWeightsInt40.scdef); - weights3 = CONVERT_COMPUTE_FLOAT8(charWeights0) * scale + offset; + weights3 = ic + 3 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0) * scale; + #endif } - vstore8(weights0, 0, output+output_offset); - vstore8(weights1, 0, output+output_offset+outputChannelAlign); - vstore8(weights2, 0, output+output_offset+2*outputChannelAlign); - vstore8(weights3, 0, output+output_offset+3*outputChannelAlign); + vstore8(CONVERT_FLOAT8(weights0), 0, output+output_offset); + vstore8(CONVERT_FLOAT8(weights1), 0, output+output_offset+outputChannelAlign); + vstore8(CONVERT_FLOAT8(weights2), 0, output+output_offset+2*outputChannelAlign); + vstore8(CONVERT_FLOAT8(weights3), 0, output+output_offset+3*outputChannelAlign); #else const int ic = x << 1; const int oc = y << 3; const int output_offset = ic * outputChannelAlign + oc; - - int kindex = (ic / blockDim) * outputChannel4Align * 2; + + #ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 ScaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + kindex + oc * 2)); + COMPUTE_FLOAT16 ScaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + ((ic / blockDim) * outputChannel4Align + oc) * 2)) / coef); scale = ScaleOffset.s02468ace; offset = ScaleOffset.s13579bdf; } + #else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (ic / blockDim) * outputChannel4Align + oc)) / coef); + #endif COMPUTE_FLOAT8 weights0, weights1; { #ifdef USE_IMAGE @@ -90,12 +112,17 @@ __kernel void inverse_quant_weight(GLOBAL_SIZE_DIM2 #else COMPUTE_FLOAT16 wei = CONVERT_COMPUTE_FLOAT16(vload16(x, weight + y * inputChannel4Align * 8)); #endif + #ifdef ASYMMETRIC weights0 = wei.s01234567 * scale + offset; - weights1 = wei.s89abcdef * scale + offset; + weights1 = ic + 1 >= inputChannel ? 0 : wei.s89abcdef * scale + offset; + #else + weights0 = wei.s01234567 * scale; + weights1 = ic + 1 >= inputChannel ? 0 : wei.s89abcdef * scale; + #endif } - vstore8(weights0, 0, output+output_offset); - vstore8(weights1, 0, output+output_offset+outputChannelAlign); -#endif + vstore8(CONVERT_FLOAT8(weights0), 0, output+output_offset); + vstore8(CONVERT_FLOAT8(weights1), 0, output+output_offset+outputChannelAlign); + #endif } #define UCHAR4_TO_FLOAT8(b, scale, offset) \ @@ -109,7 +136,6 @@ __kernel void inverse_quant_weight(GLOBAL_SIZE_DIM2 wei.s7 = (COMPUTE_FLOAT)((b.s3 & 15) - 8); \ wei = wei * scale + offset; - __kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2 __global const FLOAT* input, #ifdef USE_IMAGE @@ -117,14 +143,15 @@ __kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2 #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int bhw, __private const int dstChannelAlign, __private const int srcChannelAlign, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int x = get_global_id(0); //b/4 const int y = get_global_id(1); //c/8 @@ -133,7 +160,7 @@ __kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2 const int out_b_idx = x << 2; const int out_c_idx = y << 1; - COMPUTE_FLOAT8 out0 = CONVERT_FLOAT8(vload8(0, bias + (out_c_idx << 2))); + COMPUTE_FLOAT8 out0 = CONVERT_COMPUTE_FLOAT8(vload8(0, bias + (out_c_idx << 2))); COMPUTE_FLOAT8 out1 = out0; COMPUTE_FLOAT8 out2 = out0; COMPUTE_FLOAT8 out3 = out0; @@ -154,12 +181,17 @@ __kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2 #if INPUT_BATCH_LEAVES_NUM != 0 if(out_b_idx + 3 >= bhw){ for (int i = 0; i < blockNum; i++){ + #ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)); + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)) / coef); scale = scaleOffset.s02468ace; offset = scaleOffset.s13579bdf; } + #else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (out_c_idx << 2) + i * dstChannelAlign)) / coef); + COMPUTE_FLOAT8 offset = 0; + #endif for (int j = 0; j < loop_end; j++) { int k = i * loop + j; COMPUTE_FLOAT8 wei; @@ -272,12 +304,17 @@ __kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2 } else { #endif for (int i = 0; i < blockNum; i++){ + #ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)); + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)) / coef); scale = scaleOffset.s02468ace; offset = scaleOffset.s13579bdf; } + #else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (out_c_idx << 2) + i * dstChannelAlign)) / coef); + COMPUTE_FLOAT8 offset = 0; + #endif for (int j = 0; j < loop_end; j++) { int k = i * loop + j; COMPUTE_FLOAT8 wei; @@ -395,14 +432,15 @@ __kernel void gemm_b4_c8_int8_buf(GLOBAL_SIZE_DIM2 #else __global const char *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int bhw, __private const int dstChannelAlign, __private const int srcChannelAlign, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int x = get_global_id(0); //b/4 const int y = get_global_id(1); //c/8 @@ -411,7 +449,7 @@ __kernel void gemm_b4_c8_int8_buf(GLOBAL_SIZE_DIM2 const int out_b_idx = x << 2; const int out_c_idx = y << 1; - COMPUTE_FLOAT8 out0 = CONVERT_FLOAT8(vload8(0, bias + (out_c_idx << 2))); + COMPUTE_FLOAT8 out0 = CONVERT_COMPUTE_FLOAT8(vload8(0, bias + (out_c_idx << 2))); COMPUTE_FLOAT8 out1 = out0; COMPUTE_FLOAT8 out2 = out0; COMPUTE_FLOAT8 out3 = out0; @@ -434,9 +472,15 @@ __kernel void gemm_b4_c8_int8_buf(GLOBAL_SIZE_DIM2 for (int i = 0; i < blockNum; i++){ COMPUTE_FLOAT16 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)) / coef); scale = (COMPUTE_FLOAT16)(scaleOffset.s02468ace, scaleOffset.s02468ace); offset = (COMPUTE_FLOAT16)(scaleOffset.s13579bdf, scaleOffset.s13579bdf); + #else + scale.s01234567 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (out_c_idx << 2) + i * dstChannelAlign)) / coef); + scale.s89abcdef = scale.s01234567; + offset = 0; + #endif } for (int j = 0; j < loop_end; j++) { int k = i * loop + j; @@ -549,9 +593,15 @@ __kernel void gemm_b4_c8_int8_buf(GLOBAL_SIZE_DIM2 for (int i = 0; i < blockNum; i++){ COMPUTE_FLOAT16 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + (out_c_idx << 3) + i * dstChannelAlign * 2)) / coef); scale = (COMPUTE_FLOAT16)(scaleOffset.s02468ace, scaleOffset.s02468ace); offset = (COMPUTE_FLOAT16)(scaleOffset.s13579bdf, scaleOffset.s13579bdf); + #else + scale.s01234567 = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + (out_c_idx << 2) + i * dstChannelAlign)) / coef); + scale.s89abcdef = scale.s01234567; + offset = 0; + #endif } for (int j = 0; j < loop_end; j++) { int k = i * loop + j; diff --git a/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl b/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl index a2edaa419..306f65c41 100644 --- a/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl +++ b/source/backend/opencl/execution/cl/gemv_conv1x1_buf.cl @@ -31,14 +31,15 @@ __kernel void gemv_conv_c8_int4_buf(GLOBAL_SIZE_DIM_2 #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int dstChannelC4, __private const int srcChannelC4, __private const int srcChannel, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int lid = get_local_id(0); const int oc = get_global_id(1); //oc/8 const int oc8 = oc << 3; @@ -55,12 +56,17 @@ __kernel void gemv_conv_c8_int4_buf(GLOBAL_SIZE_DIM_2 for(int j = lid; j < loop; j+=WGS){ int k4 = j << 2; +#ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + (k4 / blockDim) * dstChannelC4 * 8)); + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + (k4 / blockDim) * dstChannelC4 * 8)) / coef); scale = scaleOffset.s02468ace; offset = scaleOffset.s13579bdf; } +#else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + (k4 / blockDim) * dstChannelC4 * 4)) / coef); + COMPUTE_FLOAT8 offset = 0; +#endif COMPUTE_FLOAT8 wei; COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(0, input + k4)); #ifdef USE_IMAGE @@ -88,14 +94,18 @@ __kernel void gemv_conv_c8_int4_buf(GLOBAL_SIZE_DIM_2 #if INPUT_CHANNEL_LEAVES_NUM != 0 { int k4 = loop << 2; +#ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + (k4 / blockDim) * dstChannelC4 * 8)); + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + (k4 / blockDim) * dstChannelC4 * 8)) / coef); scale = scaleOffset.s02468ace; offset = scaleOffset.s13579bdf; } +#else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + (k4 / blockDim) * dstChannelC4 * 4)) / coef); + COMPUTE_FLOAT8 offset = 0; +#endif COMPUTE_FLOAT8 wei; - COMPUTE_FLOAT4 in = CONVERT_COMPUTE_FLOAT4(vload4(0, input + k4)); #ifdef USE_IMAGE uchar16 charWeightsInt40 = as_uchar16(read_imagei(weight, SAMPLER, (int2)(j, oc))); #else @@ -146,14 +156,15 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 #else __global const char *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int dstChannelC4, __private const int srcChannelC4, __private const int srcChannel, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int lid = get_local_id(0); const int oc = get_global_id(1); //oc/8 const int oc8 = oc << 3; @@ -171,9 +182,15 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 int k2 = j << 1; COMPUTE_FLOAT16 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + (k2 / blockDim) * dstChannelC4 * 8)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + (k2 / blockDim) * dstChannelC4 * 8)) / coef); scale = (COMPUTE_FLOAT16)(scaleOffset.s02468ace, scaleOffset.s02468ace); offset = (COMPUTE_FLOAT16)(scaleOffset.s13579bdf, scaleOffset.s13579bdf); + #else + COMPUTE_FLOAT8 scaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + (k2 / blockDim) * dstChannelC4 * 4)) / coef); + scale = (COMPUTE_FLOAT16)(scaleOffset, scaleOffset); + offset = 0; + #endif } COMPUTE_FLOAT2 in = CONVERT_COMPUTE_FLOAT2(vload2(0, input + k2)); #ifdef USE_IMAGE @@ -191,12 +208,17 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 #if INPUT_CHANNEL_LEAVES_NUM != 0 { int k2 = loop << 1; - COMPUTE_FLOAT8 scale, offset; COMPUTE_FLOAT16 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + (k2 / blockDim) * dstChannelC4 * 8)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + (k2 / blockDim) * dstChannelC4 * 8)) / coef); scale = (COMPUTE_FLOAT16)(scaleOffset.s02468ace, scaleOffset.s02468ace); offset = (COMPUTE_FLOAT16)(scaleOffset.s13579bdf, scaleOffset.s13579bdf); + #else + COMPUTE_FLOAT8 scaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + (k2 / blockDim) * dstChannelC4 * 4)) / coef); + scale = (COMPUTE_FLOAT16)(scaleOffset, scaleOffset); + offset = 0; + #endif } #ifdef USE_IMAGE COMPUTE_FLOAT16 wei = CONVERT_COMPUTE_FLOAT16(as_char16(read_imagei(weight, SAMPLER, (int2)(j, oc)))) * scale + offset; @@ -204,7 +226,7 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 COMPUTE_FLOAT16 wei = CONVERT_COMPUTE_FLOAT16(vload16(j, weight + weight_offset)) * scale + offset; #endif { - out0 = mad((COMPUTE_FLOAT8)in[k2], wei.s01234567, out0); + out0 = mad((COMPUTE_FLOAT8)input[k2], wei.s01234567, out0); } } #endif @@ -235,14 +257,15 @@ __kernel void gemv_conv_c8_int4_buf(GLOBAL_SIZE_DIM_2 #else __global const uchar *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int dstChannelC4, __private const int srcChannelC4, __private const int srcChannel, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int ic = get_global_id(0); const int oc = get_global_id(1); //oc/8 @@ -260,12 +283,17 @@ __kernel void gemv_conv_c8_int4_buf(GLOBAL_SIZE_DIM_2 const int weight_offset = oc * srcChannelC4 * 16; #endif for (int i = 0; i < blockNum; i++){ +#ifdef ASYMMETRIC COMPUTE_FLOAT8 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + i * dstChannelC4 * 8)); + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + i * dstChannelC4 * 8)) / coef); scale = scaleOffset.s02468ace; offset = scaleOffset.s13579bdf; } +#else + COMPUTE_FLOAT8 scale = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + i * dstChannelC4 * 4)) / coef); + COMPUTE_FLOAT8 offset = 0; +#endif for (int j = 0; j < loop_end; j++) { int k = i * loop + j; COMPUTE_FLOAT8 wei; @@ -338,14 +366,15 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 #else __global const char *weight, #endif - __global const float *dequantScaleOffset, + __global const FLOAT *dequantScaleOffset, __global const FLOAT *bias, __global FLOAT* output, __private const int dstChannelC4, __private const int srcChannelC4, __private const int srcChannel, __private const int blockNum, - __private const int blockDim) { + __private const int blockDim, + __private const float coef) { const int ic = get_global_id(0); const int oc = get_global_id(1); //oc/8 UNIFORM_BOUNDRY_CHECK_2(ic, oc); @@ -363,9 +392,15 @@ __kernel void gemv_conv_c8_int8_buf(GLOBAL_SIZE_DIM_2 for (int i = 0; i < blockNum; i++){ COMPUTE_FLOAT16 scale, offset; { - COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(vload16(0, dequantScaleOffset + oc8 * 2 + i * dstChannelC4 * 8)); + #ifdef ASYMMETRIC + COMPUTE_FLOAT16 scaleOffset = CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0, dequantScaleOffset + oc8 * 2 + i * dstChannelC4 * 8)) / coef); scale = (COMPUTE_FLOAT16)(scaleOffset.s02468ace, scaleOffset.s02468ace); offset = (COMPUTE_FLOAT16)(scaleOffset.s13579bdf, scaleOffset.s13579bdf); + #else + COMPUTE_FLOAT8 scaleOffset = CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0, dequantScaleOffset + oc8 + i * dstChannelC4 * 4)) / coef); + scale = (COMPUTE_FLOAT16)(scaleOffset, scaleOffset); + offset = 0; + #endif } for (int j = 0; j < loop_end; j++) { int k = i * loop + j; diff --git a/source/backend/opencl/execution/cl/opencl_program.cc b/source/backend/opencl/execution/cl/opencl_program.cc index 7ea6914fe..a2abb3fad 100644 --- a/source/backend/opencl/execution/cl/opencl_program.cc +++ b/source/backend/opencl/execution/cl/opencl_program.cc @@ -2506,14 +2506,15 @@ const char* gemv_conv1x1_buf = "#else\n" " __global const uchar *weight,\n" "#endif\n" -" __global const float *dequantScaleOffset,\n" +" __global const FLOAT *dequantScaleOffset,\n" " __global const FLOAT *bias,\n" " __global FLOAT* output,\n" " __private const int dstChannelC4,\n" " __private const int srcChannelC4,\n" " __private const int srcChannel,\n" " __private const int blockNum,\n" -" __private const int blockDim) {\n" +" __private const int blockDim,\n" +" __private const float coef) {\n" " const int lid=get_local_id(0);\n" " const int oc=get_global_id(1); //oc/8\n" " const int oc8=oc << 3;\n" @@ -2530,12 +2531,17 @@ const char* gemv_conv1x1_buf = " \n" " for(int j=lid; j= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #else\n" -" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #endif\n" " COMPUTE_FLOAT4 scale0=(COMPUTE_FLOAT4)(ScaleOffset0.s0,ScaleOffset0.s2,ScaleOffset0.s4,ScaleOffset0.s6);\n" " COMPUTE_FLOAT4 offset0=(COMPUTE_FLOAT4)(ScaleOffset0.s1,ScaleOffset0.s3,ScaleOffset0.s5,ScaleOffset0.s7);\n" " COMPUTE_FLOAT4 scale1=(COMPUTE_FLOAT4)(ScaleOffset1.s0,ScaleOffset1.s2,ScaleOffset1.s4,ScaleOffset1.s6);\n" " COMPUTE_FLOAT4 offset1=(COMPUTE_FLOAT4)(ScaleOffset1.s1,ScaleOffset1.s3,ScaleOffset1.s5,ScaleOffset1.s7);\n" +" #else\n" +" COMPUTE_FLOAT4 scale0=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #ifdef CHANNEL_BOUNDARY_PROTECT\n" +" COMPUTE_FLOAT4 scale1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #else\n" +" COMPUTE_FLOAT4 scale1=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #endif\n" +" COMPUTE_FLOAT4 offset0=0,offset1=0;\n" +" #endif\n" " //weights NC4HW4 [1,4*icC4,ocC4*kh*kw,1] xic4\n" " //index: [0,4*in_c_idx,out_c_idx_0*kh*kw+kh_start*kw+kw_start,0]\n" " const int inp_offset_base=(out_b_idx+in_c_idx*batch)*in_hw.x*in_hw.y*4;\n" @@ -5090,7 +5155,7 @@ const char* conv_2d_int_buf = "#else\n" " __global const uchar *weight,\n" "#endif\n" -" __global const float *dequantScaleOffset,\n" +" __global const FLOAT *dequantScaleOffset,\n" " __global const FLOAT *bias,\n" " __global FLOAT *output,\n" " __private const int2 in_hw,\n" @@ -5105,7 +5170,8 @@ const char* conv_2d_int_buf = " __private const int out_w_blocks,\n" " __private const int out_c_blocks,\n" " __private const int out_h_blocks,\n" -" __private const int blockDim) {\n" +" __private const int blockDim,\n" +" __private const float coef) {\n" " const int out_c_w_idx=get_global_id(0); //c/4 w\n" " const int out_b_h_idx=get_global_id(1); //b h\n" " DEAL_NON_UNIFORM_DIM2(out_c_w_idx,out_b_h_idx);\n" @@ -5134,17 +5200,26 @@ const char* conv_2d_int_buf = " const int in_hw_size=in_hw.x*in_hw.y;\n" " // weight: [ic/4,oc,4],loop: ic/4\n" " for(ushort in_c_idx=0; in_c_idx= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #else\n" -" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #endif\n" " COMPUTE_FLOAT4 scale0=(COMPUTE_FLOAT4)(ScaleOffset0.s0,ScaleOffset0.s2,ScaleOffset0.s4,ScaleOffset0.s6);\n" " COMPUTE_FLOAT4 offset0=(COMPUTE_FLOAT4)(ScaleOffset0.s1,ScaleOffset0.s3,ScaleOffset0.s5,ScaleOffset0.s7);\n" " COMPUTE_FLOAT4 scale1=(COMPUTE_FLOAT4)(ScaleOffset1.s0,ScaleOffset1.s2,ScaleOffset1.s4,ScaleOffset1.s6);\n" " COMPUTE_FLOAT4 offset1=(COMPUTE_FLOAT4)(ScaleOffset1.s1,ScaleOffset1.s3,ScaleOffset1.s5,ScaleOffset1.s7);\n" +" #else\n" +" COMPUTE_FLOAT4 scale0=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #ifdef CHANNEL_BOUNDARY_PROTECT\n" +" COMPUTE_FLOAT4 scale1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #else\n" +" COMPUTE_FLOAT4 scale1=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #endif\n" +" COMPUTE_FLOAT4 offset0=0,offset1=0;\n" +" #endif\n" " //weights NC4HW4 [1,4*icC4,ocC4*kh*kw,1] xic4\n" " //index: [0,4*in_c_idx,out_c_idx_0*kh*kw+kh_start*kw+kw_start,0]\n" " const int inp_offset_base=(out_b_idx+in_c_idx*batch)*in_hw.x*in_hw.y*4;\n" @@ -5320,7 +5395,7 @@ const char* conv_2d_int_buf = "#else\n" " __global const uchar *weight,\n" "#endif\n" -" __global const float *dequantScaleOffset,\n" +" __global const FLOAT *dequantScaleOffset,\n" " __global const FLOAT *bias,\n" " __global FLOAT *output,\n" " __private const int2 in_hw,\n" @@ -5335,7 +5410,8 @@ const char* conv_2d_int_buf = " __private const int out_w_blocks,\n" " __private const int out_c_blocks,\n" " __private const int out_h_blocks,\n" -" __private const int blockDim) {\n" +" __private const int blockDim,\n" +" __private const float coef) {\n" " const int out_c_w_idx=get_global_id(0); //c/4 w\n" " const int out_b_h_idx=get_global_id(1); //b h\n" " DEAL_NON_UNIFORM_DIM2(out_c_w_idx,out_b_h_idx);\n" @@ -5369,17 +5445,26 @@ const char* conv_2d_int_buf = " const int weight_oc_offset=filter_hw.x*filter_hw.y*4;\n" " const int weight_ic_offset=out_c_blocks*weight_oc_offset;\n" " for(ushort in_c_idx=0; in_c_idx= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT8)0 : CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #else\n" -" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(vload8(out_c_idx_1,dequantScaleOffset+kindex));\n" +" COMPUTE_FLOAT8 ScaleOffset1=CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*8))/coef);\n" " #endif\n" " COMPUTE_FLOAT4 scale0=(COMPUTE_FLOAT4)(ScaleOffset0.s0,ScaleOffset0.s2,ScaleOffset0.s4,ScaleOffset0.s6);\n" " COMPUTE_FLOAT4 offset0=(COMPUTE_FLOAT4)(ScaleOffset0.s1,ScaleOffset0.s3,ScaleOffset0.s5,ScaleOffset0.s7);\n" " COMPUTE_FLOAT4 scale1=(COMPUTE_FLOAT4)(ScaleOffset1.s0,ScaleOffset1.s2,ScaleOffset1.s4,ScaleOffset1.s6);\n" " COMPUTE_FLOAT4 offset1=(COMPUTE_FLOAT4)(ScaleOffset1.s1,ScaleOffset1.s3,ScaleOffset1.s5,ScaleOffset1.s7);\n" +" #else\n" +" COMPUTE_FLOAT4 scale0=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_0,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #ifdef CHANNEL_BOUNDARY_PROTECT\n" +" COMPUTE_FLOAT4 scale1=out_c_idx_1 >= out_c_blocks ? (COMPUTE_FLOAT4)0 : CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #else\n" +" COMPUTE_FLOAT4 scale1=CONVERT_COMPUTE_FLOAT4(convert_float4(vload4(out_c_idx_1,dequantScaleOffset+(in_c_idx*4)/blockDim*out_c_blocks*4))/coef);\n" +" #endif\n" +" COMPUTE_FLOAT4 offset0=0,offset1=0;\n" +" #endif\n" " //weights NC4HW4 [1,4*icC4,ocC4*kh*kw,1] xic4\n" " //index: [0,4*in_c_idx,out_c_idx_0*kh*kw+kh_start*kw+kw_start,0]\n" " int weight_offset=((((4*in_c_idx+0)* out_c_blocks+out_c_idx_0) *filter_hw.x+kh_start)*filter_hw.y+0)*4;\n" @@ -10601,6 +10686,49 @@ const char* buffer_convert_quant = " vstore16(out1,0,output_ptr+outputOffset+16);\n" "#endif\n" "}\n" +"__kernel void conv2d_1x1_ic_oc_weight_quant_buffer(GLOBAL_SIZE_2_DIMS\n" +"#ifdef USE_LOW_BIT_WEIGHT_INT4\n" +" __global const uchar *input_ptr,\n" +" __global uchar *output_ptr,//(Ci/packCin， Co/packCout,packCin， packCout)\n" +"#else\n" +" __global const char *input_ptr,\n" +" __global char *output_ptr,//(Ci/packCin， Co/packCout,packCin， packCout)\n" +"#endif\n" +" __private const int input_channel,\n" +" __private const int output_channel,\n" +" __private const int icPack,\n" +" __private const int ocPack) {\n" +" int x=get_global_id(0); // ic/icPack\n" +" int y=get_global_id(1); // oc/ocPack\n" +" DEAL_NON_UNIFORM_DIM2(x,y);\n" +" const int xin=x*icPack;\n" +" const int yin=y*ocPack;\n" +" const int inputChannelC4=(input_channel+icPack-1)/icPack;\n" +" const int outputChannelC4=(output_channel+ocPack-1)/ocPack;\n" +"#ifdef USE_LOW_BIT_WEIGHT_INT4\n" +" const int inputOffset=(yin*input_channel+xin)/2;\n" +" const int outputOffset=((x*outputChannelC4+y)*icPack*ocPack)/2;\n" +" for(int i=0; i> 4) : (s1 & 0x0f);\n" +" output_ptr[outputOffset+i*(ocPack/2)+j]=s0 | s1;\n" +" }\n" +" }\n" +"#else\n" +" const int inputOffset=yin*input_channel+xin;\n" +" const int outputOffset=(x*outputChannelC4+y)*icPack*ocPack;\n" +" for(int i=0; i= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale+offset;\n" +" \n" +" UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.s89ab);\n" +" weights2=ic+2 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale+offset;\n" +" \n" +" UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.scdef);\n" +" weights3=ic+3 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale+offset;\n" +" #else\n" +" UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.s0123);\n" +" weights0=CONVERT_COMPUTE_FLOAT8(charWeights0)*scale;\n" +" \n" +" UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.s4567);\n" +" weights1=ic+1 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale;\n" " \n" " UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.s89ab);\n" -" weights2=CONVERT_COMPUTE_FLOAT8(charWeights0)*scale+offset;\n" +" weights2=ic+2 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale;\n" " \n" " UCHAR4_TO_CHAR8(charWeights0,charWeightsInt40.scdef);\n" -" weights3=CONVERT_COMPUTE_FLOAT8(charWeights0)*scale+offset;\n" +" weights3=ic+3 >= inputChannel ? 0 : CONVERT_COMPUTE_FLOAT8(charWeights0)*scale;\n" +" #endif\n" " }\n" -" vstore8(weights0,0,output+output_offset);\n" -" vstore8(weights1,0,output+output_offset+outputChannelAlign);\n" -" vstore8(weights2,0,output+output_offset+2*outputChannelAlign);\n" -" vstore8(weights3,0,output+output_offset+3*outputChannelAlign);\n" +" vstore8(CONVERT_FLOAT8(weights0),0,output+output_offset);\n" +" vstore8(CONVERT_FLOAT8(weights1),0,output+output_offset+outputChannelAlign);\n" +" vstore8(CONVERT_FLOAT8(weights2),0,output+output_offset+2*outputChannelAlign);\n" +" vstore8(CONVERT_FLOAT8(weights3),0,output+output_offset+3*outputChannelAlign);\n" "#else\n" " const int ic=x << 1;\n" " const int oc=y << 3;\n" " const int output_offset=ic*outputChannelAlign+oc;\n" -" int kindex=(ic/blockDim)*outputChannel4Align*2;\n" +" \n" +" #ifdef ASYMMETRIC\n" " COMPUTE_FLOAT8 scale,offset;\n" " {\n" -" COMPUTE_FLOAT16 ScaleOffset=CONVERT_COMPUTE_FLOAT16(vload16(0,dequantScaleOffset+kindex+oc*2));\n" +" COMPUTE_FLOAT16 ScaleOffset=CONVERT_COMPUTE_FLOAT16(convert_float16(vload16(0,dequantScaleOffset+((ic/blockDim)*outputChannel4Align+oc)*2))/coef);\n" " scale=ScaleOffset.s02468ace;\n" " offset=ScaleOffset.s13579bdf;\n" " }\n" +" #else\n" +" COMPUTE_FLOAT8 scale=CONVERT_COMPUTE_FLOAT8(convert_float8(vload8(0,dequantScaleOffset+(ic/blockDim)*outputChannel4Align+oc))/coef);\n" +" #endif\n" " COMPUTE_FLOAT8 weights0,weights1;\n" " {\n" " #ifdef USE_IMAGE\n" @@ -20609,12 +20760,17 @@ const char* gemm_conv1x1_buf = " #else\n" " COMPUTE_FLOAT16 wei=CONVERT_COMPUTE_FLOAT16(vload16(x,weight+y*inputChannel4Align*8));\n" " #endif\n" +" #ifdef ASYMMETRIC\n" " weights0=wei.s01234567*scale+offset;\n" -" weights1=wei.s89abcdef*scale+offset;\n" +" weights1=ic+1 >= inputChannel ? 0 : wei.s89abcdef*scale+offset;\n" +" #else\n" +" weights0=wei.s01234567*scale;\n" +" weights1=ic+1 >= inputChannel ? 0 : wei.s89abcdef*scale;\n" +" #endif\n" " }\n" -" vstore8(weights0,0,output+output_offset);\n" -" vstore8(weights1,0,output+output_offset+outputChannelAlign);\n" -"#endif\n" +" vstore8(CONVERT_FLOAT8(weights0),0,output+output_offset);\n" +" vstore8(CONVERT_FLOAT8(weights1),0,output+output_offset+outputChannelAlign);\n" +" #endif\n" "}\n" "#define UCHAR4_TO_FLOAT8(b, scale, offset) "" wei.s0 = (COMPUTE_FLOAT)((b.s0 >> 4) - 8); "" wei.s1 = (COMPUTE_FLOAT)((b.s0 & 15) - 8); "" wei.s2 = (COMPUTE_FLOAT)((b.s1 >> 4) - 8); "" wei.s3 = (COMPUTE_FLOAT)((b.s1 & 15) - 8); "" wei.s4 = (COMPUTE_FLOAT)((b.s2 >> 4) - 8); "" wei.s5 = (COMPUTE_FLOAT)((b.s2 & 15) - 8); "" wei.s6 = (COMPUTE_FLOAT)((b.s3 >> 4) - 8); "" wei.s7 = (COMPUTE_FLOAT)((b.s3 & 15) - 8); "" wei=wei*scale+offset;\n" "__kernel void gemm_b4_c8_int4_buf(GLOBAL_SIZE_DIM2\n" @@ -20624,21 +20780,22 @@ const char* gemm_conv1x1_buf = "#else\n" " __global const uchar *weight,\n" "#endif\n" -" __global const float *dequantScaleOffset,\n" +" __global const FLOAT *dequantScaleOffset,\n" " __global const FLOAT *bias,\n" " __global FLOAT* output,\n" " __private const int bhw,\n" " __private const int dstChannelAlign,\n" " __private const int srcChannelAlign,\n" " __private const int blockNum,\n" -" __private const int blockDim) {\n" +" __private const int blockDim,\n" +" __private const float coef) {\n" " const int x=get_global_id(0); //b/4\n" " const int y=get_global_id(1); //c/8\n" " UNIFORM_BOUNDRY_CHECK(x,y);\n" " \n" " const int out_b_idx=x << 2;\n" " const int out_c_idx=y << 1;\n" -" COMPUTE_FLOAT8 out0=CONVERT_FLOAT8(vload8(0,bias+(out_c_idx << 2)));\n" +" COMPUTE_FLOAT8 out0=CONVERT_COMPUTE_FLOAT8(vload8(0,bias+(out_c_idx << 2)));\n" " COMPUTE_FLOAT8 out1=out0;\n" " COMPUTE_FLOAT8 out2=out0;\n" " COMPUTE_FLOAT8 out3=out0;\n" @@ -20658,12 +20815,17 @@ const char* gemm_conv1x1_buf = "#if INPUT_BATCH_LEAVES_NUM != 0\n" " if(out_b_idx+3 >= bhw){\n" " for (int i=0; i -#define MNN_OP_SUPPORT_LOG +// #define MNN_OP_SUPPORT_LOG //#define MNN_VULKAN_DUMP_MEMORY_USAGE namespace MNN { diff --git a/source/backend/vulkan/buffer/execution/VulkanLayernorm.cpp b/source/backend/vulkan/buffer/execution/VulkanLayernorm.cpp index 2f3114e51..82c37c842 100644 --- a/source/backend/vulkan/buffer/execution/VulkanLayernorm.cpp +++ b/source/backend/vulkan/buffer/execution/VulkanLayernorm.cpp @@ -95,12 +95,13 @@ ErrorCode VulkanLayernorm::onEncode(const std::vector& inputs, const st inside *= inputs.at(0)->length(i); } inside /= mGroup; - } - for (int i = 0; i < rank - mAxisSize; ++i) { - outside *= inputs.at(0)->length(i); - } - for (int i = rank - mAxisSize; i < rank; ++i) { - inside *= inputs.at(0)->length(i); + } else { + for (int i = 0; i < rank - mAxisSize; ++i) { + outside *= inputs.at(0)->length(i); + } + for (int i = rank - mAxisSize; i < rank; ++i) { + inside *= inputs.at(0)->length(i); + } } auto param = reinterpret_cast(mParam->map()); param->size[0] = inside; diff --git a/source/backend/vulkan/component/VulkanDevice.cpp b/source/backend/vulkan/component/VulkanDevice.cpp index 06f1eb5f2..6addd05c5 100644 --- a/source/backend/vulkan/component/VulkanDevice.cpp +++ b/source/backend/vulkan/component/VulkanDevice.cpp @@ -105,6 +105,20 @@ VulkanDevice::VulkanDevice(std::shared_ptr instance) vkGetPhysicalDeviceProperties(mPhysicalDevice, &mDeviceProty); vkGetPhysicalDeviceMemoryProperties(mPhysicalDevice, &mMemoryProty); getDeviceQueue(mQueueFamilyIndex, 0, mQueue); + + // query subgroupSize + { + VkPhysicalDeviceProperties2 deviceProperties2 = {}; + deviceProperties2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PROPERTIES_2; + + VkPhysicalDeviceSubgroupProperties subgroupProperties = {}; + subgroupProperties.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_PROPERTIES; + + deviceProperties2.pNext = &subgroupProperties; + vkGetPhysicalDeviceProperties2(mPhysicalDevice, &deviceProperties2); + mSubgroupSize = subgroupProperties.subgroupSize; + } + #ifdef MNN_VULKAN_PRINT_EXT uint32_t pPropertyCount; vkEnumerateInstanceExtensionProperties(nullptr, &pPropertyCount, nullptr); diff --git a/source/backend/vulkan/component/VulkanDevice.hpp b/source/backend/vulkan/component/VulkanDevice.hpp index 7eae18dca..61ca8983f 100644 --- a/source/backend/vulkan/component/VulkanDevice.hpp +++ b/source/backend/vulkan/component/VulkanDevice.hpp @@ -171,6 +171,10 @@ class VulkanDevice : public NonCopyable { } } + uint32_t getSubgroupSize() const { + return mSubgroupSize; + } + private: const VkResult enumerateDeviceExtensionProperties(const VkPhysicalDevice& dev, std::vector& exts_props) const; @@ -184,6 +188,7 @@ class VulkanDevice : public NonCopyable { VkPhysicalDeviceProperties mDeviceProty; VkQueue mQueue; VkPhysicalDeviceMemoryProperties mMemoryProty; + uint32_t mSubgroupSize; }; } // namespace MNN #endif /* VulkanDevice_hpp */ diff --git a/source/backend/vulkan/component/VulkanPipeline.cpp b/source/backend/vulkan/component/VulkanPipeline.cpp index 9113476a5..aa0500b04 100644 --- a/source/backend/vulkan/component/VulkanPipeline.cpp +++ b/source/backend/vulkan/component/VulkanPipeline.cpp @@ -118,6 +118,7 @@ SharedPtr VulkanPipelineFactory::getPrivatePipeline(const std::s } VulkanPipeline * pipeline = createComputePipeline((uint8_t*)content.first, content.second, types, {}); + pipeline->mTuneName = key; SharedPtr resPipeline = pipeline; return resPipeline; } diff --git a/source/backend/vulkan/component/VulkanPipeline.hpp b/source/backend/vulkan/component/VulkanPipeline.hpp index 661303b92..9c17001d1 100644 --- a/source/backend/vulkan/component/VulkanPipeline.hpp +++ b/source/backend/vulkan/component/VulkanPipeline.hpp @@ -161,6 +161,9 @@ class VulkanPipeline : public RefCount { VulkanLayout::DescriptorSet* createSet() const; void changePipeline(const std::vector& localSize) const; +public: + std::string mTuneName; + private: const VulkanDevice& mDevice; mutable VkPipeline mPipeline; diff --git a/source/backend/vulkan/component/VulkanQueryPool.cpp b/source/backend/vulkan/component/VulkanQueryPool.cpp index 7cbe6a928..006fadff4 100644 --- a/source/backend/vulkan/component/VulkanQueryPool.cpp +++ b/source/backend/vulkan/component/VulkanQueryPool.cpp @@ -13,9 +13,9 @@ VulkanQueryPool::VulkanQueryPool(const VulkanDevice& dev) : mDevice(dev){ VkQueryPoolCreateInfo queryPoolCreateInfo = {}; queryPoolCreateInfo.sType = VK_STRUCTURE_TYPE_QUERY_POOL_CREATE_INFO; queryPoolCreateInfo.queryType = VK_QUERY_TYPE_TIMESTAMP; - queryPoolCreateInfo.queryCount = 2; // 需要两个时间戳，一个用于开始，一个用于结束 - - vkCreateQueryPool(mDevice.get(), &queryPoolCreateInfo, nullptr, &queryPool); + queryPoolCreateInfo.queryCount = 2; + + CALL_VK(vkCreateQueryPool(mDevice.get(), &queryPoolCreateInfo, nullptr, &queryPool)); } VulkanQueryPool::~VulkanQueryPool(){ @@ -32,10 +32,10 @@ void VulkanQueryPool::VulkanCmdWriteTimestamp(VkCommandBuffer commandBuffer, int float VulkanQueryPool::VulkanGetQueryPoolResults(){ uint64_t timestamps[2]; - vkGetQueryPoolResults(mDevice.get(), queryPool, 0, 2, sizeof(uint64_t) * 2, timestamps, sizeof(uint64_t), VK_QUERY_RESULT_WAIT_BIT); - - float timestampPeriod = mDevice.getTimestampPeriod(); - float executionTime = (timestamps[1] - timestamps[0]) * timestampPeriod * 1e-3f; // us + CALL_VK(vkGetQueryPoolResults(mDevice.get(), queryPool, 0, 2, sizeof(uint64_t) * 2, timestamps, sizeof(uint64_t), VK_QUERY_RESULT_64_BIT | VK_QUERY_RESULT_WAIT_BIT)); + + double timestampPeriod = mDevice.getTimestampPeriod(); + double executionTime = (timestamps[1] - timestamps[0]) * timestampPeriod / double(1e6); // us return executionTime; } } // namespace MNN diff --git a/source/backend/vulkan/image/backend/VulkanBackend.cpp b/source/backend/vulkan/image/backend/VulkanBackend.cpp index 1362641bc..bd5e089eb 100644 --- a/source/backend/vulkan/image/backend/VulkanBackend.cpp +++ b/source/backend/vulkan/image/backend/VulkanBackend.cpp @@ -22,7 +22,7 @@ #ifdef MNN_USE_NEON #include #endif -#define MNN_OP_SUPPORT_LOG +// #define MNN_OP_SUPPORT_LOG //#define MNN_VULKAN_DUMP_MEMORY_USAGE #define MNN_VULKAN_MAX_CACHE_CONVSIZE 50 namespace MNN { @@ -57,7 +57,7 @@ static void _copyTensorToBuffer(const Tensor* source, const VulkanBuffer* dest) VulkanBackend::VulkanBackend(const VulkanRuntime* runtime, const Backend::Info& info) : Backend(MNN_FORWARD_VULKAN) { mRuntime = runtime; - mDirect = Backend::Info::INDIRECT != info.mode; + mDirect = (mRuntime->mGpuMode & MNNGpuMode::MNN_GPU_RECORD_BATCH) == 0; mDynamicMemoryPool.reset(new VulkanMemoryPool(runtime->mMemoryPool.get())); auto& dev = device(); @@ -500,5 +500,114 @@ void VulkanBackend::copyBufferToImage(const VulkanBuffer* buffer, const VulkanIm mRuntime->mCmdPool->submitAndWait(cmdbuffer->get()); } +float VulkanBackend::getPipelineTime(const VulkanPipeline* pipeline, std::shared_ptr des, std::vector groupSize) { + std::shared_ptr cmd; + cmd.reset(const_cast(mRuntime->mCmdPool->allocBuffer())); + cmd->begin(0); + mRuntime->mQueryPool->VulkanCmdResetQueryPool(cmd.get()->get()); + mRuntime->mQueryPool->VulkanCmdWriteTimestamp(cmd.get()->get(), 0); + pipeline->bind(cmd.get()->get(), des->get()); + vkCmdDispatch(cmd.get()->get(), groupSize[0], groupSize[1], groupSize[2]); + mRuntime->mQueryPool->VulkanCmdWriteTimestamp(cmd.get()->get(), 1); + cmd->end(); + mRuntime->mCmdPool->submitAndWait(cmd.get()->get()); + float time = mRuntime->mQueryPool->VulkanGetQueryPoolResults(); + return time; +} + + +std::vector VulkanBackend::autoTunePipeline(SharedPtr pipeline, std::shared_ptr des, const std::vector gws, const uint32_t tuneDimension, std::vector defaultLws,float * const minCostPtr) { + bool isPrivate = !(pipeline->mTuneName.empty()); + MNN_ASSERT(isPrivate); + if (mRuntime->mGpuMode & MNNGpuMode::MNN_GPU_TUNING_NONE) { + MNN_ASSERT(defaultLws.size() == 3); + MNN_ASSERT(minCostPtr == nullptr); + pipeline->changePipeline(defaultLws); + return defaultLws; + } + MNN_ASSERT(tuneDimension > 0 && tuneDimension <= 3); + + std::unordered_map & tuneMap = mRuntime->mTuneMap; + VKTuneKey tuneKey = {pipeline->mTuneName, {gws[0], gws[1], gws[2]}}; + if (tuneMap.find(tuneKey) != tuneMap.end()) { + VKTuneValue tuneValue = tuneMap[tuneKey]; + if (minCostPtr) { + *minCostPtr = tuneValue.optimalCost; + } + pipeline->changePipeline({tuneValue.optimalLws[0], tuneValue.optimalLws[1], tuneValue.optimalLws[2]}); + return {tuneValue.optimalLws[0], tuneValue.optimalLws[1], tuneValue.optimalLws[2]}; + } + + std::vector workGroupCount(3, 1); + std::vector lwsOptimal(3, 1); + float minCost = -1.0f; + + std::vector maxLocalWorkGroupSize(3, 1); + int maxNumInvocation = mRuntime->mDevice->getMaxComputeWorkGroupInvocations(); + mRuntime->mDevice->getMaxComputeWorkGroupSize(maxLocalWorkGroupSize); + uint32_t subgroupSize = mRuntime->mDevice->getSubgroupSize(); + + uint32_t minLocalSize, maxLocalX, maxLocalY, maxLocalZ; + minLocalSize = 1; + maxLocalX = ALIMIN(maxLocalWorkGroupSize[0], gws[0] << 1); + maxLocalY = ALIMIN(maxLocalWorkGroupSize[1], gws[1] << 1); + maxLocalZ = ALIMIN(maxLocalWorkGroupSize[2], gws[2] << 1); + + std::pair localSizeRangeX = std::pair(minLocalSize, maxLocalX); + std::pair localSizeRangeY = (tuneDimension > 1) ? std::pair(minLocalSize, maxLocalY) : std::pair(1, 1); + std::pair localSizeRangeZ = (tuneDimension > 2) ? std::pair(minLocalSize, maxLocalZ) : std::pair(1, 1); + + bool tuneNormalFlag = (mRuntime->mGpuMode & MNNGpuMode::MNN_GPU_TUNING_WIDE); + + auto checkInvalid = tuneNormalFlag + ? [](uint32_t x, uint32_t y, uint32_t z, uint32_t subgroupSize) -> bool { return x * y * z > 4 * subgroupSize || x > 128 || y > 128 || z > 128 ; } // MNN_GPU_TUNING_WIDE + : [](uint32_t x, uint32_t y, uint32_t z, uint32_t subgroupSize) -> bool { return x * y * z > 16 * subgroupSize; }; // MNN_GPU_TUNING_HEAVY + + for (uint32_t z = localSizeRangeZ.first; z <= localSizeRangeZ.second; z = z << 1) { + for (uint32_t y = localSizeRangeY.first; y <= localSizeRangeY.second; y = y << 1) { + for (uint32_t x = localSizeRangeX.first; x <= localSizeRangeX.second; x = x << 1) { + if (x * y * z > maxNumInvocation) { + continue; + } + if (x * y * z <= 16) { + continue; + } + if (checkInvalid(x, y, z, subgroupSize)) { + continue; + } + + workGroupCount[0] = UP_DIV(gws[0], x); + workGroupCount[1] = UP_DIV(gws[1], y); + workGroupCount[2] = UP_DIV(gws[2], z); + pipeline->changePipeline({x, y, z}); + auto costTime = getPipelineTime(pipeline.get(), des, {workGroupCount[0], workGroupCount[1], workGroupCount[2]}); + // MNN_PRINT("LWS[%4u,%4u,%4u]---Time[%4.2f]ms\n", x, y, z, costTime); + if(costTime < minCost || minCost < 0.0f) { + minCost = costTime; + lwsOptimal[0] = x; + lwsOptimal[1] = y; + lwsOptimal[2] = z; + } + + } + } + } + + // MNN_PRINT("Optimal LWS[%4u, %4u, %4u]. GWS[%4u, %4u, %4u]. Time[%4.2f]ms\n", lwsOptimal[0], lwsOptimal[1], lwsOptimal[2], + // gws[0], gws[1], gws[2], + // minCost); + + pipeline->changePipeline(lwsOptimal); + + if (minCostPtr) { + *minCostPtr = minCost; + } + + tuneMap[tuneKey] = {{lwsOptimal[0], lwsOptimal[1], lwsOptimal[2]}, minCost}; + + return lwsOptimal; +} + + } // namespace MNN diff --git a/source/backend/vulkan/image/backend/VulkanBackend.hpp b/source/backend/vulkan/image/backend/VulkanBackend.hpp index 1ea259882..20e7421d0 100644 --- a/source/backend/vulkan/image/backend/VulkanBackend.hpp +++ b/source/backend/vulkan/image/backend/VulkanBackend.hpp @@ -82,6 +82,11 @@ class VulkanBackend : public Backend { const VulkanCommandPool::Buffer* getInitCommandBuffer() const { return mInitBuffer.get(); } + + std::vector autoTunePipeline(SharedPtr pipeline, std::shared_ptr des, const std::vector gws, const uint32_t tuneDimension = 3, std::vector defaultLws = {}, float * const minCostPtr = nullptr); + + float getPipelineTime(const VulkanPipeline* pipeline, std::shared_ptr des, std::vector groupSize); + private: bool _supportImageSize(const Tensor* tensor); const VulkanDevice& device() const; diff --git a/source/backend/vulkan/image/compiler/AllShader.cpp b/source/backend/vulkan/image/compiler/AllShader.cpp index 6d70d11a2..386e4b90b 100644 --- a/source/backend/vulkan/image/compiler/AllShader.cpp +++ b/source/backend/vulkan/image/compiler/AllShader.cpp @@ -2127,7 +2127,7 @@ unsigned int glsl_softmaxImage_AXIS_C_comp_len = 5204; const unsigned char glsl_convolutionDepthwiseMali_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xdb, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0xec, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 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0x00, 0x11, 0x00, 0x00, 0x00, + 0x2d, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0xe5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xb1, 0x00, 0x05, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, 0x2d, 0x00, 0x00, 0x00, 0x9b, 0x00, 0x04, 0x00, 0x2e, 0x00, 0x00, 0x00, @@ -2337,691 +2280,169 @@ const unsigned char glsl_convolutionDepthwiseMali_comp[] = { 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xfa, 0x00, 0x04, 0x00, 0x31, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x33, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x32, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x17, 0x00, 0x00, 0x00, 0x36, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, + 0x3e, 0x00, 0x00, 0x00, 0x3f, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, + 0x3d, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x40, 0x00, 0x00, 0x00, 0x3f, 0x00, 0x00, 0x00, 0x84, 0x00, 0x05, 0x00, + 0x11, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, + 0x40, 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int glsl_convolutionDepthwiseMali_RELU_comp_len = 5360; +unsigned int glsl_convolutionDepthwiseMali_comp_len = 3724; -const unsigned char glsl_convolutionDepthwiseMali_RELU6_comp[] = { +const unsigned char glsl_relu_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xe1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x5a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -3029,476 +2450,21 @@ const unsigned char glsl_convolutionDepthwiseMali_RELU6_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x08, 0x00, 0x00, 0x00, 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glsl_relu_comp[] = { - 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x5a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xc2, 0x01, 0x00, 0x00, - 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, 0x0d, 0x00, 0x00, 0x00, - 0x67, 0x6c, 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0x33, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, + 0x17, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x03, 0x00, 0x15, 0x00, 0x00, 0x00, + 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x16, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, + 0x16, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x19, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x02, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, + 0x0a, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -3703,68 +2660,43 @@ const unsigned char glsl_unaryImage_comp[] = { 0x44, 0x00, 0x00, 0x00, 0x43, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x33, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x12, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, - 0x12, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, - 0x1a, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0xb1, 0x00, 0x05, 0x00, - 0x1c, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, - 0x1b, 0x00, 0x00, 0x00, 0xf7, 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1732; +unsigned int glsl_unaryImage_comp_len = 1332; const unsigned char glsl_unaryImage_SIGMOID_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x4d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -3775,66 +2707,57 @@ const unsigned char glsl_unaryImage_SIGMOID_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 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0x02, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, + 0x0a, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -3851,20 +2774,16 @@ const unsigned char glsl_unaryImage_SIGMOID_comp[] = { 0x00, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x4b, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x2c, 0x00, 0x06, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x4c, 0x00, 0x00, 0x00, 0x4b, 0x00, 0x00, 0x00, - 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, - 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, - 0x22, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x33, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, - 0x09, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x13, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, + 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x07, 0x00, + 0x32, 0x00, 0x00, 0x00, 0x55, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x00, 0x00, + 0x3d, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x00, 0x00, + 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, + 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, + 0x51, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, @@ -3872,63 +2791,38 @@ const unsigned char glsl_unaryImage_SIGMOID_comp[] = { 0x13, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0xf7, 0x00, 0x03, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xfa, 0x00, 0x04, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x24, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x26, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x26, 0x00, 0x00, 0x00, 0x8b, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, - 0x24, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x29, 0x00, 0x00, 0x00, - 0x28, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x2a, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, - 0x2a, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, - 0x2c, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, - 0x25, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x2d, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x87, 0x00, 0x05, 0x00, - 0x06, 0x00, 0x00, 0x00, 0x2e, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, - 0x2d, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x30, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, - 0x3e, 0x00, 0x03, 0x00, 0x30, 0x00, 0x00, 0x00, 0x2e, 0x00, 0x00, 0x00, + 0x13, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x87, 0x00, 0x05, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x2e, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, + 0x27, 0x00, 0x00, 0x00, 0x50, 0x00, 0x05, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x54, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x2e, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x36, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00, - 0x38, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, - 0x3a, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, 0x64, 0x00, 0x04, 0x00, - 0x35, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00, - 0x5f, 0x00, 0x07, 0x00, 0x32, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x00, 0x00, 0x3a, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, - 0x18, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x34, 0x00, 0x00, 0x00, - 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, - 0x3e, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x7f, 0x00, 0x04, 0x00, - 0x32, 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0x00, 0x20, 0x00, 0x00, 0x00, - 0x49, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, - 0x32, 0x00, 0x00, 0x00, 0x4a, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, - 0x63, 0x00, 0x04, 0x00, 0x48, 0x00, 0x00, 0x00, 0x49, 0x00, 0x00, 0x00, - 0x4a, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, + 0x38, 0x00, 0x00, 0x00, 0x64, 0x00, 0x04, 0x00, 0x35, 0x00, 0x00, 0x00, + 0x3b, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00, 0x5f, 0x00, 0x07, 0x00, + 0x32, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x00, 0x00, + 0x54, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, + 0x7f, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x3c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x06, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, + 0x3f, 0x00, 0x00, 0x00, 0x81, 0x00, 0x05, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x55, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, + 0x88, 0x00, 0x05, 0x00, 0x32, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, + 0x55, 0x00, 0x00, 0x00, 0x42, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0x45, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x47, 0x00, 0x00, 0x00, + 0x63, 0x00, 0x04, 0x00, 0x48, 0x00, 0x00, 0x00, 0x54, 0x00, 0x00, 0x00, + 0x44, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_SIGMOID_comp_len = 1912; +unsigned int glsl_unaryImage_SIGMOID_comp_len = 1456; const unsigned char glsl_unaryImage_TANH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -3939,66 +2833,57 @@ const unsigned char glsl_unaryImage_TANH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x73, 0x69, 0x7a, 0x65, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, - 0x17, 0x00, 0x00, 0x00, 0x75, 0x43, 0x6f, 0x6e, 0x73, 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0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_ABS_comp_len = 1784; +unsigned int glsl_unaryImage_ABS_comp_len = 1356; const unsigned char glsl_unaryImage_SQRT_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -4245,66 +3067,57 @@ const unsigned char glsl_unaryImage_SQRT_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 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0x03, 0x00, @@ -4322,72 +3135,45 @@ const unsigned char glsl_unaryImage_SQRT_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x33, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x00, 0x00, 0x41, 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glsl_unaryImage_RSQRT_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -4398,66 +3184,57 @@ const unsigned char glsl_unaryImage_RSQRT_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 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0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_RSQRT_comp_len = 1784; +unsigned int glsl_unaryImage_RSQRT_comp_len = 1356; const unsigned char glsl_unaryImage_NEG_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 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0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -4628,71 +3369,45 @@ const unsigned char glsl_unaryImage_NEG_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x33, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0xf8, 0x00, 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0x00, 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, + 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_NEG_comp_len = 1776; +unsigned int glsl_unaryImage_NEG_comp_len = 1348; const unsigned char glsl_unaryImage_SQUARE_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x50, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -4703,66 +3418,57 @@ const unsigned char glsl_unaryImage_SQUARE_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 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0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -4857,66 +3535,57 @@ const unsigned char glsl_unaryImage_EXP_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 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0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -4934,72 +3603,45 @@ const unsigned char glsl_unaryImage_EXP_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 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0x01, 0x00 + 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_EXP_comp_len = 1784; +unsigned int glsl_unaryImage_EXP_comp_len = 1356; const unsigned char glsl_unaryImage_SIGN_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5010,66 +3652,57 @@ const unsigned char glsl_unaryImage_SIGN_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x73, 0x69, 0x7a, 0x65, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, - 0x17, 0x00, 0x00, 0x00, 0x75, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, - 0x74, 0x00, 0x00, 0x00, 0x05, 0x00, 0x03, 0x00, 0x22, 0x00, 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0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_SIGN_comp_len = 1784; +unsigned int glsl_unaryImage_SIGN_comp_len = 1356; const unsigned char glsl_unaryImage_LOG_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x4a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5163,66 +3769,57 @@ const unsigned char glsl_unaryImage_LOG_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 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0x19, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x02, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, + 0x0a, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -5244,74 +3841,47 @@ const unsigned char glsl_unaryImage_LOG_comp[] = { 0x48, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 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0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, - 0x20, 0x00, 0x00, 0x00, 0x46, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, 0x47, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, 0x45, 0x00, 0x00, 0x00, - 0x46, 0x00, 0x00, 0x00, 0x47, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x40, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x42, 0x00, 0x00, 0x00, + 0x45, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x45, 0x00, 0x00, 0x00, 0x51, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_LOG_comp_len = 1856; +unsigned int glsl_unaryImage_LOG_comp_len = 1428; const unsigned char glsl_unaryImage_TAN_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5322,66 +3892,57 @@ const unsigned char glsl_unaryImage_TAN_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 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0x01, 0x00 }; -unsigned int glsl_unaryImage_TAN_comp_len = 1784; +unsigned int glsl_unaryImage_TAN_comp_len = 1356; const unsigned char glsl_unaryImage_COS_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5475,66 +4009,57 @@ const unsigned char glsl_unaryImage_COS_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 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0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, 0x42, 0x00, 0x00, 0x00, - 0x43, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_COS_comp_len = 1784; +unsigned int glsl_unaryImage_COS_comp_len = 1356; const unsigned char glsl_unaryImage_SIN_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5628,66 +4126,57 @@ const unsigned char glsl_unaryImage_SIN_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 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0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -5705,72 +4194,45 @@ const unsigned char glsl_unaryImage_SIN_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 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+ 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_SIN_comp_len = 1784; +unsigned int glsl_unaryImage_SIN_comp_len = 1356; const unsigned char glsl_unaryImage_CEIL_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5781,66 +4243,57 @@ const unsigned char glsl_unaryImage_CEIL_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 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= 1784; +unsigned int glsl_unaryImage_CEIL_comp_len = 1356; const unsigned char glsl_unaryImage_FLOOR_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -5934,66 +4360,57 @@ const unsigned char glsl_unaryImage_FLOOR_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 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0x00, 0x04, 0x00, 0x42, 0x00, 0x00, 0x00, - 0x43, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_FLOOR_comp_len = 1784; +unsigned int glsl_unaryImage_FLOOR_comp_len = 1356; const unsigned char glsl_unaryImage_EXPM1_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x4a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x52, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 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0x00, 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x41, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0x43, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, - 0x22, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, - 0x46, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, - 0x44, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x46, 0x00, 0x00, 0x00, - 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x63, 0x00, 0x04, 0x00, 0x44, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00, + 0x40, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, + 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_RECIPROCAL_comp_len = 1824; +unsigned int glsl_unaryImage_RECIPROCAL_comp_len = 1396; const unsigned char glsl_unaryImage_SINH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -6401,66 +4721,57 @@ const unsigned char glsl_unaryImage_SINH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 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0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, + 0x0a, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -6478,72 +4789,45 @@ const unsigned char glsl_unaryImage_SINH_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 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0x00, 0x00, 0x43, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, 0x42, 0x00, 0x00, 0x00, - 0x43, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 -}; -unsigned int glsl_unaryImage_SINH_comp_len = 1784; + 0x0e, 0x00, 0x00, 0x00, 0x51, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, + 0x13, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, + 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, + 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, + 0x1a, 0x00, 0x00, 0x00, 0xb1, 0x00, 0x05, 0x00, 0x1c, 0x00, 0x00, 0x00, + 0x1d, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, + 0xf7, 0x00, 0x03, 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0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -6554,66 +4838,57 @@ const unsigned char glsl_unaryImage_ASINH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x15, 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0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_ASINH_comp_len = 1784; +unsigned int glsl_unaryImage_ASINH_comp_len = 1356; const unsigned char glsl_unaryImage_ASIN_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -6707,66 +4955,57 @@ const unsigned char glsl_unaryImage_ASIN_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 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= 1784; +unsigned int glsl_unaryImage_ASIN_comp_len = 1356; const unsigned char glsl_unaryImage_COSH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -6860,66 +5072,57 @@ const unsigned char glsl_unaryImage_COSH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 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0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_COSH_comp_len = 1784; +unsigned int glsl_unaryImage_COSH_comp_len = 1356; const unsigned char glsl_unaryImage_ACOS_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -7013,66 +5189,57 @@ const unsigned char glsl_unaryImage_ACOS_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 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0x00, 0x00, - 0x3d, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, - 0x34, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, 0x42, 0x00, 0x00, 0x00, - 0x43, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, - 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 + 0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_ACOS_comp_len = 1784; +unsigned int glsl_unaryImage_ACOS_comp_len = 1356; const unsigned char glsl_unaryImage_ACOSH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -7166,66 +5306,57 @@ const unsigned char glsl_unaryImage_ACOSH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 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0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -7243,72 +5374,45 @@ const unsigned char glsl_unaryImage_ACOSH_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 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0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_ATAN_comp_len = 1784; +unsigned int glsl_unaryImage_ATAN_comp_len = 1356; const unsigned char glsl_unaryImage_ATANH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -7472,66 +5540,57 @@ const unsigned char glsl_unaryImage_ATANH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 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0x00, 0x02, 0x00, + 0x4f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -7783,66 +5780,57 @@ const unsigned char glsl_unaryImage_ROUND_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 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0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -7860,72 +5848,45 @@ const unsigned char glsl_unaryImage_ROUND_comp[] = { 0x46, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, 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0x3c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x3f, 0x00, 0x00, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x42, 0x00, 0x00, 0x00, 0x4e, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_ROUND_comp_len = 1784; +unsigned int glsl_unaryImage_ROUND_comp_len = 1356; const unsigned char glsl_unaryImage_HARDSWISH_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -7936,23 +5897,16 @@ const unsigned char glsl_unaryImage_HARDSWISH_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x73, 0x69, 0x7a, 0x65, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, - 0x17, 0x00, 0x00, 0x00, 0x75, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, - 0x74, 0x00, 0x00, 0x00, 0x05, 0x00, 0x03, 0x00, 0x22, 0x00, 0x00, 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0x00, 0x05, 0x00, 0x17, 0x00, 0x00, 0x00, + 0x75, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x00, 0x00, 0x00, + 0x05, 0x00, 0x04, 0x00, 0x38, 0x00, 0x00, 0x00, 0x75, 0x49, 0x6e, 0x70, + 0x75, 0x74, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x60, 0x00, 0x00, 0x00, 0x75, 0x4f, 0x75, 0x74, 0x70, 0x75, 0x74, 0x00, 0x47, 0x00, 0x04, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x48, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, @@ -7972,82 +5926,70 @@ const unsigned char glsl_unaryImage_HARDSWISH_comp[] = { 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x15, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x15, 0x00, 0x04, 0x00, - 0x0a, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 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0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x13, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, + 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, + 0x51, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, @@ -8055,87 +5997,49 @@ const unsigned char glsl_unaryImage_HARDSWISH_comp[] = { 0x13, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0xf7, 0x00, 0x03, 0x00, 0x1f, 0x00, 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0x00, 0x00, + 0x4d, 0x00, 0x00, 0x00, 0x57, 0x00, 0x00, 0x00, 0x46, 0x00, 0x00, 0x00, + 0x83, 0x00, 0x05, 0x00, 0x32, 0x00, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, + 0x5a, 0x00, 0x00, 0x00, 0x4d, 0x00, 0x00, 0x00, 0x85, 0x00, 0x05, 0x00, + 0x32, 0x00, 0x00, 0x00, 0x5d, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00, + 0x5c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x5e, 0x00, 0x00, 0x00, + 0x61, 0x00, 0x00, 0x00, 0x60, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x61, 0x00, 0x00, 0x00, 0x6d, 0x00, 0x00, 0x00, 0x5d, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_HARDSWISH_comp_len = 2472; +unsigned int glsl_unaryImage_HARDSWISH_comp_len = 1788; const unsigned char glsl_unaryImage_GELU_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x5e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x66, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -8146,22 +6050,17 @@ const unsigned char glsl_unaryImage_GELU_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 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0x00, 0x04, 0x00, 0x0b, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x7c, 0x00, 0x04, 0x00, + 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, + 0x51, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, @@ -8259,77 +6145,46 @@ const unsigned char glsl_unaryImage_GELU_comp[] = { 0x13, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0xf7, 0x00, 0x03, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xfa, 0x00, 0x04, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 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0x00, 0x00, 0x54, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0x56, 0x00, 0x00, 0x00, 0x59, 0x00, 0x00, 0x00, 0x58, 0x00, 0x00, 0x00, + 0x63, 0x00, 0x04, 0x00, 0x59, 0x00, 0x00, 0x00, 0x65, 0x00, 0x00, 0x00, + 0x55, 0x00, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x1f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_unaryImage_GELU_comp_len = 2272; +unsigned int glsl_unaryImage_GELU_comp_len = 1684; const unsigned char glsl_unaryImage_SILU_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x4d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x56, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -8340,66 +6195,57 @@ const unsigned char glsl_unaryImage_SILU_comp[] = { 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x70, 0x6f, 0x73, 0x54, 0x6d, 0x70, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, - 0x0d, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, 0x6c, 0x6f, 0x62, 0x61, - 0x6c, 0x49, 0x6e, 0x76, 0x6f, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, - 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x42, 0x75, 0x66, 0x66, 0x65, 0x72, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x15, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x73, 0x69, 0x7a, 0x65, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00, - 0x17, 0x00, 0x00, 0x00, 0x75, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, - 0x74, 0x00, 0x00, 0x00, 0x05, 0x00, 0x03, 0x00, 0x22, 0x00, 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0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x16, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, + 0x16, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x19, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x02, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, + 0x0a, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x32, 0x00, 0x00, 0x00, + 0x31, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x19, 0x00, 0x09, 0x00, 0x35, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x03, 0x00, @@ -8416,20 +6262,16 @@ const unsigned char glsl_unaryImage_SILU_comp[] = { 0x00, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x4b, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x2c, 0x00, 0x06, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x4c, 0x00, 0x00, 0x00, 0x4b, 0x00, 0x00, 0x00, - 0x2f, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, - 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, - 0x3b, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x21, 0x00, 0x00, 0x00, - 0x22, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, - 0x33, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, - 0x3d, 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0x00, 0x7c, 0x00, 0x04, 0x00, + 0x07, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, + 0x51, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, @@ -8437,58 +6279,34 @@ const unsigned char glsl_unaryImage_SILU_comp[] = { 0x13, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0xf7, 0x00, 0x03, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xfa, 0x00, 0x04, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, - 0xf8, 0x00, 0x02, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x41, 0x00, 0x05, 0x00, - 0x11, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, - 0x24, 0x00, 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glsl_convolutionDepthwise_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xdb, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0xfd, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -8870,442 +6688,320 @@ const unsigned char glsl_convolutionDepthwise_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x05, 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-const unsigned char glsl_convolutionDepthwise_RELU_comp[] = { +const unsigned char glsl_convolutionDepthwise_FP32_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xdf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0xfd, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -9313,450 +7009,320 @@ const unsigned char glsl_convolutionDepthwise_RELU_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 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0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_convolutionDepthwise_RELU_comp_len = 5360; +unsigned int glsl_convolutionDepthwise_FP32_comp_len = 3804; -const unsigned char glsl_convolutionDepthwise_RELU6_comp[] = { +const unsigned char glsl_convolutionDepthwise_RELU_FP32_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xe1, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x06, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -9764,1922 +7330,7163 @@ const unsigned char glsl_convolutionDepthwise_RELU6_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x10, 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+unsigned int glsl_convolutionDepthwise_RELU_FP32_comp_len = 3876; -const unsigned char glsl_gridSampleBilinear_comp[] = { +const unsigned char glsl_convolutionDepthwise_RELU6_FP32_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xd1, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x08, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 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3672; +unsigned int glsl_convolutionDepthwise_RELU6_FP32_comp_len = 3924; -const unsigned char glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp[] = { +const unsigned char glsl_convolutionDepthwise_FP16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xfe, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x45, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 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glsl_convolutionDepthwise_RELU_FP16_comp_len = 4044; -const unsigned char glsl_gridSampleNearest_comp[] = { +const unsigned char glsl_convolutionDepthwise_RELU6_FP16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0xe4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xc2, 0x01, 0x00, 0x00, - 0x05, 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glsl_relu6_comp_len = 1644; - -const unsigned char glsl_binaryImage_comp[] = { - 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x00, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, - 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, - 0x00, 0x00, 0x00, 0x00, 0x05, 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0xf8, 0x00, 0x02, 0x00, 0x76, 0x02, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0xca, 0x02, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0xca, 0x02, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 +}; +unsigned int glsl_convolution1x1_c8w4_comp_len = 6408; + +const unsigned char glsl_convolution1x1_c8w4_FP32_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0x0b, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, + 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, + 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x01, 0x00, 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glsl_convolution1x1_c8w4_RELU_FP32_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0x33, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, + 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, + 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, + 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, + 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, 0x0c, 0x00, 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0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0xb0, 0x02, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0xb0, 0x02, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x04, 0x03, 0x00, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x04, 0x03, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, + 0x38, 0x00, 0x01, 0x00 +}; +unsigned int glsl_convolution1x1_c8w4_RELU_FP32_comp_len = 6676; + +const unsigned char glsl_convolution1x1_c8w4_RELU6_FP32_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0x35, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, + 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, + 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x10, 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0x00, 0x06, 0x03, 0x00, 0x00, + 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 +}; +unsigned int glsl_convolution1x1_c8w4_RELU6_FP32_comp_len = 6752; + +const unsigned char glsl_convolution1x1_c8w4_FP16_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xdd, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x09, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, + 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, + 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 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0x02, 0x00, 0x00, 0x96, 0x02, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, + 0x86, 0x02, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x86, 0x02, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x44, 0x02, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x44, 0x02, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0x9c, 0x02, 0x00, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x9c, 0x02, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, + 0x38, 0x00, 0x01, 0x00 +}; +unsigned int glsl_convolution1x1_c8w4_FP16_comp_len = 6832; + +const unsigned char glsl_convolution1x1_c8w4_RELU_FP16_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xed, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x09, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, + 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 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+const unsigned char glsl_convolution1x1_c8w4_RELU6_FP16_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xef, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x09, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, + 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, + 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, + 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, 0x04, 0x00, 0x09, 0x00, + 0x47, 0x4c, 0x5f, 0x41, 0x4d, 0x44, 0x5f, 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glsl_convolution1x1_c8w4_RELU6_FP16_comp_len = 7432; + +const unsigned char glsl_gridSampleNearest_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xe4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, + 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, + 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xc2, 0x01, 0x00, 0x00, + 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, + 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0xd6, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 +}; +unsigned int glsl_convolution1x1_FP16_comp_len = 3372; + +const unsigned char glsl_convolution1x1_RELU_FP16_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xe8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x09, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, + 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, + 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 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glsl_binary_blit_SQUDIFF_comp_len = 2828; +unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp_len = 4616; -const unsigned char glsl_convolutionDepthwise_s1d1_w2_comp[] = { +const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x17, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x62, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -25480,71 +31417,33 @@ const unsigned char glsl_convolutionDepthwise_s1d1_w2_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 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glsl_convolutionDepthwise_s1d1_w2_FP16_comp_len = 4744; -const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU_comp[] = { +const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x1b, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 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}; -unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU6_comp_len = 6984; +unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp_len = 4960; const unsigned char glsl_matmul_output_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, @@ -28193,7 +34013,7 @@ unsigned int glsl_matmul_output_TRANSPOSE_BIAS_comp_len = 3216; const unsigned char glsl_gemm16x16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x54, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x75, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, @@ -28201,11 +34021,10 @@ const unsigned char glsl_gemm16x16_comp[] = { 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 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0x01, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x4a, 0x01, 0x00, 0x00, 0x50, 0x01, 0x00, 0x00, 0x74, 0x01, 0x00, 0x00, + 0xf9, 0x00, 0x02, 0x00, 0x28, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, + 0x28, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_gemm16x16_comp_len = 7468; +unsigned int glsl_gemm16x16_comp_len = 3876; const unsigned char glsl_gemm16x16_FP16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x3a, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x5b, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x09, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, @@ -28828,14 +34348,13 @@ const unsigned char glsl_gemm16x16_FP16_comp[] = { 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 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0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x15, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x03, 0x00, 0x16, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x17, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00, @@ -28907,493 +34410,282 @@ const unsigned char glsl_gemm16x16_FP16_comp[] = { 0x2b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x16, 0x00, 0x03, 0x00, 0x29, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x2a, 0x00, 0x00, 0x00, - 0x29, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, - 0x2b, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x2a, 0x00, 0x00, 0x00, - 0x2b, 0x00, 0x04, 0x00, 0x29, 0x00, 0x00, 0x00, 0x2d, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x07, 0x00, 0x2a, 0x00, 0x00, 0x00, - 0x2e, 0x00, 0x00, 0x00, 0x2d, 0x00, 0x00, 0x00, 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glsl_deconvolutionDepthwise_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, @@ -31685,800 +36977,4001 @@ const unsigned char glsl_deconvIm2Col_RELU6_comp[] = { 0x5f, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x5f, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_deconvIm2Col_RELU6_comp_len = 4292; +unsigned int glsl_deconvIm2Col_RELU6_comp_len = 4292; + +const unsigned char glsl_buffer2Image1D_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0x36, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x2c, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, + 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, + 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0x00 +}; +unsigned int glsl_convolution1x1_w4_comp_len = 5128; + +const unsigned char glsl_convolution1x1_w4_FP32_comp[] = { + 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, + 0xd1, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, + 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, + 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, + 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00, 0xb8, 0x01, 0x00, 0x00, + 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 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-unsigned int glsl_argmax_ARGMIN_comp_len = 2096; +unsigned int glsl_convolution1x1_w4_RELU_FP16_comp_len = 5788; -const unsigned char glsl_buffer2Image3D_comp[] = { +const unsigned char glsl_convolution1x1_w4_RELU6_FP16_comp[] = { 0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0b, 0x00, 0x08, 0x00, - 0x55, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00, 0x38, 0x00, 0x00, 0x00, - 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x4c, 0x53, 0x4c, - 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30, 0x00, 0x00, 0x00, 0x00, - 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x03, 0x00, 0x03, 0x00, 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0x04, 0x00, 0x8a, 0x01, 0x00, 0x00, 0x8e, 0x01, 0x00, 0x00, + 0x90, 0x01, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0xb2, 0x01, 0x00, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0x7c, 0x01, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0x5d, 0x01, 0x00, 0x00, 0x92, 0x01, 0x00, 0x00, 0x5f, 0x01, 0x00, 0x00, + 0x50, 0x00, 0x05, 0x00, 0x59, 0x00, 0x00, 0x00, 0x95, 0x01, 0x00, 0x00, + 0x4b, 0x01, 0x00, 0x00, 0x52, 0x01, 0x00, 0x00, 0x73, 0x00, 0x04, 0x00, + 0x5c, 0x00, 0x00, 0x00, 0x97, 0x01, 0x00, 0x00, 0x38, 0x01, 0x00, 0x00, + 0x63, 0x00, 0x04, 0x00, 0x92, 0x01, 0x00, 0x00, 0x95, 0x01, 0x00, 0x00, + 0x97, 0x01, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x5d, 0x01, 0x00, 0x00, + 0x98, 0x01, 0x00, 0x00, 0x5f, 0x01, 0x00, 0x00, 0x80, 0x00, 0x05, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x9a, 0x01, 0x00, 0x00, 0x4b, 0x01, 0x00, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x50, 0x00, 0x05, 0x00, 0x59, 0x00, 0x00, 0x00, + 0x9c, 0x01, 0x00, 0x00, 0x9a, 0x01, 0x00, 0x00, 0x52, 0x01, 0x00, 0x00, + 0x73, 0x00, 0x04, 0x00, 0x5c, 0x00, 0x00, 0x00, 0x9e, 0x01, 0x00, 0x00, + 0x3c, 0x01, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, 0x98, 0x01, 0x00, 0x00, + 0x9c, 0x01, 0x00, 0x00, 0x9e, 0x01, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, + 0x5d, 0x01, 0x00, 0x00, 0x9f, 0x01, 0x00, 0x00, 0x5f, 0x01, 0x00, 0x00, + 0x80, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0xa1, 0x01, 0x00, 0x00, + 0x4b, 0x01, 0x00, 0x00, 0x95, 0x00, 0x00, 0x00, 0x50, 0x00, 0x05, 0x00, + 0x59, 0x00, 0x00, 0x00, 0xa3, 0x01, 0x00, 0x00, 0xa1, 0x01, 0x00, 0x00, + 0x52, 0x01, 0x00, 0x00, 0x73, 0x00, 0x04, 0x00, 0x5c, 0x00, 0x00, 0x00, + 0xa5, 0x01, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x63, 0x00, 0x04, 0x00, + 0x9f, 0x01, 0x00, 0x00, 0xa3, 0x01, 0x00, 0x00, 0xa5, 0x01, 0x00, 0x00, + 0x3d, 0x00, 0x04, 0x00, 0x5d, 0x01, 0x00, 0x00, 0xa6, 0x01, 0x00, 0x00, + 0x5f, 0x01, 0x00, 0x00, 0x80, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, + 0xa8, 0x01, 0x00, 0x00, 0x4b, 0x01, 0x00, 0x00, 0x9f, 0x00, 0x00, 0x00, + 0x50, 0x00, 0x05, 0x00, 0x59, 0x00, 0x00, 0x00, 0xaa, 0x01, 0x00, 0x00, + 0xa8, 0x01, 0x00, 0x00, 0x52, 0x01, 0x00, 0x00, 0x73, 0x00, 0x04, 0x00, + 0x5c, 0x00, 0x00, 0x00, 0xac, 0x01, 0x00, 0x00, 0x44, 0x01, 0x00, 0x00, + 0x63, 0x00, 0x04, 0x00, 0xa6, 0x01, 0x00, 0x00, 0xaa, 0x01, 0x00, 0x00, + 0xac, 0x01, 0x00, 0x00, 0xf9, 0x00, 0x02, 0x00, 0xb2, 0x01, 0x00, 0x00, + 0xf8, 0x00, 0x02, 0x00, 0xb2, 0x01, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, + 0x38, 0x00, 0x01, 0x00 }; -unsigned int glsl_buffer2Image3D_comp_len = 1664; +unsigned int glsl_convolution1x1_w4_RELU6_FP16_comp_len = 5848; diff --git a/source/backend/vulkan/image/compiler/VulkanShaderMap.cpp b/source/backend/vulkan/image/compiler/VulkanShaderMap.cpp index 513d88dc8..0fc6db6f1 100644 --- a/source/backend/vulkan/image/compiler/VulkanShaderMap.cpp +++ b/source/backend/vulkan/image/compiler/VulkanShaderMap.cpp @@ -11,8 +11,6 @@ mMaps.insert(std::make_pair("glsl_softmaxImage_AXIS_H_comp", std::make_pair(glsl mMaps.insert(std::make_pair("glsl_softmaxImage_AXIS_W_comp", std::make_pair(glsl_softmaxImage_AXIS_W_comp,glsl_softmaxImage_AXIS_W_comp_len))); mMaps.insert(std::make_pair("glsl_softmaxImage_AXIS_C_comp", std::make_pair(glsl_softmaxImage_AXIS_C_comp,glsl_softmaxImage_AXIS_C_comp_len))); mMaps.insert(std::make_pair("glsl_convolutionDepthwiseMali_comp", std::make_pair(glsl_convolutionDepthwiseMali_comp,glsl_convolutionDepthwiseMali_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwiseMali_RELU_comp", std::make_pair(glsl_convolutionDepthwiseMali_RELU_comp,glsl_convolutionDepthwiseMali_RELU_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwiseMali_RELU6_comp", std::make_pair(glsl_convolutionDepthwiseMali_RELU6_comp,glsl_convolutionDepthwiseMali_RELU6_comp_len))); mMaps.insert(std::make_pair("glsl_relu_comp", std::make_pair(glsl_relu_comp,glsl_relu_comp_len))); mMaps.insert(std::make_pair("glsl_unaryImage_comp", std::make_pair(glsl_unaryImage_comp,glsl_unaryImage_comp_len))); mMaps.insert(std::make_pair("glsl_unaryImage_SIGMOID_comp", std::make_pair(glsl_unaryImage_SIGMOID_comp,glsl_unaryImage_SIGMOID_comp_len))); @@ -47,10 +45,21 @@ mMaps.insert(std::make_pair("glsl_unaryImage_GELU_comp", std::make_pair(glsl_una mMaps.insert(std::make_pair("glsl_unaryImage_SILU_comp", std::make_pair(glsl_unaryImage_SILU_comp,glsl_unaryImage_SILU_comp_len))); mMaps.insert(std::make_pair("glsl_im2col_comp", std::make_pair(glsl_im2col_comp,glsl_im2col_comp_len))); mMaps.insert(std::make_pair("glsl_convolutionDepthwise_comp", std::make_pair(glsl_convolutionDepthwise_comp,glsl_convolutionDepthwise_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU_comp", std::make_pair(glsl_convolutionDepthwise_RELU_comp,glsl_convolutionDepthwise_RELU_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU6_comp", std::make_pair(glsl_convolutionDepthwise_RELU6_comp,glsl_convolutionDepthwise_RELU6_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_FP32_comp", std::make_pair(glsl_convolutionDepthwise_FP32_comp,glsl_convolutionDepthwise_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU_FP32_comp", std::make_pair(glsl_convolutionDepthwise_RELU_FP32_comp,glsl_convolutionDepthwise_RELU_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU6_FP32_comp", std::make_pair(glsl_convolutionDepthwise_RELU6_FP32_comp,glsl_convolutionDepthwise_RELU6_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_FP16_comp", std::make_pair(glsl_convolutionDepthwise_FP16_comp,glsl_convolutionDepthwise_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU_FP16_comp", std::make_pair(glsl_convolutionDepthwise_RELU_FP16_comp,glsl_convolutionDepthwise_RELU_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_RELU6_FP16_comp", std::make_pair(glsl_convolutionDepthwise_RELU6_FP16_comp,glsl_convolutionDepthwise_RELU6_FP16_comp_len))); mMaps.insert(std::make_pair("glsl_gridSampleBilinear_comp", std::make_pair(glsl_gridSampleBilinear_comp,glsl_gridSampleBilinear_comp_len))); mMaps.insert(std::make_pair("glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp", std::make_pair(glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp,glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_comp", std::make_pair(glsl_convolution1x1_c8w4_comp,glsl_convolution1x1_c8w4_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_FP32_comp", std::make_pair(glsl_convolution1x1_c8w4_FP32_comp,glsl_convolution1x1_c8w4_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_RELU_FP32_comp", std::make_pair(glsl_convolution1x1_c8w4_RELU_FP32_comp,glsl_convolution1x1_c8w4_RELU_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_RELU6_FP32_comp", std::make_pair(glsl_convolution1x1_c8w4_RELU6_FP32_comp,glsl_convolution1x1_c8w4_RELU6_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_FP16_comp", std::make_pair(glsl_convolution1x1_c8w4_FP16_comp,glsl_convolution1x1_c8w4_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_RELU_FP16_comp", std::make_pair(glsl_convolution1x1_c8w4_RELU_FP16_comp,glsl_convolution1x1_c8w4_RELU_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_c8w4_RELU6_FP16_comp", std::make_pair(glsl_convolution1x1_c8w4_RELU6_FP16_comp,glsl_convolution1x1_c8w4_RELU6_FP16_comp_len))); mMaps.insert(std::make_pair("glsl_gridSampleNearest_comp", std::make_pair(glsl_gridSampleNearest_comp,glsl_gridSampleNearest_comp_len))); mMaps.insert(std::make_pair("glsl_gridSampleNearest_PAD_MODE_ZEROS_comp", std::make_pair(glsl_gridSampleNearest_PAD_MODE_ZEROS_comp,glsl_gridSampleNearest_PAD_MODE_ZEROS_comp_len))); mMaps.insert(std::make_pair("glsl_relu6_comp", std::make_pair(glsl_relu6_comp,glsl_relu6_comp_len))); @@ -94,6 +103,13 @@ mMaps.insert(std::make_pair("glsl_winogradTransformDest2_3_1_comp", std::make_pa mMaps.insert(std::make_pair("glsl_winogradTransformDest2_3_1_RELU_comp", std::make_pair(glsl_winogradTransformDest2_3_1_RELU_comp,glsl_winogradTransformDest2_3_1_RELU_comp_len))); mMaps.insert(std::make_pair("glsl_winogradTransformDest2_3_1_RELU6_comp", std::make_pair(glsl_winogradTransformDest2_3_1_RELU6_comp,glsl_winogradTransformDest2_3_1_RELU6_comp_len))); mMaps.insert(std::make_pair("glsl_winogradTransformSource2_3_1_comp", std::make_pair(glsl_winogradTransformSource2_3_1_comp,glsl_winogradTransformSource2_3_1_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_comp", std::make_pair(glsl_convolution1x1_comp,glsl_convolution1x1_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_FP32_comp", std::make_pair(glsl_convolution1x1_FP32_comp,glsl_convolution1x1_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_RELU_FP32_comp", std::make_pair(glsl_convolution1x1_RELU_FP32_comp,glsl_convolution1x1_RELU_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_RELU6_FP32_comp", std::make_pair(glsl_convolution1x1_RELU6_FP32_comp,glsl_convolution1x1_RELU6_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_FP16_comp", std::make_pair(glsl_convolution1x1_FP16_comp,glsl_convolution1x1_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_RELU_FP16_comp", std::make_pair(glsl_convolution1x1_RELU_FP16_comp,glsl_convolution1x1_RELU_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_RELU6_FP16_comp", std::make_pair(glsl_convolution1x1_RELU6_FP16_comp,glsl_convolution1x1_RELU6_FP16_comp_len))); mMaps.insert(std::make_pair("glsl_col2Im_comp", std::make_pair(glsl_col2Im_comp,glsl_col2Im_comp_len))); mMaps.insert(std::make_pair("glsl_col2Im_RELU_comp", std::make_pair(glsl_col2Im_RELU_comp,glsl_col2Im_RELU_comp_len))); mMaps.insert(std::make_pair("glsl_col2Im_RELU6_comp", std::make_pair(glsl_col2Im_RELU6_comp,glsl_col2Im_RELU6_comp_len))); @@ -109,8 +125,12 @@ mMaps.insert(std::make_pair("glsl_binary_blit_VMAX_comp", std::make_pair(glsl_bi mMaps.insert(std::make_pair("glsl_binary_blit_VMIN_comp", std::make_pair(glsl_binary_blit_VMIN_comp,glsl_binary_blit_VMIN_comp_len))); mMaps.insert(std::make_pair("glsl_binary_blit_SQUDIFF_comp", std::make_pair(glsl_binary_blit_SQUDIFF_comp,glsl_binary_blit_SQUDIFF_comp_len))); mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_comp,glsl_convolutionDepthwise_s1d1_w2_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU_comp,glsl_convolutionDepthwise_s1d1_w2_RELU_comp_len))); -mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU6_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU6_comp,glsl_convolutionDepthwise_s1d1_w2_RELU6_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_FP32_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_FP32_comp,glsl_convolutionDepthwise_s1d1_w2_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp,glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp,glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_FP16_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_FP16_comp,glsl_convolutionDepthwise_s1d1_w2_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp,glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp", std::make_pair(glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp,glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp_len))); mMaps.insert(std::make_pair("glsl_matmul_output_comp", std::make_pair(glsl_matmul_output_comp,glsl_matmul_output_comp_len))); mMaps.insert(std::make_pair("glsl_matmul_output_BIAS_comp", std::make_pair(glsl_matmul_output_BIAS_comp,glsl_matmul_output_BIAS_comp_len))); mMaps.insert(std::make_pair("glsl_matmul_output_TRANSPOSE_comp", std::make_pair(glsl_matmul_output_TRANSPOSE_comp,glsl_matmul_output_TRANSPOSE_comp_len))); @@ -129,5 +149,12 @@ mMaps.insert(std::make_pair("glsl_scale_comp", std::make_pair(glsl_scale_comp,gl mMaps.insert(std::make_pair("glsl_argmax_comp", std::make_pair(glsl_argmax_comp,glsl_argmax_comp_len))); mMaps.insert(std::make_pair("glsl_argmax_ARGMIN_comp", std::make_pair(glsl_argmax_ARGMIN_comp,glsl_argmax_ARGMIN_comp_len))); mMaps.insert(std::make_pair("glsl_buffer2Image3D_comp", std::make_pair(glsl_buffer2Image3D_comp,glsl_buffer2Image3D_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_comp", std::make_pair(glsl_convolution1x1_w4_comp,glsl_convolution1x1_w4_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_FP32_comp", std::make_pair(glsl_convolution1x1_w4_FP32_comp,glsl_convolution1x1_w4_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_RELU_FP32_comp", std::make_pair(glsl_convolution1x1_w4_RELU_FP32_comp,glsl_convolution1x1_w4_RELU_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_RELU6_FP32_comp", std::make_pair(glsl_convolution1x1_w4_RELU6_FP32_comp,glsl_convolution1x1_w4_RELU6_FP32_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_FP16_comp", std::make_pair(glsl_convolution1x1_w4_FP16_comp,glsl_convolution1x1_w4_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_RELU_FP16_comp", std::make_pair(glsl_convolution1x1_w4_RELU_FP16_comp,glsl_convolution1x1_w4_RELU_FP16_comp_len))); +mMaps.insert(std::make_pair("glsl_convolution1x1_w4_RELU6_FP16_comp", std::make_pair(glsl_convolution1x1_w4_RELU6_FP16_comp,glsl_convolution1x1_w4_RELU6_FP16_comp_len))); } } diff --git a/source/backend/vulkan/image/execution/VulkanConvolution.cpp b/source/backend/vulkan/image/execution/VulkanConvolution.cpp index ca43921b1..9a56ac206 100644 --- a/source/backend/vulkan/image/execution/VulkanConvolution.cpp +++ b/source/backend/vulkan/image/execution/VulkanConvolution.cpp @@ -118,22 +118,15 @@ bool VulkanConvolutionDepthwise::_init(const float* weightData, size_t weightSiz VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER}; MNN_ASSERT(OpType_ConvolutionDepthwise == convOp->type()); - auto macro = getPostTreatMacro(common); + auto macroRelu = getPostTreatMacro(common); + bool useFP16 = (extra->gpuType() == VulkanRuntime::ADRENO || extra->gpuType() == VulkanRuntime::MALI) && extra->getMemoryPool().permitFp16(); + std::string macroPrecision = (useFP16) ? "FP16_" : "FP32_"; + std::string macro = macroRelu + macroPrecision; if (common->strideX() == 1 && common->strideY() == 1 && common->dilateX() == 1 && common->dilateY() == 1 ) { - mConvPipeline = extra->getPipeline("glsl_convolutionDepthwise_s1d1_w2_" + macro + "comp", convTypes); + mConvPipeline = extra->getPrivatePipeline("glsl_convolutionDepthwise_s1d1_w2_" + macro + "comp", convTypes); mUseS1D1W2 = true; - mLocalX = 8; - mLocalY = 8; } else { - if (extra->gpuType() == VulkanRuntime::ADRENO) { - mConvPipeline = extra->getPipeline("glsl_convolutionDepthwise_" + macro + "comp", convTypes); - mLocalX = 16; - mLocalY = 16; - } else { - mConvPipeline = extra->getPipeline("glsl_convolutionDepthwiseMali_" + macro + "comp", convTypes); - mLocalX = 8; - mLocalY = 8; - } + mConvPipeline = extra->getPrivatePipeline("glsl_convolutionDepthwise_" + macro + "comp", convTypes); } auto c4 = UP_DIV(common->outputCount(), 4); @@ -223,6 +216,13 @@ ErrorCode VulkanConvolutionDepthwise::onEncodeConvolution(const Convolution2DCom extra->copyBufferToImage(biasBuffer.get(), mBias.get()); bias = mBias.get(); } + + if (mUseS1D1W2) { + mGws = {(uint32_t)UP_DIV(ow, 2), (uint32_t)oh, (uint32_t)ocDiv4 * input->batch()}; + } else { + mGws = {(uint32_t)ow, (uint32_t)oh, (uint32_t)ocDiv4 * input->batch()}; + } + /*Write Command Buffer*/ mConvSet.reset(mConvPipeline->createSet()); mConvSet->writeImage(((VulkanTensor*)output->deviceId())->image()->view(), mSampler->get(), VK_IMAGE_LAYOUT_GENERAL, 0); @@ -231,16 +231,13 @@ ErrorCode VulkanConvolutionDepthwise::onEncodeConvolution(const Convolution2DCom mConvSet->writeImage(mKernel->view(), mSampler->get(), VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 2); mConvSet->writeImage(bias->view(), mSampler->get(), VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 3); mConvSet->writeBuffer(convCons->buffer(), 4, convCons->size()); + mLws = extra->autoTunePipeline(mConvPipeline, mConvSet, mGws, 2, {8, 8, 1}); mConvPipeline->bind(cmdBuffer->get(), mConvSet->get()); mKernel->barrierRead(cmdBuffer->get()); mBias->barrierRead(cmdBuffer->get()); ((VulkanTensor*)input->deviceId())->image()->barrierRead(cmdBuffer->get()); ((VulkanTensor*)output->deviceId())->image()->barrierWrite(cmdBuffer->get()); - if (mUseS1D1W2) { - vkCmdDispatch(cmdBuffer->get(), UP_DIV(UP_DIV(ow, 2), mLocalX), UP_DIV(oh, mLocalY), ocDiv4 * input->batch()); - } else { - vkCmdDispatch(cmdBuffer->get(), UP_DIV(ow, mLocalX), UP_DIV(oh, mLocalY), ocDiv4 * input->batch()); - } + vkCmdDispatch(cmdBuffer->get(), UP_DIV(mGws[0], mLws[0]), UP_DIV(mGws[1], mLws[1]), UP_DIV(mGws[2], mLws[2])); return NO_ERROR; } diff --git a/source/backend/vulkan/image/execution/VulkanConvolution.hpp b/source/backend/vulkan/image/execution/VulkanConvolution.hpp index 9f49fc2b8..1b238070e 100644 --- a/source/backend/vulkan/image/execution/VulkanConvolution.hpp +++ b/source/backend/vulkan/image/execution/VulkanConvolution.hpp @@ -92,7 +92,7 @@ class VulkanConvolutionDepthwise : public VulkanConvolutionCommon { bool _init(const float* weightData, size_t weightSize, const Op* op, Backend* bn); std::shared_ptr mKernel; - const VulkanPipeline* mConvPipeline; + SharedPtr mConvPipeline; std::shared_ptr mConvSet; const VulkanSampler* mSampler; @@ -100,9 +100,10 @@ class VulkanConvolutionDepthwise : public VulkanConvolutionCommon { std::vector> mExtraSets; std::vector> mExtraBuffers; - int mLocalX = 0; - int mLocalY = 0; bool mUseS1D1W2 = false; + + std::vector mGws; + std::vector mLws; }; } // namespace MNN diff --git a/source/backend/vulkan/image/execution/VulkanConvolution1x1.cpp b/source/backend/vulkan/image/execution/VulkanConvolution1x1.cpp new file mode 100644 index 000000000..d1982f94d --- /dev/null +++ b/source/backend/vulkan/image/execution/VulkanConvolution1x1.cpp @@ -0,0 +1,152 @@ +// +// VulkanConvolutionImpl.hpp +// MNN +// +// Created by MNN on 2025/01/10. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "VulkanConvolution1x1.hpp" +#include "VulkanConvolution.hpp" +#include "VulkanMatMul.hpp" + +namespace MNN{ + +static void writeParameters(VulkanMatMul::Reorder::nchwBuffer& parameters, int co, int ci, int kh, int kw) { + parameters.size[0] = co; + parameters.size[1] = ci; + parameters.size[2] = kh; + parameters.size[3] = kw; + parameters.stride[0] = ci * kh * kw; + parameters.stride[1] = kh * kw; + parameters.stride[2] = kw; + parameters.stride[3] = 1; +} + +struct VulkanImageConv1x1Param { + ivec4 inputSize; + ivec4 outputSize; +}; + +VulkanConvolution1x1::VulkanConvolution1x1(VulkanBackend* vkBn, const Convolution2DCommon* convCommon, const float* weightPtr, const float* biasPtr, const int ic, const int oc) : VulkanBasicExecution(vkBn) { + mConvCommon = convCommon; + + mConv1x1Param = std::make_shared(vkBn->getMemoryPool(), false, sizeof(VulkanImageConv1x1Param), nullptr, VK_BUFFER_USAGE_UNIFORM_BUFFER_BIT); + + std::vector types{VK_DESCRIPTOR_TYPE_STORAGE_IMAGE, + VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, + VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, + VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, + VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER}; + auto macroRelu = VulkanConvolutionCommon::getPostTreatMacro(mConvCommon); + bool useFP16 = (vkBn->gpuType() == VulkanRuntime::ADRENO || vkBn->gpuType() == VulkanRuntime::MALI) && vkBn->getMemoryPool().permitFp16(); + std::string macroPrecision = useFP16 ? "FP16_" : "FP32_"; + std::string macro = macroRelu + macroPrecision; + + mCands.push_back(vkBn->getPrivatePipeline("glsl_convolution1x1_" + macro + "comp", types)); + mCands.push_back(vkBn->getPrivatePipeline("glsl_convolution1x1_w4_" + macro + "comp", types)); + mCands.push_back(vkBn->getPrivatePipeline("glsl_convolution1x1_c8w4_" + macro + "comp", types)); + + mDescriptorSet.reset(mCands[0]->createSet()); + + // write mBias + mBias.reset(new VulkanImage(vkBn->getMemoryPool(), false, {UP_DIV(oc, 4), 1})); + auto biasBuffer = std::make_shared(vkBn->getMemoryPool(), false, sizeof(float) * ALIGN_UP4(oc)); + auto bias = biasBuffer->map(); + ::memset(bias, 0, sizeof(float) * ALIGN_UP4(oc)); + if (biasPtr != nullptr) { + ::memcpy(bias, biasPtr, sizeof(float) * ALIGN_UP4(oc)); + } + biasBuffer->unmap(); + vkBn->copyBufferToImage(biasBuffer.get(), mBias.get(), VK_IMAGE_LAYOUT_READ_ONLY_OPTIMAL); + +{ + if (nullptr != weightPtr) { + size_t weightSize = sizeof(float) * ALIGN_UP4(ic) * ALIGN_UP4(oc); + std::shared_ptr kernelStageBuffer = std::make_shared(vkBn->getMemoryPool(), false, weightSize, nullptr, VK_BUFFER_USAGE_STORAGE_BUFFER_BIT); + auto kernelPtr = (float*)kernelStageBuffer->map(); + ::memset(kernelPtr, 0, weightSize); + for (int indexOc = 0; indexOc < oc; indexOc++) { + int indexOcOut = indexOc / 4; + int indexOcIn = indexOc % 4; + for (int indexIc = 0; indexIc < ic; indexIc++) { + int dstOffset = indexOcIn + indexIc * 4 + 4 * ALIGN_UP4(ic) * indexOcOut; + kernelPtr[dstOffset] = weightPtr[indexIc + indexOc * ic]; + } + } + kernelStageBuffer->unmap(); + mKernel = std::make_shared(vkBn->getMemoryPool(), false, ALIGN_UP4(ic), UP_DIV(oc, 4)); + vkBn->copyBufferToImage(kernelStageBuffer.get(), mKernel.get()); + } +} + +} + + +VulkanConvolution1x1::~VulkanConvolution1x1() { + // Do nothing +} + + +ErrorCode VulkanConvolution1x1::onEncode(const std::vector& inputs, const std::vector& outputs, const VulkanCommandPool::Buffer* cmdBuffer) { + auto vkBn = (VulkanBackend*)backend(); + + auto input = inputs[0]; + auto output = outputs[0]; + + int icDiv4 = UP_DIV(input->channel(), 4); + int ocDiv4 = UP_DIV(output->channel(), 4); + + { + auto conv1x1Param = reinterpret_cast(mConv1x1Param->map()); + conv1x1Param->inputSize[0] = input->width(); + conv1x1Param->inputSize[1] = input->height(); + conv1x1Param->inputSize[2] = icDiv4; + conv1x1Param->inputSize[3] = input->batch(); + conv1x1Param->outputSize[0] = output->width(); + conv1x1Param->outputSize[1] = output->height(); + conv1x1Param->outputSize[2] = ocDiv4; + conv1x1Param->outputSize[3] = output->batch(); + mConv1x1Param->unmap(); + } + + mDescriptorSet->writeImage(((VulkanTensor*)output->deviceId())->image()->view(), vkBn->getCommonSampler()->get(), VK_IMAGE_LAYOUT_GENERAL, 0); + mDescriptorSet->writeImage(((VulkanTensor*)input->deviceId())->image()->view(), vkBn->getCommonSampler()->get(), VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 1); + mDescriptorSet->writeImage(mKernel->view(), vkBn->getCommonSampler()->get(), VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 2); + mDescriptorSet->writeImage(mBias->view(), vkBn->getCommonSampler()->get(), VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL, 3); + mDescriptorSet->writeBuffer(mConv1x1Param->buffer(), 4, mConv1x1Param->size()); + + mKernel->barrierRead(cmdBuffer->get()); + mBias->barrierRead(cmdBuffer->get()); + ((VulkanTensor*)input->deviceId())->image()->barrierRead(cmdBuffer->get()); + ((VulkanTensor*)output->deviceId())->image()->barrierWrite(cmdBuffer->get()); + + std::vector gws, lws; + + mCandidataGws.push_back({output->width() * output->height() * output->batch(), ocDiv4, 1}); + mCandidataGws.push_back({UP_DIV(output->width(), 4) * output->height() * output->batch(), ocDiv4, 1}); + mCandidataGws.push_back({UP_DIV(output->width(), 4) * output->height() * output->batch(), UP_DIV(ocDiv4, 2), 1}); + + float costMin = -1.0f; + float costCurr; + int optimalIndex = -1; + for (int i = 0; i < mCands.size(); i++) { + auto lwsCurr = vkBn->autoTunePipeline(mCands[i], mDescriptorSet, {(uint32_t)mCandidataGws[i][0], (uint32_t)mCandidataGws[i][1], (uint32_t)mCandidataGws[i][2]}, 2, {8, 8, 1}, &costCurr); + if (costCurr < costMin || costMin < 0) { + optimalIndex = i; + costMin = costCurr; + lws = lwsCurr; + } + } + mPipeline = mCands[optimalIndex]; + gws = {(uint32_t)mCandidataGws[optimalIndex][0], (uint32_t)mCandidataGws[optimalIndex][1], (uint32_t)mCandidataGws[optimalIndex][2]}; + + + mPipeline->bind(cmdBuffer->get(), mDescriptorSet->get()); + + vkCmdDispatch(cmdBuffer->get(), UP_DIV(gws[0], lws[0]), UP_DIV(gws[1], lws[1]), UP_DIV(gws[2], lws[2])); + + return NO_ERROR; +} + +} // end namespace MNN diff --git a/source/backend/vulkan/image/execution/VulkanConvolutionImpl.cpp b/source/backend/vulkan/image/execution/VulkanConvolutionImpl.cpp index f5b3f0b29..ef4ca3329 100644 --- a/source/backend/vulkan/image/execution/VulkanConvolutionImpl.cpp +++ b/source/backend/vulkan/image/execution/VulkanConvolutionImpl.cpp @@ -11,6 +11,7 @@ #include "VulkanConvolution.hpp" #include "VulkanConvolutionWinograd.hpp" #include "VulkanMatMul.hpp" +#include "VulkanConvolution1x1.hpp" //#define MNN_OPEN_TIME_TRACE #include namespace MNN { @@ -219,6 +220,13 @@ VulkanBasicExecution* VulkanConvolutionImpl::create(VulkanBackend* backend, cons if (ALIGN_UP4(ci) * convOption->kernelX() * convOption->kernelY() > imageLimit) { return nullptr; } + + if (convOption->kernelX() == 1 && convOption->kernelY() == 1 && + convOption->strideX() == 1 && convOption->strideY() == 1 && + inputs[0]->width() == output->width() && inputs[0]->height() == output->height()) { + return new VulkanConvolution1x1(backend, convOption, weightPtr, biasPtr, ci, co); + } + return new VulkanConvolutionIm2Col(backend, convOption, weightPtr, biasPtr, ci, co); } diff --git a/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.cpp b/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.cpp index 5d70ae5e3..190d5084a 100644 --- a/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.cpp +++ b/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.cpp @@ -49,9 +49,9 @@ VulkanMatrixMultier4x4::VulkanMatrixMultier4x4(VulkanBackend* backend, const flo VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER}; bool supportFp16 = backend->getMemoryPool().permitFp16(); if ((backend->gpuType() == VulkanRuntime::ADRENO || backend->gpuType() == VulkanRuntime::MALI) && supportFp16) { - mPipeline = mBackend->getPipeline("glsl_gemm16x16_FP16_comp", types); + mPipeline = mBackend->getPrivatePipeline("glsl_gemm16x16_FP16_comp", types); } else { - mPipeline = mBackend->getPipeline("glsl_gemm16x16_comp", types); + mPipeline = mBackend->getPrivatePipeline("glsl_gemm16x16_comp", types); } } mDescriptorSet.reset(mPipeline->createSet()); @@ -101,6 +101,7 @@ void VulkanMatrixMultier4x4::prepare(const VulkanCommandPool::Buffer* commandBuf mDescriptorSet->writeBuffer(mConstBuffer->buffer(), 3, mConstBuffer->size()); mOutputWidth = ow; mOutputHeight = oh; + mLws = mBackend->autoTunePipeline(mPipeline, mDescriptorSet, {(uint32_t)mOutputWidth, (uint32_t)mOutputHeight / 4, (uint32_t)mDepth}, 2, {8, 8, 1}); } void VulkanMatrixMultier4x4::compute(const VulkanCommandPool::Buffer* commandBuffer) const { @@ -108,7 +109,7 @@ void VulkanMatrixMultier4x4::compute(const VulkanCommandPool::Buffer* commandBuf mDest->barrierWrite(commandBuffer->get()); mSource->barrierRead(commandBuffer->get()); mKernel->barrierRead(commandBuffer->get()); - vkCmdDispatch(commandBuffer->get(), UP_DIV(mOutputWidth, 8), UP_DIV(mOutputHeight / 4, 8), mDepth); + vkCmdDispatch(commandBuffer->get(), UP_DIV(mOutputWidth, mLws[0]), UP_DIV(mOutputHeight / 4, mLws[1]), mDepth); } } // namespace MNN diff --git a/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.hpp b/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.hpp index d139f0dee..f96ee77a0 100644 --- a/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.hpp +++ b/source/backend/vulkan/image/execution/VulkanMatrixMultier4x4.hpp @@ -29,7 +29,7 @@ class VulkanMatrixMultier4x4 : public NonCopyable { } private: - const VulkanPipeline* mPipeline; + SharedPtr mPipeline; std::shared_ptr mDescriptorSet; VulkanBackend* mBackend; @@ -46,6 +46,7 @@ class VulkanMatrixMultier4x4 : public NonCopyable { int mOutputWidth = 0; int mOutputHeight = 0; + std::vector mLws; }; } // namespace MNN diff --git a/source/backend/vulkan/image/execution/Vulkanconvolution1x1.hpp b/source/backend/vulkan/image/execution/Vulkanconvolution1x1.hpp new file mode 100644 index 000000000..76bc1899a --- /dev/null +++ b/source/backend/vulkan/image/execution/Vulkanconvolution1x1.hpp @@ -0,0 +1,38 @@ +// +// VulkanConvolutionImpl.hpp +// MNN +// +// Created by MNN on 2025/01/10. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef VulkanConvolution1x1_hpp +#define VulkanConvolution1x1_hpp + +#include "VulkanBasicExecution.hpp" + + +namespace MNN { + +class VulkanConvolution1x1 : public VulkanBasicExecution { +public: + VulkanConvolution1x1(VulkanBackend* backend, const Convolution2DCommon* convCommon, const float* weightPtr, const float* biasPtr, const int ic, const int oc); + ~VulkanConvolution1x1(); + ErrorCode onEncode(const std::vector& inputs, const std::vector& outputs, + const VulkanCommandPool::Buffer* cmdBuffer) override; + +private: + SharedPtr mPipeline; + std::vector> mCands; + std::shared_ptr mDescriptorSet; + std::shared_ptr mConv1x1Param; + const Convolution2DCommon* mConvCommon; + std::shared_ptr mKernel; + std::shared_ptr mKernelBuffer; + std::shared_ptr mBias; + std::vector> mCandidataGws; +}; + +} // end namespace MNN + +#endif /* VulkanConvolution1x1_hpp */ diff --git a/source/backend/vulkan/image/execution/glsl/convolution1x1.comp b/source/backend/vulkan/image/execution/glsl/convolution1x1.comp new file mode 100644 index 000000000..516bf7293 --- /dev/null +++ b/source/backend/vulkan/image/execution/glsl/convolution1x1.comp @@ -0,0 +1,69 @@ +#version 440 core + +#if defined(FP16) || defined(RELU_FP16) || defined(RELU6_FP16) +#extension GL_AMD_gpu_shader_half_float: enable +#define FLOAT4 f16vec4 +#else +#define FLOAT4 vec4 +#endif + +layout(set=0, binding=0) writeonly uniform image2D uOutput; + +layout(set=0, binding=1) uniform sampler2D uInput; + +layout(set=0, binding=2) uniform sampler2D uKernel; + +layout(set=0, binding=3) uniform sampler2D uBias; + +layout(set=0, binding=4) readonly uniform constBuffer { + ivec4 inputSize; // w h icDiv4 n + ivec4 outputSize; // w h ocDiv4 n +} uConstant; + +#define UP_DIV(x, y) (((x)+(y)-1)/(y)) + +// ow * oh * ocDiv4 on +layout (local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +void main() { + // ------------- + int outputIndexNHW = int(gl_GlobalInvocationID.x); + int outputIndexC4 = int(gl_GlobalInvocationID.y); + + if (outputIndexNHW >= uConstant.outputSize.w * uConstant.outputSize.y * uConstant.outputSize.x || outputIndexC4 >= uConstant.outputSize.z) { + return; + } + + int outputIndexW = outputIndexNHW % uConstant.outputSize.x; + int outputIndexNH = outputIndexNHW / uConstant.outputSize.x; + int outputIndexH = outputIndexNH % uConstant.outputSize.y; + int outputIndexN = outputIndexNH / uConstant.outputSize.y; + // ------------- + + FLOAT4 result = FLOAT4(texelFetch(uBias, ivec2(outputIndexC4, 0), 0)); + + for (int inputIndexC4 = 0; inputIndexC4 < uConstant.inputSize.z; inputIndexC4 ++) { + FLOAT4 inputValue = FLOAT4(texelFetch(uInput, ivec2(outputIndexW + inputIndexC4 * uConstant.inputSize.x, outputIndexH + outputIndexN * uConstant.inputSize.y), 0)); + int kernelIndexXbase = inputIndexC4 * 4; + FLOAT4 weight0 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 0, outputIndexC4), 0)); + FLOAT4 weight1 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 1, outputIndexC4), 0)); + FLOAT4 weight2 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 2, outputIndexC4), 0)); + FLOAT4 weight3 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 3, outputIndexC4), 0)); + + result += inputValue.x * weight0; + result += inputValue.y * weight1; + result += inputValue.z * weight2; + result += inputValue.w * weight3; + } + +#if defined(RELU_FP32) || defined(RELU_FP16) + result = FLOAT4(max(result, FLOAT4(0))); +#endif +#if defined(RELU6_FP32) || defined(RELU6_FP16) + result = FLOAT4(clamp(result, FLOAT4(0), FLOAT4(6))); +#endif + + imageStore(uOutput, ivec2(outputIndexW + outputIndexC4 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), result); + + return; +} \ No newline at end of file diff --git a/source/backend/vulkan/image/execution/glsl/convolution1x1_c8w4.comp b/source/backend/vulkan/image/execution/glsl/convolution1x1_c8w4.comp new file mode 100644 index 000000000..72e12411a --- /dev/null +++ b/source/backend/vulkan/image/execution/glsl/convolution1x1_c8w4.comp @@ -0,0 +1,163 @@ +#version 440 core + +#if defined(FP16) || defined(RELU_FP16) || defined(RELU6_FP16) +#extension GL_AMD_gpu_shader_half_float: enable +#define FLOAT4 f16vec4 +#else +#define FLOAT4 vec4 +#endif + +layout(set=0, binding=0) writeonly uniform image2D uOutput; + +layout(set=0, binding=1) uniform sampler2D uInput; + +layout(set=0, binding=2) uniform sampler2D uKernel; + +layout(set=0, binding=3) uniform sampler2D uBias; + +layout(set=0, binding=4) readonly uniform constBuffer { + ivec4 inputSize; // w h icDiv4 n + ivec4 outputSize; // w h ocDiv4 n +} uConstant; + +#define UP_DIV(x, y) (((x)+(y)-1)/(y)) + +// ow * oh * ocDiv4 on +layout (local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +void main() { + // ----------------------------- + int outputIndexNHW4 = int(gl_GlobalInvocationID.x); + int outputIndexC8 = int(gl_GlobalInvocationID.y); + int outputC8 = UP_DIV(uConstant.outputSize.z, 2); + int outputW4 = UP_DIV(uConstant.outputSize.x, 4); + + if (outputIndexNHW4 >= outputW4 * uConstant.outputSize.y * uConstant.outputSize.w || outputIndexC8 >= outputC8) { + return; + } + + int outputIndexW4 = outputIndexNHW4 % outputW4; + int outputIndexNH = outputIndexNHW4 / outputW4; + int outputIndexW0 = outputIndexW4 * 4; + int outputIndexW1 = outputIndexW0 + 1; + int outputIndexW2 = outputIndexW0 + 2; + int outputIndexW3 = outputIndexW0 + 3; + int outputIndexH = outputIndexNH % uConstant.outputSize.y; + int outputIndexN = outputIndexNH / uConstant.outputSize.y; + // ----------------------------- + + int outputIndexC0 = outputIndexC8 * 2; + int outputIndexC1 = outputIndexC0 + 1; + FLOAT4 resultW0C0 = FLOAT4(texelFetch(uBias, ivec2(outputIndexC0, 0), 0)); + FLOAT4 resultW1C0 = resultW0C0; + FLOAT4 resultW2C0 = resultW0C0; + FLOAT4 resultW3C0 = resultW0C0; + FLOAT4 resultW0C1 = FLOAT4(texelFetch(uBias, ivec2(outputIndexC1, 0), 0)); + FLOAT4 resultW1C1 = resultW0C1; + FLOAT4 resultW2C1 = resultW0C1; + FLOAT4 resultW3C1 = resultW0C1; + + for (int inputIndexC4 = 0; inputIndexC4 < uConstant.inputSize.z; inputIndexC4 ++) { + FLOAT4 inputValueW0 = FLOAT4(texelFetch(uInput, ivec2(outputIndexW0 + inputIndexC4 * uConstant.inputSize.x, outputIndexH + outputIndexN * uConstant.inputSize.y), 0)); + FLOAT4 inputValueW1 = FLOAT4(texelFetch(uInput, ivec2(outputIndexW1 + inputIndexC4 * uConstant.inputSize.x, outputIndexH + outputIndexN * uConstant.inputSize.y), 0)); + FLOAT4 inputValueW2 = FLOAT4(texelFetch(uInput, ivec2(outputIndexW2 + inputIndexC4 * uConstant.inputSize.x, outputIndexH + outputIndexN * uConstant.inputSize.y), 0)); + FLOAT4 inputValueW3 = FLOAT4(texelFetch(uInput, ivec2(outputIndexW3 + inputIndexC4 * uConstant.inputSize.x, outputIndexH + outputIndexN * uConstant.inputSize.y), 0)); + int kernelIndexXbase = inputIndexC4 * 4; + FLOAT4 weightC00 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 0, outputIndexC0), 0)); + FLOAT4 weightC01 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 1, outputIndexC0), 0)); + FLOAT4 weightC02 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 2, outputIndexC0), 0)); + FLOAT4 weightC03 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 3, outputIndexC0), 0)); + FLOAT4 weightC10 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 0, outputIndexC1), 0)); + FLOAT4 weightC11 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 1, outputIndexC1), 0)); + FLOAT4 weightC12 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 2, outputIndexC1), 0)); + FLOAT4 weightC13 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 3, outputIndexC1), 0)); + + resultW0C0 += inputValueW0.x * weightC00; + resultW0C0 += inputValueW0.y * weightC01; + resultW0C0 += inputValueW0.z * weightC02; + resultW0C0 += inputValueW0.w * weightC03; + + resultW0C1 += inputValueW0.x * weightC10; + resultW0C1 += inputValueW0.y * weightC11; + resultW0C1 += inputValueW0.z * weightC12; + resultW0C1 += inputValueW0.w * weightC13; + + resultW1C0 += inputValueW1.x * weightC00; + resultW1C0 += inputValueW1.y * weightC01; + resultW1C0 += inputValueW1.z * weightC02; + resultW1C0 += inputValueW1.w * weightC03; + + resultW1C1 += inputValueW1.x * weightC10; + resultW1C1 += inputValueW1.y * weightC11; + resultW1C1 += inputValueW1.z * weightC12; + resultW1C1 += inputValueW1.w * weightC13; + + resultW2C0 += inputValueW2.x * weightC00; + resultW2C0 += inputValueW2.y * weightC01; + resultW2C0 += inputValueW2.z * weightC02; + resultW2C0 += inputValueW2.w * weightC03; + + resultW2C1 += inputValueW2.x * weightC10; + resultW2C1 += inputValueW2.y * weightC11; + resultW2C1 += inputValueW2.z * weightC12; + resultW2C1 += inputValueW2.w * weightC13; + + resultW3C0 += inputValueW3.x * weightC00; + resultW3C0 += inputValueW3.y * weightC01; + resultW3C0 += inputValueW3.z * weightC02; + resultW3C0 += inputValueW3.w * weightC03; + + resultW3C1 += inputValueW3.x * weightC10; + resultW3C1 += inputValueW3.y * weightC11; + resultW3C1 += inputValueW3.z * weightC12; + resultW3C1 += inputValueW3.w * weightC13; + } + +#if defined(RELU_FP32) || defined(RELU_FP16) + resultW0C0 = FLOAT4(max(resultW0C0, FLOAT4(0))); + resultW0C1 = FLOAT4(max(resultW0C1, FLOAT4(0))); + resultW1C0 = FLOAT4(max(resultW1C0, FLOAT4(0))); + resultW1C1 = FLOAT4(max(resultW1C1, FLOAT4(0))); + resultW2C0 = FLOAT4(max(resultW2C0, FLOAT4(0))); + resultW2C1 = FLOAT4(max(resultW2C1, FLOAT4(0))); + resultW3C0 = FLOAT4(max(resultW3C0, FLOAT4(0))); + resultW3C1 = FLOAT4(max(resultW3C1, FLOAT4(0))); +#endif + +#if defined(RELU6_FP32) || defined(RELU6_FP16) + resultW0C0 = FLOAT4(clamp(resultW0C0, FLOAT4(0), FLOAT4(6))); + resultW0C1 = FLOAT4(clamp(resultW0C1, FLOAT4(0), FLOAT4(6))); + resultW1C0 = FLOAT4(clamp(resultW1C0, FLOAT4(0), FLOAT4(6))); + resultW1C1 = FLOAT4(clamp(resultW1C1, FLOAT4(0), FLOAT4(6))); + resultW2C0 = FLOAT4(clamp(resultW2C0, FLOAT4(0), FLOAT4(6))); + resultW2C1 = FLOAT4(clamp(resultW2C1, FLOAT4(0), FLOAT4(6))); + resultW3C0 = FLOAT4(clamp(resultW3C0, FLOAT4(0), FLOAT4(6))); + resultW3C1 = FLOAT4(clamp(resultW3C1, FLOAT4(0), FLOAT4(6))); +#endif + + imageStore(uOutput, ivec2(outputIndexW0 + outputIndexC0 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW0C0); + if (outputIndexW1 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW1 + outputIndexC0 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW1C0); + } + if (outputIndexW2 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW2 + outputIndexC0 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW2C0); + } + if (outputIndexW3 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW3 + outputIndexC0 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW3C0); + } + + if (outputIndexC1 < uConstant.outputSize.z) { + imageStore(uOutput, ivec2(outputIndexW0 + outputIndexC1 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW0C1); + if (outputIndexW1 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW1 + outputIndexC1 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW1C1); + } + if (outputIndexW2 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW2 + outputIndexC1 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW2C1); + } + if (outputIndexW3 < uConstant.outputSize.x) { + imageStore(uOutput, ivec2(outputIndexW3 + outputIndexC1 * uConstant.outputSize.x, outputIndexH + outputIndexN * uConstant.outputSize.y), resultW3C1); + } + } + + return; +} \ No newline at end of file diff --git a/source/backend/vulkan/image/execution/glsl/convolution1x1_w4.comp b/source/backend/vulkan/image/execution/glsl/convolution1x1_w4.comp new file mode 100644 index 000000000..734501aa6 --- /dev/null +++ b/source/backend/vulkan/image/execution/glsl/convolution1x1_w4.comp @@ -0,0 +1,121 @@ +#version 440 core + +#if defined(FP16) || defined(RELU_FP16) || defined(RELU6_FP16) +#extension GL_AMD_gpu_shader_half_float: enable +#define FLOAT4 f16vec4 +#else +#define FLOAT4 vec4 +#endif + +layout(set=0, binding=0) writeonly uniform image2D uOutput; +layout(set=0, binding=1) uniform sampler2D uInput; + +layout(set=0, binding=2) uniform sampler2D uKernel; + +layout(set=0, binding=3) uniform sampler2D uBias; + +layout(set=0, binding=4) readonly uniform constBuffer { + ivec4 inputSize; // w h icDiv4 n + ivec4 outputSize; // w h ocDiv4 n +} uConstant; + +#define UP_DIV(x, y) (((x)+(y)-1)/(y)) + +layout (local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +void main() { + // ------------- + int outputIndexNHW4 = int(gl_GlobalInvocationID.x); + int outputIndexC4 = int(gl_GlobalInvocationID.y); + int outputW4 = UP_DIV(uConstant.outputSize.x, 4); + + if (outputIndexNHW4 >= uConstant.outputSize.w * uConstant.outputSize.y * outputW4 || outputIndexC4 >= uConstant.outputSize.z) { + return; + } + + int outputIndexW4 = outputIndexNHW4 % outputW4; + int outputIndexNH = outputIndexNHW4 / outputW4; + int outputIndexH = outputIndexNH % uConstant.outputSize.y; + int outputIndexN = outputIndexNH / uConstant.outputSize.y; + // ------------- + + FLOAT4 result0 = FLOAT4(texelFetch(uBias, ivec2(outputIndexC4, 0), 0)); + FLOAT4 result1 = result0; + FLOAT4 result2 = result0; + FLOAT4 result3 = result0; + + int outputIndexWBase = outputIndexW4 * 4; + + for (int inputIndexC4 = 0; inputIndexC4 < uConstant.inputSize.z; inputIndexC4 ++) { + int inputPosWBase = outputIndexWBase + inputIndexC4 * uConstant.inputSize.x; + int inputPosH = outputIndexH + outputIndexN * uConstant.inputSize.y; + FLOAT4 inputValue0 = FLOAT4(texelFetch(uInput, ivec2(inputPosWBase, inputPosH), 0)); + FLOAT4 inputValue1 = FLOAT4(texelFetch(uInput, ivec2(inputPosWBase + 1, inputPosH), 0)); + FLOAT4 inputValue2 = FLOAT4(texelFetch(uInput, ivec2(inputPosWBase + 2, inputPosH), 0)); + FLOAT4 inputValue3 = FLOAT4(texelFetch(uInput, ivec2(inputPosWBase + 3, inputPosH), 0)); + + int kernelIndexXbase = inputIndexC4 * 4; + FLOAT4 weight0 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 0, outputIndexC4), 0)); + FLOAT4 weight1 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 1, outputIndexC4), 0)); + FLOAT4 weight2 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 2, outputIndexC4), 0)); + FLOAT4 weight3 = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexXbase + 3, outputIndexC4), 0)); + + result0 += inputValue0.x * weight0; + result0 += inputValue0.y * weight1; + result0 += inputValue0.z * weight2; + result0 += inputValue0.w * weight3; + + result1 += inputValue1.x * weight0; + result1 += inputValue1.y * weight1; + result1 += inputValue1.z * weight2; + result1 += inputValue1.w * weight3; + + result2 += inputValue2.x * weight0; + result2 += inputValue2.y * weight1; + result2 += inputValue2.z * weight2; + result2 += inputValue2.w * weight3; + + result3 += inputValue3.x * weight0; + result3 += inputValue3.y * weight1; + result3 += inputValue3.z * weight2; + result3 += inputValue3.w * weight3; + } + +#if defined(RELU_FP32) || defined(RELU_FP16) + result0 = FLOAT4(max(result0, FLOAT4(0))); + result1 = FLOAT4(max(result1, FLOAT4(0))); + result2 = FLOAT4(max(result2, FLOAT4(0))); + result3 = FLOAT4(max(result3, FLOAT4(0))); +#endif +#if defined(RELU6_FP32) || defined(RELU6_FP16) + result0 = FLOAT4(clamp(result0, FLOAT4(0), FLOAT4(6))); + result1 = FLOAT4(clamp(result1, FLOAT4(0), FLOAT4(6))); + result2 = FLOAT4(clamp(result2, FLOAT4(0), FLOAT4(6))); + result3 = FLOAT4(clamp(result3, FLOAT4(0), FLOAT4(6))); +#endif + + int outputPosWBase = outputIndexWBase + outputIndexC4 * uConstant.outputSize.x; + int outputPosH = outputIndexH + outputIndexN * uConstant.outputSize.y; + int WLeft = uConstant.outputSize.x - outputIndexW4 * 4; + if (WLeft == 1) { + imageStore(uOutput, ivec2(outputPosWBase, outputPosH), result0); + return; + } + if (WLeft == 2) { + imageStore(uOutput, ivec2(outputPosWBase, outputPosH), result0); + imageStore(uOutput, ivec2(outputPosWBase + 1, outputPosH), result1); + return; + } + if (WLeft == 3) { + imageStore(uOutput, ivec2(outputPosWBase, outputPosH), result0); + imageStore(uOutput, ivec2(outputPosWBase + 1, outputPosH), result1); + imageStore(uOutput, ivec2(outputPosWBase + 2, outputPosH), result2); + return; + } + imageStore(uOutput, ivec2(outputPosWBase, outputPosH), result0); + imageStore(uOutput, ivec2(outputPosWBase + 1, outputPosH), result1); + imageStore(uOutput, ivec2(outputPosWBase + 2, outputPosH), result2); + imageStore(uOutput, ivec2(outputPosWBase + 3, outputPosH), result3); + + return; +} \ No newline at end of file diff --git a/source/backend/vulkan/image/execution/glsl/convolutionDepthwise.comp b/source/backend/vulkan/image/execution/glsl/convolutionDepthwise.comp index b34b48c96..8ed147a4c 100644 --- a/source/backend/vulkan/image/execution/glsl/convolutionDepthwise.comp +++ b/source/backend/vulkan/image/execution/glsl/convolutionDepthwise.comp @@ -1,5 +1,12 @@ #version 440 core +#if defined(FP16) || defined(RELU_FP16) || defined(RELU6_FP16) +#extension GL_AMD_gpu_shader_half_float: enable +#define FLOAT4 f16vec4 +#else +#define FLOAT4 vec4 +#endif + layout(set=0, binding=0) writeonly uniform image2D uOutput; layout(set=0, binding=1) uniform sampler2D uInput; @@ -19,7 +26,7 @@ layout(set=0, binding=4) readonly uniform constBuffer { #define UP_DIV(x, y) (((x)+(y)-1)/(y)) -layout (local_size_x = 16, local_size_y = 16, local_size_z = 1) in; +layout (local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; void main() { @@ -35,26 +42,26 @@ void main() ivec2 sta = max(ivec2(0, 0), (UP_DIV(-s0, uConstant.dilate))); ivec2 end = min(uConstant.kernelSize, UP_DIV(uConstant.inputSize.xy - s0, uConstant.dilate)); int fx, fy, fz; - vec4 color = texelFetch(uBias, ivec2(oz, 0), 0); + FLOAT4 color = FLOAT4(texelFetch(uBias, ivec2(oz, 0), 0)); for (fy=sta.y; fy= 0 && inputIndexW0 < uConstant.inputSize.x) ? texelFetch(uInput, ivec2(inputIndexW0 + outputIndexC4 * uConstant.inputSize.x, inputPosH), 0) : vec4(0.0f); + FLOAT4 input1 = (inputIndexW0 >= 0 && inputIndexW0 < uConstant.inputSize.x) ? FLOAT4(texelFetch(uInput, ivec2(inputIndexW0 + outputIndexC4 * uConstant.inputSize.x, inputPosH), 0)) : FLOAT4(0); for (int kernelIndexW = 0; kernelIndexW < uConstant.kernelSize.x; kernelIndexW++) { // load input input0 = input1; inputIndexW0 = inputIndexOffset.x + kernelIndexW + 1; - input1 = (inputIndexW0 >= 0 && inputIndexW0 < uConstant.inputSize.x) ? texelFetch(uInput, ivec2(inputIndexW0 + outputIndexC4 * uConstant.inputSize.x, inputPosH), 0) : vec4(0.0f); + input1 = (inputIndexW0 >= 0 && inputIndexW0 < uConstant.inputSize.x) ? FLOAT4(texelFetch(uInput, ivec2(inputIndexW0 + outputIndexC4 * uConstant.inputSize.x, inputPosH), 0)) : FLOAT4(0); // load weight - vec4 weight = texelFetch(uKernel, ivec2(kernelIndexW + kernelIndexH * uConstant.kernelSize.x, outputIndexC4), 0); - result0 = fma(input0, weight, result0); - result1 = fma(input1, weight, result1); + FLOAT4 weight = FLOAT4(texelFetch(uKernel, ivec2(kernelIndexW + kernelIndexH * uConstant.kernelSize.x, outputIndexC4), 0)); + result0 += input0 * weight; + result1 += input1 * weight; } } -#ifdef RELU - result0 = max(result0, vec4(0)); - result1 = max(result1, vec4(0)); +#if defined(RELU_FP32) || defined(RELU_FP16) + result0 = FLOAT4(max(result0, FLOAT4(0))); + result1 = FLOAT4(max(result1, FLOAT4(0))); #endif -#ifdef RELU6 - result0 = clamp(result0, vec4(0), vec4(6)); - result1 = clamp(result1, vec4(0), vec4(6)); +#if defined(RELU6_FP32) || defined(RELU6_FP16) + result0 = FLOAT4(clamp(result0, FLOAT4(0), FLOAT4(6))); + result1 = FLOAT4(clamp(result1, FLOAT4(0), FLOAT4(6))); #endif // write output diff --git a/source/backend/vulkan/image/execution/glsl/gemm16x16.comp b/source/backend/vulkan/image/execution/glsl/gemm16x16.comp index b71b66207..2f9966ee9 100644 --- a/source/backend/vulkan/image/execution/glsl/gemm16x16.comp +++ b/source/backend/vulkan/image/execution/glsl/gemm16x16.comp @@ -17,7 +17,7 @@ layout(set=0, binding=3) readonly uniform constBuffer { ivec4 outputSize; }uConst; -layout (local_size_x = 8, local_size_y = 8, local_size_z = 1) in; +layout (local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; void main() { diff --git a/source/backend/vulkan/image/execution/glsl/macro.json b/source/backend/vulkan/image/execution/glsl/macro.json index 6e443f12d..7da1b8088 100644 --- a/source/backend/vulkan/image/execution/glsl/macro.json +++ b/source/backend/vulkan/image/execution/glsl/macro.json @@ -89,21 +89,25 @@ "RELU6" ], "convolutionDepthwise.comp":[ - "RELU", - "RELU6" + "FP32", + "RELU_FP32", + "RELU6_FP32", + "FP16", + "RELU_FP16", + "RELU6_FP16" ], "convolutionDepthwise_s1d1_w2.comp":[ - "RELU", - "RELU6" + "FP32", + "RELU_FP32", + "RELU6_FP32", + "FP16", + "RELU_FP16", + "RELU6_FP16" ], "deconvolutionDepthwise.comp":[ "RELU", "RELU6" ], - "convolutionDepthwiseMali.comp":[ - "RELU", - "RELU6" - ], "gridSampleNearest.comp":[ "PAD_MODE_ZEROS" ], @@ -128,5 +132,29 @@ "AXIS_H", "AXIS_W", "AXIS_C" + ], + "convolution1x1.comp":[ + "FP32", + "RELU_FP32", + "RELU6_FP32", + "FP16", + "RELU_FP16", + "RELU6_FP16" + ], + "convolution1x1_w4.comp":[ + "FP32", + "RELU_FP32", + "RELU6_FP32", + "FP16", + "RELU_FP16", + "RELU6_FP16" + ], + "convolution1x1_c8w4.comp":[ + "FP32", + "RELU_FP32", + "RELU6_FP32", + "FP16", + "RELU_FP16", + "RELU6_FP16" ] } diff --git a/source/backend/vulkan/image/shaders/AllShader.h b/source/backend/vulkan/image/shaders/AllShader.h index 7e9367340..3c5bce6c7 100644 --- a/source/backend/vulkan/image/shaders/AllShader.h +++ b/source/backend/vulkan/image/shaders/AllShader.h @@ -16,10 +16,6 @@ extern const unsigned char glsl_softmaxImage_AXIS_C_comp[]; extern unsigned int glsl_softmaxImage_AXIS_C_comp_len; extern const unsigned char glsl_convolutionDepthwiseMali_comp[]; extern unsigned int glsl_convolutionDepthwiseMali_comp_len; -extern const unsigned char glsl_convolutionDepthwiseMali_RELU_comp[]; -extern unsigned int glsl_convolutionDepthwiseMali_RELU_comp_len; -extern const unsigned char glsl_convolutionDepthwiseMali_RELU6_comp[]; -extern unsigned int glsl_convolutionDepthwiseMali_RELU6_comp_len; extern const unsigned char glsl_relu_comp[]; extern unsigned int glsl_relu_comp_len; extern const unsigned char glsl_unaryImage_comp[]; @@ -88,14 +84,36 @@ extern const unsigned char glsl_im2col_comp[]; extern unsigned int glsl_im2col_comp_len; extern const unsigned char glsl_convolutionDepthwise_comp[]; extern unsigned int glsl_convolutionDepthwise_comp_len; -extern const unsigned char glsl_convolutionDepthwise_RELU_comp[]; -extern unsigned int glsl_convolutionDepthwise_RELU_comp_len; -extern const unsigned char glsl_convolutionDepthwise_RELU6_comp[]; -extern unsigned int glsl_convolutionDepthwise_RELU6_comp_len; +extern const unsigned char glsl_convolutionDepthwise_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_RELU_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_RELU_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_RELU6_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_RELU6_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_FP16_comp_len; +extern const unsigned char glsl_convolutionDepthwise_RELU_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_RELU_FP16_comp_len; +extern const unsigned char glsl_convolutionDepthwise_RELU6_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_RELU6_FP16_comp_len; extern const unsigned char glsl_gridSampleBilinear_comp[]; extern unsigned int glsl_gridSampleBilinear_comp_len; extern const unsigned char glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp[]; extern unsigned int glsl_gridSampleBilinear_PAD_MODE_ZEROS_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_FP32_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_RELU_FP32_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_RELU_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_RELU6_FP32_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_RELU6_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_FP16_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_RELU_FP16_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_RELU_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_c8w4_RELU6_FP16_comp[]; +extern unsigned int glsl_convolution1x1_c8w4_RELU6_FP16_comp_len; extern const unsigned char glsl_gridSampleNearest_comp[]; extern unsigned int glsl_gridSampleNearest_comp_len; extern const unsigned char glsl_gridSampleNearest_PAD_MODE_ZEROS_comp[]; @@ -182,6 +200,20 @@ extern const unsigned char glsl_winogradTransformDest2_3_1_RELU6_comp[]; extern unsigned int glsl_winogradTransformDest2_3_1_RELU6_comp_len; extern const unsigned char glsl_winogradTransformSource2_3_1_comp[]; extern unsigned int glsl_winogradTransformSource2_3_1_comp_len; +extern const unsigned char glsl_convolution1x1_comp[]; +extern unsigned int glsl_convolution1x1_comp_len; +extern const unsigned char glsl_convolution1x1_FP32_comp[]; +extern unsigned int glsl_convolution1x1_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_RELU_FP32_comp[]; +extern unsigned int glsl_convolution1x1_RELU_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_RELU6_FP32_comp[]; +extern unsigned int glsl_convolution1x1_RELU6_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_FP16_comp[]; +extern unsigned int glsl_convolution1x1_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_RELU_FP16_comp[]; +extern unsigned int glsl_convolution1x1_RELU_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_RELU6_FP16_comp[]; +extern unsigned int glsl_convolution1x1_RELU6_FP16_comp_len; extern const unsigned char glsl_col2Im_comp[]; extern unsigned int glsl_col2Im_comp_len; extern const unsigned char glsl_col2Im_RELU_comp[]; @@ -212,10 +244,18 @@ extern const unsigned char glsl_binary_blit_SQUDIFF_comp[]; extern unsigned int glsl_binary_blit_SQUDIFF_comp_len; extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_comp[]; extern unsigned int glsl_convolutionDepthwise_s1d1_w2_comp_len; -extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU_comp[]; -extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU_comp_len; -extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU6_comp[]; -extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU6_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU6_FP32_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_FP16_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU_FP16_comp_len; +extern const unsigned char glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp[]; +extern unsigned int glsl_convolutionDepthwise_s1d1_w2_RELU6_FP16_comp_len; extern const unsigned char glsl_matmul_output_comp[]; extern unsigned int glsl_matmul_output_comp_len; extern const unsigned char glsl_matmul_output_BIAS_comp[]; @@ -252,4 +292,18 @@ extern const unsigned char glsl_argmax_ARGMIN_comp[]; extern unsigned int glsl_argmax_ARGMIN_comp_len; extern const unsigned char glsl_buffer2Image3D_comp[]; extern unsigned int glsl_buffer2Image3D_comp_len; +extern const unsigned char glsl_convolution1x1_w4_comp[]; +extern unsigned int glsl_convolution1x1_w4_comp_len; +extern const unsigned char glsl_convolution1x1_w4_FP32_comp[]; +extern unsigned int glsl_convolution1x1_w4_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_w4_RELU_FP32_comp[]; +extern unsigned int glsl_convolution1x1_w4_RELU_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_w4_RELU6_FP32_comp[]; +extern unsigned int glsl_convolution1x1_w4_RELU6_FP32_comp_len; +extern const unsigned char glsl_convolution1x1_w4_FP16_comp[]; +extern unsigned int glsl_convolution1x1_w4_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_w4_RELU_FP16_comp[]; +extern unsigned int glsl_convolution1x1_w4_RELU_FP16_comp_len; +extern const unsigned char glsl_convolution1x1_w4_RELU6_FP16_comp[]; +extern unsigned int glsl_convolution1x1_w4_RELU6_FP16_comp_len; #endif \ No newline at end of file diff --git a/source/backend/vulkan/runtime/VulkanRuntime.cpp b/source/backend/vulkan/runtime/VulkanRuntime.cpp index 191158113..c1218dc4d 100644 --- a/source/backend/vulkan/runtime/VulkanRuntime.cpp +++ b/source/backend/vulkan/runtime/VulkanRuntime.cpp @@ -114,6 +114,16 @@ VulkanRuntime::VulkanRuntime(const Backend::Info& info, std::shared_ptr(dev, VK_FILTER_NEAREST, VK_SAMPLER_ADDRESS_MODE_CLAMP_TO_EDGE); mPipelineFactory = std::make_shared(dev); mQueryPool = std::make_shared(dev); + + std::vector legalModeValues = {0x00000001, 0x00000002, 0x00000004, + 0x00000201, 0x00000202, 0x00000204}; + auto iter = std::find(legalModeValues.begin(), legalModeValues.end(), (uint32_t)mInfo.gpuMode); + if (iter == legalModeValues.end()) { + MNN_PRINT("The customized gpu mode is illegal for Vulkan backend. Using the default mode.\n"); + mGpuMode = 0x00000004; + } else { + mGpuMode = mInfo.gpuMode; + } } VulkanRuntime::~VulkanRuntime() { @@ -186,6 +196,71 @@ int VulkanRuntime::onGetRuntimeStatus(RuntimeStatus statusEnum) const { } return 0; } + +bool VulkanRuntime::onSetCache(const void* buffer, size_t size) { + // check the validity of the buffer + if (nullptr == buffer) { + mTuneBuffer.clear(); + return false; + } + + flatbuffers::Verifier verifier(static_cast(buffer), size); + if (!VKCache::VerifyTuneInfoCacheBuffer(verifier)) { + return false; + } + + auto tuneInfoCache = VKCache::GetTuneInfoCache(buffer); + auto tuneInfos = tuneInfoCache->TuneInfos(); + if (!tuneInfos) { + return false; + } + // read from buffer, write to mTuneMap + for (const auto & tuneInfo : * tuneInfos) { + VKTuneKey k; + k.shaderName = tuneInfo->shaderName()->str(); + k.gws = {tuneInfo->gws()->x(), tuneInfo->gws()->y(), tuneInfo->gws()->z()}; + + VKTuneValue v; + v.optimalLws = {tuneInfo->optimalLws()->x(), tuneInfo->optimalLws()->y(), tuneInfo->optimalLws()->z()}; + v.optimalCost = tuneInfo->optimalCost(); + mTuneMap[k] = v; + } + + return true; +} + +std::pair VulkanRuntime::onGetCache() { + std::unique_ptr builder(new flatbuffers::FlatBufferBuilder()); + std::unique_ptr tuneInfoCache(new VKCache::TuneInfoCacheT()); + + for (const auto & kvPair : mTuneMap) { + const VKTuneKey & k = kvPair.first; + const VKTuneValue & v = kvPair.second; + std::unique_ptr tuneInfo(new VKCache::TuneInfoT()); + tuneInfo->shaderName = k.shaderName; + + std::unique_ptr gwsTemp(new VKCache::WorkSizeT()); + gwsTemp->x = k.gws[0]; gwsTemp->y = k.gws[1]; gwsTemp->z = k.gws[2]; + tuneInfo->gws = std::move(gwsTemp); + + std::unique_ptr optimalLwsTemp(new VKCache::WorkSizeT()); + optimalLwsTemp->x = v.optimalLws[0]; optimalLwsTemp->y = v.optimalLws[1]; optimalLwsTemp->z = v.optimalLws[2]; + tuneInfo->optimalLws = std::move(optimalLwsTemp); + + tuneInfo->optimalCost = v.optimalCost; + + tuneInfoCache->TuneInfos.push_back(std::move(tuneInfo)); + } + + auto tuneInfoCacheOffset = VKCache::TuneInfoCache::Pack(*(builder.get()), tuneInfoCache.get()); + builder->Finish(tuneInfoCacheOffset); + uint8_t *bufTemp = builder->GetBufferPointer(); + size_t size = builder->GetSize(); + mTuneBuffer.resize(size); + ::memcpy(mTuneBuffer.data(), bufTemp, size); + return std::make_pair(mTuneBuffer.data(), size); +} + class VulkanRuntimeCreator : public RuntimeCreator { public: virtual Runtime* onCreate(const Backend::Info& info) const { diff --git a/source/backend/vulkan/runtime/VulkanRuntime.hpp b/source/backend/vulkan/runtime/VulkanRuntime.hpp index 3c04c9808..8ade36944 100644 --- a/source/backend/vulkan/runtime/VulkanRuntime.hpp +++ b/source/backend/vulkan/runtime/VulkanRuntime.hpp @@ -9,6 +9,10 @@ #ifndef VulkanRuntime_hpp #define VulkanRuntime_hpp #include +#include +#include +#include +#include "VKCache_generated.h" #include "VulkanBuffer.hpp" #include "VulkanCommandPool.hpp" #include "VulkanDevice.hpp" @@ -22,6 +26,31 @@ #include namespace MNN { + +struct VKTuneKey { + std::string shaderName; + std::array gws; + + bool operator==(const VKTuneKey & other) const { + return (shaderName == other.shaderName) && (gws == other.gws); + } +}; + +struct VKTuneValue { + std::array optimalLws; + float optimalCost; +}; + +struct VkTuneHash { + size_t operator()(const VKTuneKey & key) const { + size_t hs = std::hash()(key.shaderName); + size_t h0 = std::hash()(key.gws[0]); + size_t h1 = std::hash()(key.gws[1]); + size_t h2 = std::hash()(key.gws[2]); + return hs & h0 & h1 & h2; + } +}; + class VulkanRuntime : public Runtime { public: virtual ~ VulkanRuntime(); @@ -34,6 +63,9 @@ class VulkanRuntime : public Runtime { std::shared_ptr allocUniform(const void* src = nullptr, int size = 0); void recycleUniform(std::shared_ptr buffer); static VulkanRuntime* create(const Backend::Info& info); + virtual std::pair onGetCache() override; + virtual bool onSetCache(const void* buffer, size_t size) override; + private: VulkanRuntime(const Backend::Info& info, std::shared_ptr device, std::shared_ptr instance); Backend::Info mInfo; @@ -53,6 +85,11 @@ class VulkanRuntime : public Runtime { float mFlops = 0.0f; friend class VulkanBackend; GPUType mGpuType = OTHER; + int mGpuMode; +// member variables related to auto tuning +private: + mutable std::unordered_map mTuneMap; + std::vector mTuneBuffer; }; } #endif diff --git a/source/backend/vulkan/runtime/vulkan_wrapper.cpp b/source/backend/vulkan/runtime/vulkan_wrapper.cpp index 1da2353a3..eea42f351 100644 --- a/source/backend/vulkan/runtime/vulkan_wrapper.cpp +++ b/source/backend/vulkan/runtime/vulkan_wrapper.cpp @@ -73,6 +73,8 @@ int InitVulkanOnce(void) { MNN_DLSYM(libvulkan, "vkGetPhysicalDeviceImageFormatProperties")); vkGetPhysicalDeviceProperties = reinterpret_cast(MNN_DLSYM(libvulkan, "vkGetPhysicalDeviceProperties")); + vkGetPhysicalDeviceProperties2 = + reinterpret_cast(MNN_DLSYM(libvulkan, "vkGetPhysicalDeviceProperties2")); vkGetPhysicalDeviceQueueFamilyProperties = reinterpret_cast( MNN_DLSYM(libvulkan, "vkGetPhysicalDeviceQueueFamilyProperties")); vkGetPhysicalDeviceMemoryProperties = reinterpret_cast( @@ -331,6 +333,7 @@ PFN_vkGetPhysicalDeviceFeatures vkGetPhysicalDeviceFeatures; PFN_vkGetPhysicalDeviceFormatProperties vkGetPhysicalDeviceFormatProperties; PFN_vkGetPhysicalDeviceImageFormatProperties vkGetPhysicalDeviceImageFormatProperties; PFN_vkGetPhysicalDeviceProperties vkGetPhysicalDeviceProperties; +PFN_vkGetPhysicalDeviceProperties2 vkGetPhysicalDeviceProperties2; PFN_vkGetPhysicalDeviceQueueFamilyProperties vkGetPhysicalDeviceQueueFamilyProperties; PFN_vkGetPhysicalDeviceMemoryProperties vkGetPhysicalDeviceMemoryProperties; PFN_vkGetInstanceProcAddr vkGetInstanceProcAddr; diff --git a/source/backend/vulkan/schema/VKCache.fbs b/source/backend/vulkan/schema/VKCache.fbs new file mode 100644 index 000000000..28b608126 --- /dev/null +++ b/source/backend/vulkan/schema/VKCache.fbs @@ -0,0 +1,22 @@ +namespace VKCache; + +attribute "priority"; + +table WorkSize { + x: uint; + y: uint; + z: uint; +} + +table TuneInfo { + shaderName: string; + gws: WorkSize; + optimalLws: WorkSize; + optimalCost: float; +} + +table TuneInfoCache { + TuneInfos: [TuneInfo]; +} + +root_type TuneInfoCache; diff --git a/source/backend/vulkan/schema/current/VKCache_generated.h b/source/backend/vulkan/schema/current/VKCache_generated.h new file mode 100644 index 000000000..13baed286 --- /dev/null +++ b/source/backend/vulkan/schema/current/VKCache_generated.h @@ -0,0 +1,430 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_VKCACHE_VKCACHE_H_ +#define FLATBUFFERS_GENERATED_VKCACHE_VKCACHE_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace VKCache { + +struct WorkSize; +struct WorkSizeT; + +struct TuneInfo; +struct TuneInfoT; + +struct TuneInfoCache; +struct TuneInfoCacheT; + +inline const flatbuffers::TypeTable *WorkSizeTypeTable(); + +inline const flatbuffers::TypeTable *TuneInfoTypeTable(); + +inline const flatbuffers::TypeTable *TuneInfoCacheTypeTable(); + +struct WorkSizeT : public flatbuffers::NativeTable { + typedef WorkSize TableType; + uint32_t x; + uint32_t y; + uint32_t z; + WorkSizeT() + : x(0), + y(0), + z(0) { + } +}; + +struct WorkSize FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WorkSizeT NativeTableType; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return WorkSizeTypeTable(); + } + uint32_t x() const { + return GetField(4, 0); + } + uint32_t y() const { + return GetField(6, 0); + } + uint32_t z() const { + return GetField(8, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, 4) && + VerifyField(verifier, 6) && + VerifyField(verifier, 8) && + verifier.EndTable(); + } + WorkSizeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WorkSizeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WorkSizeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WorkSizeBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_x(uint32_t x) { + fbb_.AddElement(4, x, 0); + } + void add_y(uint32_t y) { + fbb_.AddElement(6, y, 0); + } + void add_z(uint32_t z) { + fbb_.AddElement(8, z, 0); + } + explicit WorkSizeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + WorkSizeBuilder &operator=(const WorkSizeBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWorkSize( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t x = 0, + uint32_t y = 0, + uint32_t z = 0) { + WorkSizeBuilder builder_(_fbb); + builder_.add_z(z); + builder_.add_y(y); + builder_.add_x(x); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWorkSize(flatbuffers::FlatBufferBuilder &_fbb, const WorkSizeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TuneInfoT : public flatbuffers::NativeTable { + typedef TuneInfo TableType; + std::string shaderName; + std::unique_ptr gws; + std::unique_ptr optimalLws; + float optimalCost; + TuneInfoT() + : optimalCost(0.0f) { + } +}; + +struct TuneInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TuneInfoT NativeTableType; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TuneInfoTypeTable(); + } + const flatbuffers::String *shaderName() const { + return GetPointer(4); + } + const WorkSize *gws() const { + return GetPointer(6); + } + const WorkSize *optimalLws() const { + return GetPointer(8); + } + float optimalCost() const { + return GetField(10, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, 4) && + verifier.VerifyString(shaderName()) && + VerifyOffset(verifier, 6) && + verifier.VerifyTable(gws()) && + VerifyOffset(verifier, 8) && + verifier.VerifyTable(optimalLws()) && + VerifyField(verifier, 10) && + verifier.EndTable(); + } + TuneInfoT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TuneInfoT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TuneInfoBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_shaderName(flatbuffers::Offset shaderName) { + fbb_.AddOffset(4, shaderName); + } + void add_gws(flatbuffers::Offset gws) { + fbb_.AddOffset(6, gws); + } + void add_optimalLws(flatbuffers::Offset optimalLws) { + fbb_.AddOffset(8, optimalLws); + } + void add_optimalCost(float optimalCost) { + fbb_.AddElement(10, optimalCost, 0.0f); + } + explicit TuneInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + TuneInfoBuilder &operator=(const TuneInfoBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTuneInfo( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset shaderName = 0, + flatbuffers::Offset gws = 0, + flatbuffers::Offset optimalLws = 0, + float optimalCost = 0.0f) { + TuneInfoBuilder builder_(_fbb); + builder_.add_optimalCost(optimalCost); + builder_.add_optimalLws(optimalLws); + builder_.add_gws(gws); + builder_.add_shaderName(shaderName); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTuneInfo(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TuneInfoCacheT : public flatbuffers::NativeTable { + typedef TuneInfoCache TableType; + std::vector> TuneInfos; + TuneInfoCacheT() { + } +}; + +struct TuneInfoCache FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TuneInfoCacheT NativeTableType; + static const flatbuffers::TypeTable *MiniReflectTypeTable() { + return TuneInfoCacheTypeTable(); + } + const flatbuffers::Vector> *TuneInfos() const { + return GetPointer> *>(4); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, 4) && + verifier.VerifyVector(TuneInfos()) && + verifier.VerifyVectorOfTables(TuneInfos()) && + verifier.EndTable(); + } + TuneInfoCacheT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TuneInfoCacheT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoCacheT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TuneInfoCacheBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_TuneInfos(flatbuffers::Offset>> TuneInfos) { + fbb_.AddOffset(4, TuneInfos); + } + explicit TuneInfoCacheBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + TuneInfoCacheBuilder &operator=(const TuneInfoCacheBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTuneInfoCache( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> TuneInfos = 0) { + TuneInfoCacheBuilder builder_(_fbb); + builder_.add_TuneInfos(TuneInfos); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTuneInfoCache(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoCacheT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +inline WorkSizeT *WorkSize::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new WorkSizeT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void WorkSize::UnPackTo(WorkSizeT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = x(); _o->x = _e; }; + { auto _e = y(); _o->y = _e; }; + { auto _e = z(); _o->z = _e; }; +} + +inline flatbuffers::Offset WorkSize::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WorkSizeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateWorkSize(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateWorkSize(flatbuffers::FlatBufferBuilder &_fbb, const WorkSizeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WorkSizeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _x = _o->x; + auto _y = _o->y; + auto _z = _o->z; + return VKCache::CreateWorkSize( + _fbb, + _x, + _y, + _z); +} + +inline TuneInfoT *TuneInfo::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new TuneInfoT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void TuneInfo::UnPackTo(TuneInfoT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = shaderName(); if (_e) _o->shaderName = _e->str(); }; + { auto _e = gws(); if (_e) _o->gws = std::unique_ptr(_e->UnPack(_resolver)); }; + { auto _e = optimalLws(); if (_e) _o->optimalLws = std::unique_ptr(_e->UnPack(_resolver)); }; + { auto _e = optimalCost(); _o->optimalCost = _e; }; +} + +inline flatbuffers::Offset TuneInfo::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTuneInfo(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTuneInfo(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TuneInfoT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _shaderName = _o->shaderName.empty() ? 0 : _fbb.CreateString(_o->shaderName); + auto _gws = _o->gws ? CreateWorkSize(_fbb, _o->gws.get(), _rehasher) : 0; + auto _optimalLws = _o->optimalLws ? CreateWorkSize(_fbb, _o->optimalLws.get(), _rehasher) : 0; + auto _optimalCost = _o->optimalCost; + return VKCache::CreateTuneInfo( + _fbb, + _shaderName, + _gws, + _optimalLws, + _optimalCost); +} + +inline TuneInfoCacheT *TuneInfoCache::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new TuneInfoCacheT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void TuneInfoCache::UnPackTo(TuneInfoCacheT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = TuneInfos(); if (_e) { _o->TuneInfos.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->TuneInfos[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); } } }; +} + +inline flatbuffers::Offset TuneInfoCache::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoCacheT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTuneInfoCache(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTuneInfoCache(flatbuffers::FlatBufferBuilder &_fbb, const TuneInfoCacheT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TuneInfoCacheT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _TuneInfos = _o->TuneInfos.size() ? _fbb.CreateVector> (_o->TuneInfos.size(), [](size_t i, _VectorArgs *__va) { return CreateTuneInfo(*__va->__fbb, __va->__o->TuneInfos[i].get(), __va->__rehasher); }, &_va ) : 0; + return VKCache::CreateTuneInfoCache( + _fbb, + _TuneInfos); +} + +inline const flatbuffers::TypeTable *WorkSizeTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_UINT, 0, -1 }, + { flatbuffers::ET_UINT, 0, -1 } + }; + static const char * const names[] = { + "x", + "y", + "z" + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 3, type_codes, nullptr, nullptr, names + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TuneInfoTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_STRING, 0, -1 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_SEQUENCE, 0, 0 }, + { flatbuffers::ET_FLOAT, 0, -1 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + WorkSizeTypeTable + }; + static const char * const names[] = { + "shaderName", + "gws", + "optimalLws", + "optimalCost" + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 4, type_codes, type_refs, nullptr, names + }; + return &tt; +} + +inline const flatbuffers::TypeTable *TuneInfoCacheTypeTable() { + static const flatbuffers::TypeCode type_codes[] = { + { flatbuffers::ET_SEQUENCE, 1, 0 } + }; + static const flatbuffers::TypeFunction type_refs[] = { + TuneInfoTypeTable + }; + static const char * const names[] = { + "TuneInfos" + }; + static const flatbuffers::TypeTable tt = { + flatbuffers::ST_TABLE, 1, type_codes, type_refs, nullptr, names + }; + return &tt; +} + +inline const VKCache::TuneInfoCache *GetTuneInfoCache(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const VKCache::TuneInfoCache *GetSizePrefixedTuneInfoCache(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline bool VerifyTuneInfoCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(nullptr); +} + +inline bool VerifySizePrefixedTuneInfoCacheBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(nullptr); +} + +inline void FinishTuneInfoCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root); +} + +inline void FinishSizePrefixedTuneInfoCacheBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root); +} + +inline std::unique_ptr UnPackTuneInfoCache( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetTuneInfoCache(buf)->UnPack(res)); +} + +} // namespace VKCache + +#endif // FLATBUFFERS_GENERATED_VKCACHE_VKCACHE_H_ diff --git a/source/backend/vulkan/schema/generate.sh b/source/backend/vulkan/schema/generate.sh new file mode 100755 index 000000000..9ec76578a --- /dev/null +++ b/source/backend/vulkan/schema/generate.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# check is flatbuffer installed or not +FLATC=../../../../../3rd_party/flatbuffers/tmp/flatc + +# clean up +echo "*** cleaning up ***" +rm -f current/*.h +[ ! -d current ] && mkdir current + +# flatc all fbs +pushd current > /dev/null +echo "*** generating fbs under $DIR ***" +find ../*.fbs | xargs ${FLATC} -c -b --gen-object-api --reflect-names + +popd > /dev/null + +# finish +echo "*** done ***" diff --git a/source/backend/vulkan/vulkan/vulkan_wrapper.h b/source/backend/vulkan/vulkan/vulkan_wrapper.h index 1a2113437..258ec1e25 100644 --- a/source/backend/vulkan/vulkan/vulkan_wrapper.h +++ b/source/backend/vulkan/vulkan/vulkan_wrapper.h @@ -42,6 +42,7 @@ extern PFN_vkGetPhysicalDeviceFeatures vkGetPhysicalDeviceFeatures; extern PFN_vkGetPhysicalDeviceFormatProperties vkGetPhysicalDeviceFormatProperties; extern PFN_vkGetPhysicalDeviceImageFormatProperties vkGetPhysicalDeviceImageFormatProperties; extern PFN_vkGetPhysicalDeviceProperties vkGetPhysicalDeviceProperties; +extern PFN_vkGetPhysicalDeviceProperties2 vkGetPhysicalDeviceProperties2; extern PFN_vkGetPhysicalDeviceQueueFamilyProperties vkGetPhysicalDeviceQueueFamilyProperties; extern PFN_vkGetPhysicalDeviceMemoryProperties vkGetPhysicalDeviceMemoryProperties; extern PFN_vkGetInstanceProcAddr vkGetInstanceProcAddr; diff --git a/source/core/Backend.hpp b/source/core/Backend.hpp index 4a9713b7a..d12604d8f 100644 --- a/source/core/Backend.hpp +++ b/source/core/Backend.hpp @@ -345,6 +345,8 @@ class Runtime : public NonCopyable { void setAsyncWork(std::future&& future); MNN_PUBLIC void waitAsyncWork(); + mutable ErrorCode pCurrentStatus = NO_ERROR; + // TODO: Move to Backend void* pMeta; private: diff --git a/source/core/BufferAllocator.cpp b/source/core/BufferAllocator.cpp index 855f3b20d..17e517a57 100644 --- a/source/core/BufferAllocator.cpp +++ b/source/core/BufferAllocator.cpp @@ -71,6 +71,8 @@ class MmapAllocator : public BufferAllocator::Allocator { std::string mPosfix; int mAllocTimes = 0; bool mRemove; + bool mNewMmap = false; + public: MmapAllocator(const char* dirName, const char* prefix, const char* posfix, bool autoRemove) { if (nullptr != dirName) { @@ -98,7 +100,8 @@ class MmapAllocator : public BufferAllocator::Allocator { } virtual MemChunk onAlloc(size_t size, size_t align) { MNN_ASSERT(size > 0); - std::string fileName = MNNFilePathConcat(mFileName, mPrefix + std::to_string(mAllocTimes) + "." + mPosfix); + std::string name = mPrefix + std::to_string(mAllocTimes) + "." + mPosfix; + std::string fileName = MNNFilePathConcat(mFileName, name); file_t file; if (MNNFileExist(fileName.c_str())) { file = MNNOpenFile(fileName.c_str(), MNN_FILE_READ | MNN_FILE_WRITE); @@ -109,13 +112,14 @@ class MmapAllocator : public BufferAllocator::Allocator { if (NO_ERROR != code) { MNN_ERROR("Set File size %lu error= %d\n", size, code); } + mNewMmap = true; } void* ptr = MNNMmapFile(file, size); mCache.insert(std::make_pair(ptr, std::make_tuple(file, size, fileName))); mAllocTimes++; return MemChunk(ptr, 0); } - virtual void onRelease(MemChunk chunk) { + virtual void onRelease(MemChunk chunk) override { MNN_ASSERT(chunk.second == 0); auto iter = mCache.find(chunk.first); if (iter == mCache.end()) { @@ -131,6 +135,16 @@ class MmapAllocator : public BufferAllocator::Allocator { mCache.erase(iter); mAllocTimes = 0; } + virtual void sync() override { + if (!mRemove && mNewMmap) { + for (auto& iter : mCache) { + MNNMmapSync(iter.first, std::get<1>(iter.second)); + } + std::string cacheName = mPrefix + "sync." + mPosfix; + std::string fileName = MNNFilePathConcat(mFileName, cacheName); + MNNCreateFile(fileName.c_str()); + } + } }; class RecurseAllocator : public BufferAllocator::Allocator { public: @@ -340,6 +354,10 @@ void EagerBufferAllocator::endGroup() { mCurrentFreeList = nullptr; } +void EagerBufferAllocator::sync() { + mAllocator->sync(); +} + std::pair EagerBufferAllocator::getFromFreeList(FREELIST* list, size_t size, bool permiteSplit, size_t align) { #ifdef MNN_DEBUG_MEMORY return std::make_pair(nullptr, 0); diff --git a/source/core/BufferAllocator.hpp b/source/core/BufferAllocator.hpp index bcfdfbffb..3929a14d0 100644 --- a/source/core/BufferAllocator.hpp +++ b/source/core/BufferAllocator.hpp @@ -84,6 +84,7 @@ class MNN_PUBLIC BufferAllocator : public NonCopyable { virtual ~ Allocator() = default; virtual MemChunk onAlloc(size_t size, size_t align) = 0; virtual void onRelease(MemChunk chunk) = 0; + virtual void sync() {}; static std::shared_ptr createDefault(); static std::shared_ptr createMmap(const char* dirName, const char* prefix, const char* posfix, bool autoRemove = true); static std::shared_ptr createRecurse(BufferAllocator* parent); @@ -103,6 +104,7 @@ class MNN_PUBLIC BufferAllocator : public NonCopyable { virtual void reset() {} virtual ErrorCode compute(); virtual ErrorCode apply(); + virtual void sync() {} protected: size_t mTotalSize = 0; }; @@ -163,7 +165,7 @@ class MNN_PUBLIC EagerBufferAllocator : public BufferAllocator { void barrierEnd() override; void beginGroup() override; void endGroup() override; - + void sync() override; private: class Node : public RefCount { public: diff --git a/source/core/ConvolutionCommon.hpp b/source/core/ConvolutionCommon.hpp index eae3d0072..7eca95616 100644 --- a/source/core/ConvolutionCommon.hpp +++ b/source/core/ConvolutionCommon.hpp @@ -57,7 +57,8 @@ class MNN_PUBLIC ConvolutionCommon : public Execution { int32_t srcYStep; int32_t packCUnit; int32_t destICStride; - int32_t ic; + int32_t ic; // ic packed by LP, used by im2col dst data. + int32_t icup4; // ic packed by LP, used by im2col src data. }; }; } // namespace MNN diff --git a/source/core/IDSTEncoder.hpp b/source/core/IDSTEncoder.hpp index 0982e55da..b7798d88b 100644 --- a/source/core/IDSTEncoder.hpp +++ b/source/core/IDSTEncoder.hpp @@ -242,7 +242,11 @@ static void WriteCQBlobs(std::ostream &out, const float* weightData, const float { value = fmax(fmin(round((weight - min) / alpha) + min_value, max_value), min_value); } +#ifdef LINEAR_WEIGHT_SET + *tmp = value + offset; +#else *tmp = mapWeight[value]; +#endif tmp++; } } @@ -258,7 +262,11 @@ static void WriteCQBlobs(std::ostream &out, const float* weightData, const float { value = fmax(fmin(round(weight / alpha), max_value), min_value); } +#ifdef LINEAR_WEIGHT_SET + *tmp = value + offset; +#else *tmp = mapWeight[value]; +#endif tmp++; } } diff --git a/source/core/MNNFileUtils.cpp b/source/core/MNNFileUtils.cpp index 99abb7748..fe3eee87b 100644 --- a/source/core/MNNFileUtils.cpp +++ b/source/core/MNNFileUtils.cpp @@ -47,11 +47,10 @@ bool MNNCreateDir(const char * path) { return false; } #else - if (mkdir(path, 0755) == 0 || errno == EEXIST) { + if (mkdir(path, 0755) == 0) { return true; - } else { - return false; } + return MNNDirExist(path); #endif } @@ -285,7 +284,7 @@ void * MNNMmapFile(file_t file, size_t size) #else void * addr = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_SHARED, file, 0); if (addr == MAP_FAILED) { - MNN_ERROR("MNN: Mmap failed: %s\n", strerror(errno)); + MNN_ERROR("MNN: Mmap failed\n"); return nullptr; } return addr; @@ -305,3 +304,17 @@ ErrorCode MNNUnmapFile(void * addr, size_t size) #endif return NO_ERROR; } + +ErrorCode MNNMmapSync(void * addr, size_t size) +{ +#if defined(WIN32) || defined(_WIN32) || defined(_WIN64) || defined(_MSC_VER) + if (!FlushViewOfFile(addr, 0)) { + return FILE_UNMAP_FAILED; + } +#else + if (-1 == msync(addr, size, MS_SYNC)) { + return FILE_UNMAP_FAILED; + } +#endif + return NO_ERROR; +} diff --git a/source/core/MNNFileUtils.h b/source/core/MNNFileUtils.h index cd2267897..3e5beed1f 100644 --- a/source/core/MNNFileUtils.h +++ b/source/core/MNNFileUtils.h @@ -187,4 +187,15 @@ MNN_PUBLIC void * MNNMmapFile(file_t file, size_t size); */ MNN_PUBLIC ErrorCode MNNUnmapFile(void * addr, size_t size); +/*============================================================================================= +** @brief Sync mmap memory to disk. +** @param addr -- start address of the mapped space +** size -- mapped length +** @return If succeeded, returns NO_ERROR +** If failed, returns FAILED +** @warning Make sure that this space was mapped by the MNNMmapFile() before +** and the size is correct +*/ +MNN_PUBLIC ErrorCode MNNMmapSync(void * addr, size_t size); + #endif // MNN_FileUtils_H diff --git a/source/core/Pipeline.cpp b/source/core/Pipeline.cpp index 12807a42f..722f7ca14 100644 --- a/source/core/Pipeline.cpp +++ b/source/core/Pipeline.cpp @@ -273,7 +273,7 @@ ErrorCode Pipeline::encode(bool supportDebug, bool permitCodegen) { mContext.mNeedRelease = mGeometryNeedRelease; FileLoader l(mExternalFile.c_str()); /** Size Compute and compute Const Begin */ - auto res = GeometryComputerUtils::shapeComputeAndGeometryTransform(&l, mInfo.second, mContext, mInfo.first.cache.second, mUseGeometry, false, permitCodegen); + auto res = GeometryComputerUtils::shapeComputeAndGeometryTransform(mCpuRuntime, &l, mInfo.second, mContext, mInfo.first.cache.second, mUseGeometry, false, permitCodegen); if (res != NO_ERROR) { return res; } @@ -1182,9 +1182,12 @@ void Pipeline::_copyInputs() { } ErrorCode Pipeline::execute() { _copyInputs(); + auto enterCode = _enterExecute(); + if (NO_ERROR != enterCode) { + return enterCode; + } auto& mBackend = mInfo.first.cache.first; auto& mBackupBackend = mInfo.first.cache.second; - mBackend->onExecuteBegin(); for (auto& info : mInfo.second) { if (info.type == Schedule::CONSTANT) { continue; @@ -1214,20 +1217,42 @@ ErrorCode Pipeline::execute() { #endif auto code = cmd.execution->onExecute(cmd.workInputs, cmd.workOutputs); if (NO_ERROR != code) { - mBackend->onExecuteEnd(); + _exitExecute(); return code; } } } - mBackend->onExecuteEnd(); + _exitExecute(); return NO_ERROR; } +ErrorCode Pipeline::_enterExecute() { + auto& mBackend = mInfo.first.cache.first; + auto& mBackupBackend = mInfo.first.cache.second; + mBackend->onExecuteBegin(); + mBackupBackend->onExecuteBegin(); + if (mRuntime->pCurrentStatus != NO_ERROR) { + return mRuntime->pCurrentStatus; + } + if (mCpuRuntime->pCurrentStatus != NO_ERROR) { + return mCpuRuntime->pCurrentStatus; + } + return NO_ERROR; +} +void Pipeline::_exitExecute() { + auto& mBackend = mInfo.first.cache.first; + auto& mBackupBackend = mInfo.first.cache.second; + mBackupBackend->onExecuteEnd(); + mBackend->onExecuteEnd(); +} ErrorCode Pipeline::executeCallBack(const TensorCallBackWithInfo& before, const TensorCallBackWithInfo& after) { _copyInputs(); + auto enterCode = _enterExecute(); + if (NO_ERROR != enterCode) { + return enterCode; + } auto& mBackend = mInfo.first.cache.first; auto& mBackupBackend = mInfo.first.cache.second; - mBackend->onExecuteBegin(); for (auto& info : mInfo.second) { if (info.type == Schedule::CONSTANT) { continue; @@ -1239,7 +1264,7 @@ ErrorCode Pipeline::executeCallBack(const TensorCallBackWithInfo& before, const if (nullptr == cmd.info.get()) { auto code = cmd.execution->onExecute(cmd.workInputs, cmd.workOutputs); if (NO_ERROR != code) { - mBackend->onExecuteEnd(); + _exitExecute(); return code; } continue; @@ -1248,18 +1273,18 @@ ErrorCode Pipeline::executeCallBack(const TensorCallBackWithInfo& before, const if (run) { auto code = cmd.execution->onExecute(cmd.workInputs, cmd.workOutputs); if (NO_ERROR != code) { - mBackend->onExecuteEnd(); + _exitExecute(); return code; } } auto stop = !(after(cmd.workOutputs, cmd.info.get())); if (stop) { - mBackend->onExecuteEnd(); + _exitExecute(); return CALL_BACK_STOP; } } } - mBackend->onExecuteEnd(); + _exitExecute(); return NO_ERROR; } diff --git a/source/core/Pipeline.hpp b/source/core/Pipeline.hpp index c3611fe59..ec342db9a 100644 --- a/source/core/Pipeline.hpp +++ b/source/core/Pipeline.hpp @@ -65,6 +65,8 @@ class Pipeline : public NonCopyable { typedef std::map, std::pair, std::shared_ptr>> WrapTensorCache; private: ErrorCode _allocForTensor(int index, bool allocInput); + ErrorCode _enterExecute(); + void _exitExecute(); void _copyInputs(); void _pushTuningTask(std::vector&& initInfos); void _recycleDynamicMemory(Command* command); diff --git a/source/core/Session.cpp b/source/core/Session.cpp index 3c5cb64e1..ac883ea02 100644 --- a/source/core/Session.cpp +++ b/source/core/Session.cpp @@ -99,10 +99,13 @@ void Session::ModeGroup::setHint(Interpreter::HintMode mode, int hint) { break; case Interpreter::OP_ENCODER_NUMBER_FOR_COMMIT: runtimeHint.encorderNumForCommit = hint; + break; case Interpreter::MMAP_FILE_SIZE: runtimeHint.mmapFileSize = hint; + break; case Interpreter::USE_CACHED_MMAP: runtimeHint.useCachedMmap = hint; + break; default: break; } diff --git a/source/geometry/GeometryComputerUtils.cpp b/source/geometry/GeometryComputerUtils.cpp index f2aae2a6d..0a72920b6 100644 --- a/source/geometry/GeometryComputerUtils.cpp +++ b/source/geometry/GeometryComputerUtils.cpp @@ -140,6 +140,7 @@ int GeometryComputerUtils::buildConstantTensors(std::vector& infos, GeometryComputer::Context& geoContext, @@ -321,6 +322,9 @@ ErrorCode GeometryComputerUtils::shapeComputeAndGeometryTransform( info.computeCache.needExecuteConst = dirty; if (dirty) { backupBackend->onExecuteBegin(); + if (cpuRuntime->pCurrentStatus != NO_ERROR) { + return cpuRuntime->pCurrentStatus; + } auto code = cp->execution->onExecute(c.inputs, c.outputs); if (NO_ERROR != code) { return NOT_SUPPORT; diff --git a/source/geometry/GeometryComputerUtils.hpp b/source/geometry/GeometryComputerUtils.hpp index 4da7210ae..c0dffdcdb 100644 --- a/source/geometry/GeometryComputerUtils.hpp +++ b/source/geometry/GeometryComputerUtils.hpp @@ -28,7 +28,8 @@ class GeometryComputerUtils { static Tensor::InsideDescribe::Region makeRawAddressRef(Tensor* src, int srcOffset, int size, int dstOffset = 0); static void makeRawAddressRef(Tensor* dst, Tensor* src, int srcOffset, int size, int dstOffset = 0); MNN_PUBLIC static int buildConstantTensors(std::vector& infos); - MNN_PUBLIC static ErrorCode shapeComputeAndGeometryTransform(FileLoader* external, std::vector& infos, + // TODO: Remove cpuRuntime parameter in future + MNN_PUBLIC static ErrorCode shapeComputeAndGeometryTransform(const Runtime* cpuRuntime, FileLoader* external, std::vector& infos, GeometryComputer::Context& geoContext, std::shared_ptr backupBackend, Runtime::CompilerType compileType, bool skipShapeCompute = false, bool permitCodegen = false); diff --git a/source/geometry/GeometryConv2D.cpp b/source/geometry/GeometryConv2D.cpp index 74c63d022..81317bdeb 100644 --- a/source/geometry/GeometryConv2D.cpp +++ b/source/geometry/GeometryConv2D.cpp @@ -191,15 +191,25 @@ class GeometryIm2Col : public GeometryConv2D { auto sh = common->strideY(); auto dw = common->dilateX(); auto dh = common->dilateY(); - auto pw = common->padX(); - auto ph = common->padY(); + int pl,pt,pr,pb; + if (common->pads() == nullptr) { + pl = common->padX(); + pr = common->padX(); + pt = common->padY(); + pb = common->padY(); + } else { + pl = common->pads()->data()[1]; + pr = common->pads()->data()[3]; + pt = common->pads()->data()[0]; + pb = common->pads()->data()[2]; + } auto batch = input->batch(); auto ic = input->channel(); auto iw = input->width(); auto ih = input->height(); - auto pads = std::make_pair(pw, ph); - auto ow = (iw + pw * 2 - kw) / sw + 1; - auto oh = (ih + ph * 2 - kh) / sh + 1; + auto pads = std::make_pair(pl, pt); + auto ow = (iw + pl + pr - kw) / sw + 1; + auto oh = (ih + pt + pb - kh) / sh + 1; auto tmpT = GeometryConvUtils::im2Col(output, input, ic, kh, kw, batch, oh, ow, ih, iw, sh, sw, dh, dw, pads); if (nullptr != tmpT) { res.extras.emplace_back(tmpT); diff --git a/source/geometry/GeometryConvUtils.cpp b/source/geometry/GeometryConvUtils.cpp index 21670bd24..cbd7b1984 100644 --- a/source/geometry/GeometryConvUtils.cpp +++ b/source/geometry/GeometryConvUtils.cpp @@ -6,6 +6,8 @@ // Copyright © 2018, Alibaba Group Holding Limited // +//#define MNN_OPEN_TIME_TRACE +#include #include "GeometryConvUtils.hpp" #include "ConvertUtils.hpp" @@ -40,6 +42,7 @@ flatbuffers::Offset GeometryConvUtils::makeRelu6(flatbuffers::FlatBufferBuil } void GeometryConvUtils::im2Col3d(Tensor* im2Col, Tensor* input, int ic, int kd, int kh, int kw, int batch, int od, int oh, int ow, int id, int ih, int iw, int sd, int sh, int sw, int dd, int dh, int dw, int pd, int ph, int pw, int srcKernelOffset) { + AUTOTIME; im2Col->buffer().type = halide_type_of(); im2Col->buffer().dimensions = 2; im2Col->setLength(0, ic * kd * kh * kw); @@ -49,6 +52,24 @@ void GeometryConvUtils::im2Col3d(Tensor* im2Col, Tensor* input, int ic, int kd, des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; des->dimensionFormat = MNN_DATA_FORMAT_NCHW; des->regions.clear(); + if (id == kd && ih == kh && iw == kw & pd == 0 && ph == 0 && pw == 0 && dd == 1 && dh == 1 && dw == 1) { + // fast impl: n, ic, id, ih, iw -> ic*id*ih*iw, n + Tensor::InsideDescribe::Region region; + region.origin = input; + region.size[0] = 1; + region.size[1] = ic * id * ih * iw; + region.size[2] = batch; + region.src.offset = 0; + region.dst.offset = 0; + region.src.stride[0] = 1; + region.dst.stride[0] = 1; + region.src.stride[1] = 1; + region.dst.stride[1] = batch; + region.src.stride[2] = ic * id * ih * iw; + region.dst.stride[2] = 1; + des->regions.emplace_back(std::move(region)); + return; + } des->regions.reserve(batch * ic * kd * kh * kw); for (int c = 0; c < ic; ++c) { for (int n = 0; n < batch; ++n) { diff --git a/source/geometry/GeometryELU.cpp b/source/geometry/GeometryELU.cpp index 865d3a8fe..21f7efbe4 100644 --- a/source/geometry/GeometryELU.cpp +++ b/source/geometry/GeometryELU.cpp @@ -41,8 +41,7 @@ class GeometryELU : public GeometryComputer { if (op->type() == OpType_ELU) { alpha = op->main_as_ELU()->alpha(); } else if (op->type() == OpType_Selu){ - alpha = op->main_as_Selu()->alpha() * - op->main_as_Selu()->scale(); + alpha = op->main_as_Selu()->scale() * op->main_as_Selu()->alpha(); } alphaConst->host()[0] = alpha; initTensor(mulValue, input); diff --git a/source/shape/ShapeAttention.cpp b/source/shape/ShapeAttention.cpp index 9b59c1b38..50f26f9e7 100644 --- a/source/shape/ShapeAttention.cpp +++ b/source/shape/ShapeAttention.cpp @@ -71,6 +71,16 @@ class AttentionSizeComputer : public SizeComputer { TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input)->dimensionFormat; return true; } + virtual float onComputeFlops(const MNN::Op* op, const std::vector& inputs, const std::vector& outputs) const override { + auto seqLen = outputs[0]->length(1); + auto headDim = outputs[0]->length(2); + float flops = 0.f; + // qk + qkv + flops += (2 * seqLen * headDim * seqLen); + // softmax + flops += (seqLen * seqLen); + return flops; + } }; diff --git a/source/shape/ShapeConvolution3D.cpp b/source/shape/ShapeConvolution3D.cpp index 9ca39ac30..846a7e880 100644 --- a/source/shape/ShapeConvolution3D.cpp +++ b/source/shape/ShapeConvolution3D.cpp @@ -38,9 +38,13 @@ class Convolution3DSizeComputer : public SizeComputer { if (layer->padMode() == PadMode_SAME) { outputLength = UP_DIV(inputLength, stride); } else { - const int pad = (*layer->pads())[i], kernel = (*layer->kernels())[i], dialate = (*layer->dilates())[i]; + const int padl = layer->pads()->data()[i], kernel = layer->kernels()->data()[i], dialate = layer->dilates()->data()[i]; + int padr = padl; + if (layer->pads()->size() == 6) { + padr = layer->pads()->data()[i+3]; + } const int dialatedKernel = (kernel - 1) * dialate + 1; - outputLength = (inputLength + 2 * pad - dialatedKernel) / stride + 1; + outputLength = (inputLength + padl + padr - dialatedKernel) / stride + 1; } outputBuffer.dim[i + 2].extent = outputLength; } diff --git a/test.sh b/test.sh index 168f06c40..a6da38163 100755 --- a/test.sh +++ b/test.sh @@ -619,6 +619,26 @@ android_unit_test_low_memory_armv8() { echo '### Android 64位Low Memory 权重反量化, precision=2 单元测试失败，测试终止！' failed fi + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op/convolution/weighti8i4conv2d 0 2 4 $1 2 1" + if [ $? -ne 0 ]; then + echo '### Android 64位Low Memory 设置dynamicOption=1, precision=2 4线程测试失败，测试终止！' + failed + fi + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op/convolution/weighti8i4conv2d 0 1 4 $1 2 1" + if [ $? -ne 0 ]; then + echo '### Android 64位Low Memory 设置dynamicOption=1, precision=1 4线程测试失败，测试终止！' + failed + fi + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op/convolution/weighti8i4conv2d 0 2 4 $1 2 2" + if [ $? -ne 0 ]; then + echo '### Android 64位Low Memory 设置dynamicOption=2, precision=2 4线程测试失败，测试终止！' + failed + fi + adb shell "cd /data/local/tmp/MNN&&export LD_LIBRARY_PATH=.&&./run_test.out op/convolution/weighti8i4conv2d 0 1 4 $1 2 2" + if [ $? -ne 0 ]; then + echo '### Android 64位Low Memory 设置dynamicOption=2, precision=1 4线程测试失败，测试终止！' + failed + fi } android_unit_test_low_memory_armv7() { diff --git a/test/MNNTestSuite.h b/test/MNNTestSuite.h index f961ec7cb..fa05d837e 100644 --- a/test/MNNTestSuite.h +++ b/test/MNNTestSuite.h @@ -29,24 +29,6 @@ #include #endif -static inline uint64_t getTimeInUs() { - uint64_t time; -#if defined(_MSC_VER) - LARGE_INTEGER now, freq; - QueryPerformanceCounter(&now); - QueryPerformanceFrequency(&freq); - uint64_t sec = now.QuadPart / freq.QuadPart; - uint64_t usec = (now.QuadPart % freq.QuadPart) * 1000000 / freq.QuadPart; - time = sec * 1000000 + usec; -#else - struct timeval tv; - gettimeofday(&tv, nullptr); - time = static_cast(tv.tv_sec) * 1000000 + tv.tv_usec; -#endif - return time; -} - - /** test case */ class MNNTestCase { friend class MNNTestSuite; diff --git a/test/TestUtils.h b/test/TestUtils.h index ac14eb732..e323da72a 100644 --- a/test/TestUtils.h +++ b/test/TestUtils.h @@ -106,7 +106,7 @@ MNNForwardType getCurrentType(); using ConvertFP32 = float(*)(float fp32Value); -const static ConvertFP32 FP32Converter[MNN::BackendConfig::Precision_Low + 2] = { +const static std::vector FP32Converter = { keepFP32Precision, keepFP32Precision, #ifdef MNN_SUPPORT_BF16 diff --git a/test/expr/MMapTest.cpp b/test/expr/MMapTest.cpp new file mode 100644 index 000000000..18d0be0a1 --- /dev/null +++ b/test/expr/MMapTest.cpp @@ -0,0 +1,58 @@ +// +// MMapTest.cpp +// MNNTests +// +// Created by MNN on 2025/01/14. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include +#include "core/MNNFileUtils.h" +#include "MNNTestSuite.h" +#include "TestUtils.h" +#include "MNN_generated.h" + +using namespace MNN::Express; + +class MMapTest : public MNNTestCase { +public: + virtual bool run(int precision) { + if (MNN_FORWARD_CPU != getCurrentType()) { + MNN_ERROR("Currently don't test not cpu mmap\n"); + return true; + } + auto x = _Input({1, 3, 224, 224}, NC4HW4, halide_type_of()); + x->setName("x"); + auto y = _Conv(1.0f, 0.01f, x, {3, 16}, {5, 5}); + y->setName("y"); + auto buffer = MNN::Express::Variable::save({y}); + ScheduleConfig config; + config.type = getCurrentType(); + std::shared_ptr rtm( Executor::RuntimeManager::createRuntimeManager(config)); + rtm->setExternalPath("tmp", MNN::Interpreter::EXTERNAL_WEIGHT_DIR); + Module::Config mconfig; + mconfig.rearrange = true; + std::shared_ptr m(Module::load({"x"}, {"y"}, (const unsigned char*)buffer.data(), buffer.size(), rtm, &mconfig), Module::destroy); + + // forward_type, precision_type, memory_type, power_type + auto pStatus = MNNTestSuite::get()->pStaus; + // The same as CPUBackend + std::string prefix = "0_0_0_0_"; + prefix[2] += pStatus.precision; + prefix[4] += pStatus.memory; + prefix[6] += pStatus.power; + std::string fileName = MNNFilePathConcat("tmp", prefix + "0.static"); + if (!MNNFileExist(fileName.c_str())) { + FUNC_PRINT(1); + return false; + } + m.reset(); + if (MNNFileExist(fileName.c_str())) { + FUNC_PRINT(1); + return false; + } + return true; + } +}; +MNNTestSuiteRegister(MMapTest, "expr/mmaptest"); diff --git a/test/expr/ModuleTest.cpp b/test/expr/ModuleTest.cpp index 60d25f010..5c2953ed3 100644 --- a/test/expr/ModuleTest.cpp +++ b/test/expr/ModuleTest.cpp @@ -10,6 +10,7 @@ #include #include #include "MNNTestSuite.h" +#include "TestUtils.h" #include "core/Backend.hpp" #include "RuntimeAttr.hpp" #include @@ -1206,7 +1207,41 @@ class SequenceMemoryTest : public MNNTestCase { } return _run(precision, true); } - virtual bool _run(int precision, bool shapeMultable) { + bool _checkResult(std::shared_ptr basic, int precision, bool shapeMultable) { + std::shared_ptr m0(Module::clone(basic.get()), Module::destroy); + std::shared_ptr m1(Module::clone(basic.get()), Module::destroy); + + auto x = _Input({1, 3, 32, 32}, NCHW, halide_type_of()); + auto ptr = x->writeMap(); + for (int i=0; igetInfo()->size; ++i) { + ptr[i] = i * 0.0001f; + } + x->unMap(); + x = x + _Scalar(0.001f); + auto firstResult = m0->onForward({x})[0]->readMap()[0]; + auto y = _Input({1, 3, 33, 33}, NCHW, halide_type_of()); + y->writeMap(); + y->unMap(); + m0->onForward({y}); + auto z = _Input({1, 3, 34, 34}, NCHW, halide_type_of()); + z->writeMap(); + z->unMap(); + m1->onForward({z}); + x = _Input({1, 3, 32, 32}, NCHW, halide_type_of()); + ptr = x->writeMap(); + for (int i=0; igetInfo()->size; ++i) { + ptr[i] = i * 0.0001f; + } + x->unMap(); + x = x + _Scalar(0.001f); + auto secondResult = m0->onForward({x})[0]->readMap()[0]; + if (fabsf(firstResult - secondResult) >= 1e-6) { + return false; + } + return true; + } + + bool _run(int precision, bool shapeMultable) { BackendConfig bnConfig; auto exe = Executor::newExecutor(MNN_FORWARD_CPU, bnConfig, 1); ExecutorScope scope(exe); @@ -1280,6 +1315,8 @@ class SequenceMemoryTest : public MNNTestCase { if (!res) { return false; } + m1.reset(); + _checkResult(m0, precision, shapeMultable); return true; } }; @@ -1384,3 +1421,110 @@ class ExecutorResetLoadModuleTest : public MNNTestCase { } }; MNNTestSuiteRegister(ExecutorResetLoadModuleTest, "expr/ExecutorResetLoadModuleTest"); + +class SequenceForwardResizeTest : public MNNTestCase { +public: + virtual bool run(int precision) { + // Make Model include convolution in shape compute and content compute + auto x = _Input({1, 3, 24, 24}, NCHW, halide_type_of()); + x->setName("x"); + auto y = _Square(x); + auto z = _Erf(y); + z = _Sqrt(z); + z->setName("z"); + auto buffer = Variable::save({z}); + ScheduleConfig config; + config.type = getCurrentType(); + std::shared_ptr rtm0( Executor::RuntimeManager::createRuntimeManager(config)); + std::shared_ptr rtm1( Executor::RuntimeManager::createRuntimeManager(config)); + + Module::Config mconfig; + mconfig.rearrange = false; + std::shared_ptr m0(Module::load({"x"}, {"z"}, (const unsigned char*)buffer.data(), buffer.size(), rtm0, &mconfig), Module::destroy); + std::shared_ptr m1(Module::load({"x"}, {"z"}, (const unsigned char*)buffer.data(), buffer.size(), rtm1, &mconfig), Module::destroy); + x = _Input({1, 3, 24, 24}, NCHW, halide_type_of()); + auto xPtr = x->writeMap(); + ::memset(xPtr, 0, x->getInfo()->size * sizeof(float)); + x->unMap(); + y = m0->onForward({x})[0]; + z = m1->onForward({y})[0]; + int status0 = 0; + int status1 = 0; + rtm0->getInfo(MNN::Interpreter::RESIZE_STATUS, &status0); + rtm1->getInfo(MNN::Interpreter::RESIZE_STATUS, &status1); + if (status0 != 2 || status1 != 2) { + FUNC_PRINT(1); + return false; + } + const_cast(z->getTensor())->wait(MNN::Tensor::MAP_TENSOR_READ, true); + y = m0->onForward({x})[0]; + z = m1->onForward({y})[0]; + rtm0->getInfo(MNN::Interpreter::RESIZE_STATUS, &status0); + rtm1->getInfo(MNN::Interpreter::RESIZE_STATUS, &status1); + if (status0 != 1 || status1 != 1) { + FUNC_PRINT(1); + return false; + } + y = nullptr; + z = nullptr; + y = m0->onForward({x})[0]; + z = m1->onForward({y})[0]; + rtm0->getInfo(MNN::Interpreter::RESIZE_STATUS, &status0); + rtm1->getInfo(MNN::Interpreter::RESIZE_STATUS, &status1); + if (status0 != 0 || status1 != 0) { + FUNC_PRINT(1); + return false; + } + x = _Input({1, 3, 12, 12}, NCHW, halide_type_of()); + y = m0->onForward({x})[0]; + rtm0->getInfo(MNN::Interpreter::RESIZE_STATUS, &status0); + if (2 != status0) { + FUNC_PRINT(1); + return false; + } + BackendConfig originConfig; + auto exe = Executor::newExecutor(MNN_FORWARD_CPU, originConfig, 1); + { + ExecutorScope _s(exe); + std::shared_ptr m2(Module::clone(m0.get())); + auto rtm2 = m2->getInfo()->runTimeManager; + if (rtm2 == rtm0) { + FUNC_PRINT(1); + return false; + } + int status2 = 0; + rtm2->getInfo(MNN::Interpreter::RESIZE_STATUS, &status2); + if (0 != status2) { + FUNC_PRINT(1); + return false; + } + auto x2 = _Input({1, 3, 24, 24}, NCHW, halide_type_of()); + auto xPtr = x2->writeMap(); + ::memset(xPtr, 0, x2->getInfo()->size * sizeof(float)); + x2->unMap(); + auto y2 = m2->onForward({x})[0]; + + rtm2->getInfo(MNN::Interpreter::RESIZE_STATUS, &status2); + if (2 != status2) { + FUNC_PRINT(1); + return false; + } + } + x = nullptr; + y = nullptr; + z = nullptr; + x = _Input({1, 3, 12, 12}, NCHW, halide_type_of()); + x->writeMap(); + m0->onForward({x}); + m1->onForward({x}); + x = _Input({1, 3, 36, 36}, NCHW, halide_type_of()); + x->writeMap(); + m0->onForward({x}); + x = _Input({1, 3, 12, 12}, NCHW, halide_type_of()); + x->writeMap(); + m1->onForward({x}); + + return true; + } +}; +MNNTestSuiteRegister(SequenceForwardResizeTest, "expr/SequenceForwardResizeTest"); diff --git a/test/model/MobileNetTest.cpp b/test/model/MobileNetTest.cpp index e5bd5028b..158c89ae2 100644 --- a/test/model/MobileNetTest.cpp +++ b/test/model/MobileNetTest.cpp @@ -11,6 +11,7 @@ #endif #include +#include #include #include "MNNTestSuite.h" #include "TestUtils.h" @@ -109,13 +110,12 @@ class MobileNetTest : public MNNTestCase { std::shared_ptr hostTensor(MNN::Tensor::createHostTensorFromDevice(outputTensor, false)); for(int i=0; i<20; i++)//warmm up { - auto timeBegin = getTimeInUs(); + MNN::Timer _t; inputTensor->copyFromHostTensor(input.get()); net->runSession(session); outputTensor->copyToHostTensor(hostTensor.get()); - auto timeEnd = getTimeInUs(); - printf("run cost %f ms\n", ((timeEnd - timeBegin) / 1000.0)); + printf("run cost %f ms\n", ((_t.durationInUs()) / 1000.0)); } float tolerance = backend == MNN_FORWARD_CPU ? 0.04 : 0.1; diff --git a/test/op/ConvertTest.cpp b/test/op/ConvertTest.cpp index af03f8d61..01c1b55a3 100644 --- a/test/op/ConvertTest.cpp +++ b/test/op/ConvertTest.cpp @@ -29,6 +29,7 @@ class ConvertTest : public MNNTestCase { { auto gotOutput = output->readMap(); if (!checkVector(gotOutput, expectedOutput.data(), 16, 0.01)) { + FUNC_PRINT(1); MNN_ERROR("ConvertTest test failed!\n"); for (int i = 0; i < 16; ++i) { MNN_PRINT("Correct: %f - Compute: %f\n", expectedOutput[i], gotOutput[i]); @@ -40,6 +41,7 @@ class ConvertTest : public MNNTestCase { { auto gotOutput = output->readMap(); if (!checkVector(gotOutput, inpudata, 12, 0.01)) { + FUNC_PRINT(1); MNN_ERROR("ConvertTest test failed!\n"); for (int i = 0; i < 12; ++i) { MNN_PRINT("Correct: %f - Compute: %f\n", inpudata[i], gotOutput[i]); diff --git a/test/op/ConvolutionTest.cpp b/test/op/ConvolutionTest.cpp index 134dd19fe..2e6d640cb 100644 --- a/test/op/ConvolutionTest.cpp +++ b/test/op/ConvolutionTest.cpp @@ -574,14 +574,17 @@ class ConvolutionInt8CommonTest : public ConvolutionCommonTest { auto minValue = minMax.first; wScale[2*k] = minMax.first; auto absMax = minMax.second - minMax.first; - wScale[2*k+1] = absMax / (threshold - clampMin); - float scale = 0.0f; + wScale[2*k+1] = 1; + + float quantscale = 1.0f; if (absMax >= 0.000001f) { - scale = 1.0f / wScale[2*k+1]; + wScale[2 * k + 1] = absMax / (threshold - clampMin); + quantscale = 1.0f / wScale[2*k+1]; + } float* ptr = weightData.data() + beginIndex; for (int i = 0; i < kernel_size; ++i) { - int8_t quantValue = int8_t(std::round((ptr[i] - minValue) * scale + clampMin)); + int8_t quantValue = int8_t(std::round((ptr[i] - minValue) * quantscale + clampMin)); float floatValue = ((float)quantValue - clampMin) * wScale[2*k+1] + minValue; quantWeight[k * kernel_size + i] = quantValue; ptr[i] = floatValue; @@ -602,8 +605,7 @@ class ConvolutionInt8CommonTest : public ConvolutionCommonTest { } } } - reference_conv2d(inputData, weightData, biasData, outputData, outputDataSeparateBias, batch, ic, oc, ih, iw, mode, pad_h, pad_w, kh, kw, - stride, dilation, group, FP32Converter[precision]); + reference_conv2d(inputData, weightData, biasData, outputData, outputDataSeparateBias, batch, ic, oc, ih, iw, mode, pad_h, pad_w, kh, kw, stride, dilation, group, FP32Converter[precision]); if (outputData.size() == 0) { return true; } @@ -616,7 +618,8 @@ class ConvolutionInt8CommonTest : public ConvolutionCommonTest { if (nbit == 4 && weightLength > 10000) { errorScale = 50.0f; } - if (precision > MNN::BackendConfig::Precision_High) { + int memory = MNNTestSuite::get()->pStaus.memory; + if (precision > MNN::BackendConfig::Precision_High || memory > MNN::BackendConfig::Memory_High) { errorScale = 100.0f; } std::vector> activations = { @@ -646,15 +649,18 @@ class ConvolutionInt8CommonTest : public ConvolutionCommonTest { } // difference below 0.5% relative error is considered correct. + output = _Convert(output, NCHW); auto outputPtr = output->readMap(); // when using low precision, im2col or strassen convolution error rate to reference value is about 1e-4, winograd has larger error rate. if (!checkVectorByRelativeError(outputPtr, toutputData.data(), toutputData.data(), toutputData.size(), 0.001 * errorScale)) { - MNN_PRINT("precision:%d, expect:\t expect2:\t real:\t\n", precision); + MNN_PRINT("precision:%d, memory:%d\n", precision, memory); + MNN_PRINT("expect:\t real:\t\n"); for (int i = 0; i < toutputData.size(); ++i) { - MNN_PRINT("%f\t, %f\n", toutputData[i], outputPtr[i]); + MNN_PRINT("%f, %f\n", toutputData[i], outputPtr[i]); } + MNN_PRINT("output shape: n=%d c=%d h=%d w=%d\n", output->getInfo()->dim[0], output->getInfo()->dim[1], output->getInfo()->dim[2], output->getInfo()->dim[3]); MNN_ERROR("%s(%s) test failed for %d bits, async=%d , relu: %d, relu6: %d!\n", test_op_name.c_str(), device_name.c_str(), nbit, async, activation.first, activation.second); return false; } @@ -725,10 +731,10 @@ class ConvolutionTest : public ConvolutionType { int icStep = 1; int isStep = 3; std::vector ocSize = { - 1, 3, 10, 17 + 1, 4, 3, 10, 17 }; std::vector icSize = { - 4, 1, 3, 8, 11 + 1, 4, 3, 8, 11 }; std::vector isSize = { 1, 7, 9 @@ -747,9 +753,7 @@ class ConvolutionTest : public ConvolutionType { for (int s = 1; s <= 2; s++) { for (auto block : blocks) { for (int p = 0; p <= 1; p++) { - bool succ = - ConvolutionType().test(type, device_name, "Conv2D", b, ic, oc, is, - is, PadMode_CAFFE, p, p, kh, kw, s, d, 1, precision, sparseAlgo, block, false); + bool succ = ConvolutionType().test(type, device_name, "Conv2D", b, ic, oc, is, is, PadMode_CAFFE, p, p, kh, kw, s, d, 1, precision, sparseAlgo, block, false); if (!succ) { MNN_ERROR( "Error for conv b=%d, oc=%d, ic=%d, ih=%d, " diff --git a/test/op/TopKV2Test.cpp b/test/op/TopKV2Test.cpp index 054d1baef..b1fbb19de 100644 --- a/test/op/TopKV2Test.cpp +++ b/test/op/TopKV2Test.cpp @@ -6,6 +6,7 @@ // Copyright © 2018, Alibaba Group Holding Limited // +#include #include #include #include "MNNTestSuite.h" @@ -178,16 +179,15 @@ class TopKV2Test : public MNNTestCase { VARP input1 = _Input({1}, NCHW, halide_type_of()); RandomInitFloat(input0->writeMap(), numRow * lengthRow); SetK(input1->writeMap(), K); + MNN::Timer _t; - auto timeStart = getTimeInUs(); // calculate gotOutput auto res = _TopKV2(input0, input1); VARP output0 = res[0]; VARP output1 = res[1]; auto gotOutput0 = output0->readMap(); auto gotOutput1 = output1->readMap(); - auto timeEnd = getTimeInUs(); - auto timeCost = timeEnd - timeStart; + auto timeCost = _t.durationInUs(); // calculate expectedOutput std::vector expectedOutput0(numRow * K); diff --git a/test/speed/HybridConvSpeedTest.cpp b/test/speed/HybridConvSpeedTest.cpp index c17ecfebe..46c9da9f9 100644 --- a/test/speed/HybridConvSpeedTest.cpp +++ b/test/speed/HybridConvSpeedTest.cpp @@ -34,13 +34,14 @@ class HybridConvSpeedTestCommon : public MNNTestCase { float threshold = (float)(1 << (nbit - 1)) - 1.0f; float clampMin = -threshold; - VARP x = _Input({batch, ic, ih, iw}, NC4HW4, halide_type_of()); + VARP x = _Input({batch, ic, ih, iw}, NCHW, halide_type_of()); auto xInfo = x->getInfo(); auto xPtr = x->writeMap(); int8_t xMin = -(1<<(nbit-1))+1, xMax = (1<<(nbit-1))-1; for (int i=0; isize; ++i) { - xPtr[i] = (i % (xMax - xMin + 1)) * 0.27; + xPtr[i] = (i % (xMax - xMin + 1) - (xMax / 2)) * 0.27; } + x = _Convert(x, NC4HW4); for (int i = 0; i < oc; ++i) { bias[i] = i % 10 + 0.005; for (int j = 0; j < ic; ++j) { @@ -162,10 +163,11 @@ class HybridConvInt8Test : public HybridConvSpeedTestCommon { std::vector kernels = {1, 1}; bool lowmemory = true; { - std::vector< std::vector> channels = {{7, 9}, {2048, 54}, {1, 10}, {20, 153}, {9, 18}}; + std::vector< std::vector> channels = {{7, 9}, {9, 9}, {2048, 54}, {1, 10}, {20, 153}, {9, 18}}; for (int i = 0; i < channels.size(); ++i) { for (int n = 0; n < batch.size(); ++n) { auto res = testKernel("Low memory HybridConv test:", inputShape, kernels, channels[i], pad, strides, dilate, batch[n], 8, precision); + res &= testKernel("Low memory HybridConv test:", inputShape, kernels, channels[i], pad, strides, dilate, batch[n], 4, precision); if (!res) { MNN_ERROR("Error: low memory hybridConv when bits=8, n=%d, ic=%d, oc=%d\n", batch[n], channels[i][0], channels[i][1]); return false; @@ -174,7 +176,7 @@ class HybridConvInt8Test : public HybridConvSpeedTestCommon { } } { - std::vector< std::vector> channels = {{2048, 54}, {8, 8}, {8, 9}, {8, 16}}; + std::vector< std::vector> channels = {{12, 16}, {2048, 54}, {8, 8}, {8, 9}, {8, 16}}; for (int i = 0; i < channels.size(); ++i) { for (int n = 0; n < batch.size(); ++n) { auto res = testKernel("Low memory HybridConv test:", inputShape, kernels, channels[i], pad, strides, dilate, batch[n], 4, precision); @@ -210,7 +212,7 @@ class DenseConvInt8Test : public HybridConvSpeedTestCommon { for (auto block : blocks) { auto res = testKernel("Low memory ConvInt8 with kernel test:", inputShape, kernel, channels[i], pad, strides, dilate, batch[n], bits, precision, false, block); if (!res) { - MNN_ERROR("Error: low memory ConvInt8 with %dx%d kernel when n=%d, ic=%d, oc=%d, block=%d\n", kernel[0], kernel[1], batch[n], channels[i][0], channels[i][1], block); + MNN_ERROR("Error: low memory ConvInt8 with %dx%d kernel when bits=%d n=%d, ic=%d, oc=%d, block=%d\n", kernel[0], kernel[1], bits, batch[n], channels[i][0], channels[i][1], block); return false; } } diff --git a/tools/converter/forward.json b/tools/converter/forward.json index 3fb140782..304e9774c 100644 --- a/tools/converter/forward.json +++ b/tools/converter/forward.json @@ -3,5 +3,6 @@ "mode":1, "precision":1, "memory":1, - "power":1 + "power":1, + "hints":[5, 1] } diff --git a/tools/converter/include/commonKit.hpp b/tools/converter/include/commonKit.hpp deleted file mode 100644 index e261f2add..000000000 --- a/tools/converter/include/commonKit.hpp +++ /dev/null @@ -1,18 +0,0 @@ -// -//// commonKit.hpp -//// -//// Created by MNN on 2023/11/03. -//// Copyright © 2018, Alibaba Group Holding Limited -//// -// -#ifndef COMMONKit_HPP -#define COMMONKit_HPP - -#include -#include "MNN_compression.pb.h" -class CommonKit { -public: - static bool FileIsExist(std::string path); - static bool json2protobuf(const char* jsonFile, const char* protoFile=nullptr, MNN::Compression::Pipeline* pipeline=nullptr); -}; -#endif diff --git a/tools/converter/include/config.hpp b/tools/converter/include/config.hpp index 57956a9b0..756704c2a 100644 --- a/tools/converter/include/config.hpp +++ b/tools/converter/include/config.hpp @@ -11,6 +11,7 @@ #include #include #include +struct PostTreatContext; class MNN_PUBLIC modelConfig { public: modelConfig() @@ -21,6 +22,7 @@ class MNN_PUBLIC modelConfig { model(modelConfig::MAX_SOURCE), saveHalfFloat(false){ } + ~ modelConfig (); enum MODEL_SOURCE { TENSORFLOW = 0, CAFFE, ONNX, MNN, TFLITE, TORCH, JSON, MAX_SOURCE }; // MNN model path @@ -50,7 +52,7 @@ class MNN_PUBLIC modelConfig { int optimizeLevel = 1; bool keepInputFormat = true; bool alignDenormalizedValue = true; - bool detectSparseSpeedUp = true; + bool detectSparseSpeedUp = false; bool convertMatmulToConv = true; bool useGeluApproximation = true; bool transformerFuse = false; @@ -68,6 +70,7 @@ class MNN_PUBLIC modelConfig { int64_t externalTreshold = 1024 * 64; std::ofstream* externalFile = nullptr; int64_t externalOffset = 0; + PostTreatContext* compressInfo = nullptr; }; #endif // CONFIG_HPP diff --git a/tools/converter/include/options.hpp b/tools/converter/include/options.hpp deleted file mode 100644 index 4a2bd4755..000000000 --- a/tools/converter/include/options.hpp +++ /dev/null @@ -1,30 +0,0 @@ -// -// Options.hpp -// MNN -// -// Created by MNN on 2020/07/28. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#ifndef MNN_CONVERTER_COMMON_OPTIONS_HPP_ -#define MNN_CONVERTER_COMMON_OPTIONS_HPP_ - -#include - -#include "../source/compression/PipelineBuilder.hpp" - -namespace common { - -// TODO(): Refine -typedef struct Options { - bool doCompress; - compression::Pipeline compressionPipeline; -} Options; - -Options DefaultOptions(); - -Options BuildOptions(const std::string& compressionFile); - -} // namespace common - -#endif // MNN_CONVERTER_COMMON_OPTIONS_HPP_ diff --git a/tools/converter/include/writeFb.hpp b/tools/converter/include/writeFb.hpp index 008e6d927..46d1fd565 100644 --- a/tools/converter/include/writeFb.hpp +++ b/tools/converter/include/writeFb.hpp @@ -20,6 +20,6 @@ *@param benchmarkModel benchmarkModel is true, then delete the weight of Convolution etc. *@param saveHalfFloat when saveHalfFloat is true, save weight in half float data type */ -MNN_PUBLIC int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, const modelConfig& config); +MNN_PUBLIC int writeFb(std::unique_ptr& netT, const modelConfig& config); #endif // WRITEFB_HPP diff --git a/tools/converter/source/common/Common.cpp b/tools/converter/source/common/Common.cpp deleted file mode 100644 index 41c7b4373..000000000 --- a/tools/converter/source/common/Common.cpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// Common.cpp -// MNNConverter -// -// Created by MNN on 2020/07/02. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#include "Common.hpp" - -namespace MNN { - -MNN_DATA_FORMAT convertFormat(Express::Dimensionformat format) { - switch (format) { - case Express::NCHW: - return MNN_DATA_FORMAT_NCHW; - case Express::NHWC: - return MNN_DATA_FORMAT_NHWC; - case Express::NC4HW4: - return MNN_DATA_FORMAT_NC4HW4; - default: - return MNN_DATA_FORMAT_UNKNOWN; - } -} - -DataType convertDataType(halide_type_t type) { - if (type.code == halide_type_float) { - return DataType_DT_FLOAT; - } - if (type.code == halide_type_uint && type.bits == 8) { - return DataType_DT_UINT8; - } - if (type.code == halide_type_int && type.bits == 8) { - return DataType_DT_INT8; - } - if (type.code == halide_type_int && type.bits == 32) { - return DataType_DT_INT32; - } - return DataType_DT_INVALID; -} - -} // namespace MNN diff --git a/tools/converter/source/common/Common.hpp b/tools/converter/source/common/Common.hpp deleted file mode 100644 index aca580370..000000000 --- a/tools/converter/source/common/Common.hpp +++ /dev/null @@ -1,25 +0,0 @@ -// -// Common.hpp -// MNNConverter -// -// Created by MNN on 2020/07/02. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#ifndef MNN_CONVERTER_COMMON_COMMON_HPP_ -#define MNN_CONVERTER_COMMON_COMMON_HPP_ - -#include "MNN/HalideRuntime.h" -#include "MNN/expr/Expr.hpp" - -#include "MNN_generated.h" - -namespace MNN { - -DataType convertDataType(halide_type_t type); - -MNN_DATA_FORMAT convertFormat(Express::Dimensionformat format); - -} // namespace MNN - -#endif // MNN_CONVERTER_COMMON_COMMON_HPP_ diff --git a/tools/converter/source/common/CommonUtils.cpp b/tools/converter/source/common/CommonUtils.cpp new file mode 100644 index 000000000..4121b99a6 --- /dev/null +++ b/tools/converter/source/common/CommonUtils.cpp @@ -0,0 +1,78 @@ +#include +#include +#include +#include "CommonUtils.hpp" +#include "core/MNNFileUtils.h" +using namespace std; +void CommonKit::loadCompress(modelConfig& modelPath) { + bool overwrite = false; + bool readProto = false; + modelPath.compressInfo = new PostTreatContext; + do { + auto compressFileName = modelPath.compressionParamsFile; + if (compressFileName != "") { + if (!MNNFileExist(compressFileName.c_str())) { + MNN_PRINT("CompressFileName not exist, create it\n"); + overwrite = true; + break; + } + string jsonSuffix = "json"; + string suffix = compressFileName.substr(compressFileName.find_last_of('.') + 1); + if (jsonSuffix.compare(suffix) != 0) { // protobuf.bin file + std::fstream input(compressFileName.c_str(), std::ios::in | std::ios::binary); + if (!modelPath.compressInfo->proto.ParseFromIstream(&input)) { + MNN_ERROR("Failed to parse compression pipeline proto.\n"); + } else { + readProto = true; + } + } else { + readProto = CommonKit::json2protobuf(compressFileName.c_str(), nullptr, &modelPath.compressInfo->proto); + } + } + } while (false); + modelPath.compressInfo->read = readProto; + modelPath.compressInfo->write = overwrite; +} +bool CommonKit::FileIsExist(std::string path) { + return MNNFileExist(path.c_str()); +} +bool CommonKit::protobuf2json(const char* jsonFile, const MNN::Compression::Pipeline* pipeline) { + google::protobuf::util::JsonOptions options; + options.add_whitespace = true; + std::string output; + auto status = google::protobuf::util::MessageToJsonString(*pipeline, &output, options); + if (status.code() != google::protobuf::util::status_internal::StatusCode::kOk) { + FUNC_PRINT_ALL(status.message().ToString().c_str(), s); + return false; + } + { + std::ofstream out(jsonFile); + if (out.fail()) { + MNN_ERROR("Dump json error: Can't open %s\n", jsonFile); + return false; + } + out << output; + } + return true; +} + +bool CommonKit::json2protobuf(const char* jsonFile, const char* protoFile, MNN::Compression::Pipeline* pipeline) { + { + std::ifstream fileNames(jsonFile); + if (fileNames.fail()) { + return false; + } + std::ostringstream output; + output << fileNames.rdbuf(); + auto outputStr = output.str(); + google::protobuf::util::JsonParseOptions options; + options.ignore_unknown_fields = true; + auto status = google::protobuf::util::JsonStringToMessage(outputStr, pipeline, options); + if (status.code() != google::protobuf::util::status_internal::StatusCode::kOk) { + FUNC_PRINT_ALL(status.message().ToString().c_str(), s); + return false; + } + } + return true; +} + diff --git a/tools/converter/source/common/CommonUtils.hpp b/tools/converter/source/common/CommonUtils.hpp index f774fe72c..2b8137f49 100644 --- a/tools/converter/source/common/CommonUtils.hpp +++ b/tools/converter/source/common/CommonUtils.hpp @@ -16,6 +16,15 @@ #include #include "../source/core/FileLoader.hpp" +struct PostTreatContext { + MNN::Compression::Pipeline proto; + std::map, const MNN::Compression::LayerQuantizeParams*> quantInfo; + MNN::Compression::QuantizeParams* quantMutableInfo = nullptr; + std::string subgraph; + bool read = false; + bool write = false; +}; + void converToStaticModel(const MNN::Net* net, std::map>& inputConfig, std::string mnnFile); void RemoveAndStoreParam(std::unique_ptr& op, std::ofstream* fs, int64_t& offset); void loadExternalParam(std::unique_ptr& op, MNN::FileLoader* fl); @@ -23,9 +32,17 @@ void CastParamsToHalf(std::unique_ptr& op); void AlignDenormalizedValue(std::unique_ptr& op); void AddSparseInfo(std::unique_ptr& op, MNN::Compression::Pipeline proto); void fullQuantAndCoding(std::unique_ptr& netT, MNN::Compression::Pipeline proto); -void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& config); +void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& config, const PostTreatContext* context); void addUUID(std::unique_ptr& netT, MNN::Compression::Pipeline proto); void channelPruneConvert(std::unique_ptr& netT, MNN::Compression::Pipeline proto); +class CommonKit { +public: + static void loadCompress(modelConfig& modelPath); + static bool FileIsExist(std::string path); + static bool json2protobuf(const char* jsonFile, const char* protoFile=nullptr, MNN::Compression::Pipeline* pipeline=nullptr); + static bool protobuf2json(const char* jsonFile, const MNN::Compression::Pipeline* pipeline); +}; + #endif // COMMMON_UTILS_HPP diff --git a/tools/converter/source/common/FullQuantAndCoding.cpp b/tools/converter/source/common/FullQuantAndCoding.cpp index 5db4b2ec4..23e74a65b 100644 --- a/tools/converter/source/common/FullQuantAndCoding.cpp +++ b/tools/converter/source/common/FullQuantAndCoding.cpp @@ -29,6 +29,9 @@ void FullQuantAndCoding(std::unique_ptr& netT, std::unique_ptr findMinMax(const float *weights, const int count) { return {min, max}; } -void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& config) { +void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& config, const PostTreatContext* context) { const auto opType = op->type; // config.weightQuantBits only control weight quantization for float convolution // by default, do coding for convint8 and depthwiseconvint8, if there is any @@ -49,13 +49,51 @@ void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& conf opType != MNN::OpType_ConvInt8 && opType != MNN::OpType_DepthwiseConvInt8) { return; } - auto param = op->main.AsConvolution2D(); + auto param = op->main.AsConvolution2D(); auto& common = param->common; if (param->quanParameter.get() != nullptr) { return; } + auto weightQuantBits = config.weightQuantBits; + bool asymmetricQuantFlag = config.weightQuantAsymmetric; + auto weightQuantBlock = config.weightQuantBlock; + // Read or write config in proto + if (context->quantInfo.find(std::make_pair(context->subgraph, op->name)) != context->quantInfo.end()) { + auto param = context->quantInfo.find(std::make_pair(context->subgraph, op->name))->second; + if (param->weight_size() > 0) { + auto weight = param->weight(0); + if (weight.has_asymmetric()) { + asymmetricQuantFlag = weight.asymmetric(); + } + if (weight.has_bits()) { + weightQuantBits = weight.bits(); + } + if (weight.has_block_size()) { + weightQuantBlock = weight.block_size(); + } + } + } + if (nullptr != context->quantMutableInfo) { + auto& proto = context->proto; + auto layer = context->quantMutableInfo->add_layer(); + layer->set_op_name(op->name); + if (!context->subgraph.empty()) { + layer->set_subgraph_name(context->subgraph); + } + auto conv = layer->mutable_conv(); + conv->set_input_channel(common->inputCount); + conv->set_output_channel(common->outputCount); + conv->clear_kernel_size(); + conv->add_kernel_size(common->kernelX); + conv->add_kernel_size(common->kernelY); + auto weight = layer->add_weight(); + weight->set_bits(weightQuantBits); + weight->set_asymmetric(asymmetricQuantFlag); + weight->set_block_size(weightQuantBlock); + weight->set_name(op->name); + } - if (config.weightQuantBits == 0) { + if (weightQuantBits == 0) { if (opType == MNN::OpType_ConvInt8 || opType == MNN::OpType_DepthwiseConvInt8) { // Do nothing } else { @@ -64,9 +102,9 @@ void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& conf } } int bits = 8; - if ((config.weightQuantBits > 0) && ( + if ((weightQuantBits > 0) && ( opType != MNN::OpType_ConvInt8 && opType != MNN::OpType_DepthwiseConvInt8)) { - bits = config.weightQuantBits; + bits = weightQuantBits; } // Bits must from 2-8 bits = std::max(bits, 2); @@ -85,8 +123,6 @@ void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& conf int kxky = common->kernelX * common->kernelY; int icCount = kernelSize / kxky; - bool asymmetricQuantFlag = config.weightQuantAsymmetric; - float threshold = (float)(1 << (bits - 1)) - 1.0f; float clampMin = -threshold; if (asymmetricQuantFlag) { @@ -95,8 +131,8 @@ void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& conf std::vector weightData, scales; // block-wise quant int block_size = kernelSize, block_num = 1; - if (config.weightQuantBlock > 0 && (kernelSize % config.weightQuantBlock == 0) && kxky == 1) { - block_size = config.weightQuantBlock; + if (weightQuantBlock > 0 && (kernelSize % weightQuantBlock == 0) && kxky == 1) { + block_size = weightQuantBlock; block_num = kernelSize / block_size; } @@ -161,14 +197,3 @@ void WeightQuantAndCoding(std::unique_ptr& op, const modelConfig& conf param->weight.swap(empty); } }; - -void weightQuantAndCoding(std::unique_ptr& netT, const modelConfig& config) { - for (auto& op : netT->oplists) { - WeightQuantAndCoding(op, config); - } - for (auto& subgraph : netT->subgraphs) { - for (auto& op : subgraph->nodes) { - WeightQuantAndCoding(op, config); - } - } -} diff --git a/tools/converter/source/common/cli.cpp b/tools/converter/source/common/cli.cpp index 947a41ee2..a11d563c2 100644 --- a/tools/converter/source/common/cli.cpp +++ b/tools/converter/source/common/cli.cpp @@ -7,7 +7,6 @@ // #include "cli.hpp" -#include "commonKit.hpp" #if defined(_MSC_VER) #include #undef min @@ -15,6 +14,7 @@ #else #include #endif +#include #include "OpCount.hpp" #include "cxxopts.hpp" #include "config.hpp" @@ -32,13 +32,18 @@ #include #include #include +#include "CommonUtils.hpp" #include "PostConverter.hpp" #include "rapidjson/document.h" #include #include #include #include "core/MemoryFormater.h" - +modelConfig::~modelConfig() { + if (nullptr != compressInfo) { + delete compressInfo; + } +} namespace MNN { using namespace MNN::Express; static std::string _getDataType(const halide_type_t& type) { @@ -214,7 +219,8 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv "compressionParamsFile", "The path of the compression parameters that stores activation, " "weight scales and zero points for quantization or information " - "for sparsity.", + "for sparsity. " + "if the file does not exist, will create file base on user's option", cxxopts::value() ) ( @@ -278,8 +284,7 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv ) ( "detectSparseSpeedUp", - "if 1 converter would detect weights sparsity and check sparse speedup. default: 1, range : {0, 1}", - cxxopts::value() + "if add the flag converter would detect weights sparsity and check sparse speedup, may decrease model size, but will cause more time for convert." ) ( "saveExternalData", @@ -478,7 +483,7 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv modelPath.alignDenormalizedValue = result["alignDenormalizedValue"].as(); } if (result.count("detectSparseSpeedUp")) { - modelPath.detectSparseSpeedUp = result["detectSparseSpeedUp"].as(); + modelPath.detectSparseSpeedUp = true; } if (result.count("convertMatmulToConv")) { modelPath.convertMatmulToConv = result["convertMatmulToConv"].as(); @@ -647,7 +652,7 @@ static void computeUnaryBuffer(MNN::NetT* net) { val = -127; } unaryParam[i] = val; - } + } } } } @@ -723,6 +728,7 @@ bool Cli::convertModel(modelConfig& modelPath) { "FuseDupOp" }; } + CommonKit::loadCompress(modelPath); if (needOptimize) { std::cout << "Start to Optimize the MNN Net..." << std::endl; std::unique_ptr newNet = optimizeNet(netT, modelPath.forTraining, modelPath, expectedPass); @@ -731,9 +737,9 @@ bool Cli::convertModel(modelConfig& modelPath) { computeUnaryBuffer(newNet.get()); } - error = writeFb(newNet, modelPath.MNNModel, modelPath); + error = writeFb(newNet, modelPath); } else { - error = writeFb(netT, modelPath.MNNModel, modelPath); + error = writeFb(netT, modelPath); } if (0 == error) { std::cout << "Converted Success!" << std::endl; @@ -878,6 +884,7 @@ int Cli::testconvert(const std::string& defaultCacheFile, const std::string& dir BackendConfig backendConfig; backendConfig.precision = static_cast(1); config.backendConfig = &backendConfig; + std::vector hints; if (!backendConfigJson.empty()) { do { @@ -909,12 +916,24 @@ int Cli::testconvert(const std::string& defaultCacheFile, const std::string& dir if (configDoc.HasMember("power")) { config.backendConfig->power = (MNN::BackendConfig::PowerMode)configDoc["power"].GetInt(); } + if (configDoc.HasMember("hints")) { + auto array = configDoc["hints"].GetArray(); + for (auto iter = array.Begin(); iter != array.End(); iter++) { + hints.emplace_back(iter->GetInt()); + } + if (hints.size() % 2 != 0) { + MNN_ERROR("Invalid hint number: %d\n", hints.size()); + } + } } while (false); } MNN::Express::Module::Config mConfig; mConfig.shapeMutable = true; std::shared_ptr rtmgr(MNN::Express::Executor::RuntimeManager::createRuntimeManager(config)); + for (int v=0; vsetHint((Interpreter::HintMode)hints[2*v], hints[2*v+1]); + } rtmgr->setExternalFile("./convert_cache.mnn.weight"); std::shared_ptr net(MNN::Express::Module::load(inputNames, outputNames, defaultCacheFile.c_str(), rtmgr, &mConfig)); std::shared_ptr net2; @@ -1374,133 +1393,3 @@ bool Cli::json2mnn(const char* jsonFile, const char* modelFile) { } }; - - -bool CommonKit::FileIsExist(std::string path) { -#if defined(_MSC_VER) - if (INVALID_FILE_ATTRIBUTES != GetFileAttributes(path.c_str()) && GetLastError() != ERROR_FILE_NOT_FOUND) { - return true; - } -#else - if ((access(path.c_str(), F_OK)) != -1) { - return true; - } -#endif - return false; -} - -bool CommonKit::json2protobuf(const char* jsonFile, const char* protoFile, MNN::Compression::Pipeline* pipeline) { - rapidjson::Document document; - { - std::ifstream fileNames(jsonFile); - std::ostringstream output; - output << fileNames.rdbuf(); - auto outputStr = output.str(); - document.Parse(outputStr.c_str()); - if (document.HasParseError()) { - MNN_ERROR("Invalid json\n"); - return 0; - } - } - if (!document.HasMember("pipeline")) { - MNN_ERROR("Error||Invalid json file: missing pipeline member.\n"); - return 0; - } - auto pipelineInfo = document["pipeline"].GetObject(); - std::string version = pipelineInfo["version"].GetString(); - pipeline->set_version(version); - - auto algos = pipelineInfo["algo"].GetArray(); - for (auto iter = algos.begin(); iter != algos.end(); ++iter) { - auto algoInfo = iter->GetObject(); - MNN_ASSERT(algoInfo["type"].GetInt() == 0); - auto compressionType = (MNN::Compression::CompressionAlgo_CompressionType)algoInfo["type"].GetInt(); - auto quantParamsInfo = algoInfo["quant_params"].GetObject(); - auto round_mode = quantParamsInfo["round_mode"].GetInt(); - MNN::Compression::CompressionAlgo* algo = pipeline->add_algo(); - algo->set_type(compressionType); - auto quant_params = algo->mutable_quant_params(); - quant_params->set_round_mode((MNN::Compression::QuantizeParams_RoundMode)round_mode); - - auto layers = quantParamsInfo["layer"].GetArray(); - for (auto ly = layers.begin(); ly != layers.end(); ++ly) { - auto layerInfo = ly->GetObject(); - auto newLayer = quant_params->add_layer(); - if (layerInfo.HasMember("method")) { - newLayer->set_method((MNN::Compression::LayerQuantizeParams_QuantMethod)layerInfo["method"].GetInt()); - } - - // Weight. - auto weights_ = layerInfo["weight"].GetArray(); - for (auto w = weights_.begin(); w != weights_.end(); ++w) { - // Get weight info. - int bits = w->GetObject()["bits"].GetInt(); - auto name = w->GetObject()["name"].GetString(); - auto scale = w->GetObject()["scales"].GetArray(); - auto zeropoint = w->GetObject()["zero_point"].GetInt(); - auto clamp_min = w->GetObject()["clamp_min"].GetInt(); - auto clamp_max = w->GetObject()["clamp_max"].GetInt(); - // Write to newLayer - auto weight = newLayer->add_weight(); - weight->set_bits(bits); - weight->set_name(name); - weight->set_clamp_max(clamp_max); - weight->set_clamp_min(clamp_min); - for (int k = 0; k < scale.Size(); ++k) { - weight->add_scales(scale[k].GetFloat()); - } - } - - // Input. - auto inputs_ = layerInfo["input"].GetArray(); - for (auto w = inputs_.begin(); w != inputs_.end(); ++w) { - int bits = w->GetObject()["bits"].GetInt(); - auto name = w->GetObject()["name"].GetString(); - auto scale = w->GetObject()["scales"].GetArray(); - auto zeropoint = w->GetObject()["zero_point"].GetInt(); - auto clamp_min = w->GetObject()["clamp_min"].GetInt(); - auto clamp_max = w->GetObject()["clamp_max"].GetInt(); - // Write to newLayer - auto input = newLayer->add_input(); - input->set_bits(bits); - input->set_name(name); - input->set_clamp_max(clamp_max); - input->set_clamp_min(clamp_min); - for (int k = 0; k < scale.Size(); ++k) { - input->add_scales(scale[k].GetFloat()); - } - } - - // Output. - auto outputs_ = layerInfo["output"].GetArray(); - for (auto w = outputs_.begin(); w != outputs_.end(); ++w) { - int bits = w->GetObject()["bits"].GetInt(); - auto name = w->GetObject()["name"].GetString(); - auto scale = w->GetObject()["scales"].GetArray(); - auto zeropoint = w->GetObject()["zero_point"].GetInt(); - auto clamp_min = w->GetObject()["clamp_min"].GetInt(); - auto clamp_max = w->GetObject()["clamp_max"].GetInt(); - // Write to newLayer - auto output = newLayer->add_output(); - output->set_bits(bits); - output->set_name(name); - output->set_clamp_max(clamp_max); - output->set_clamp_min(clamp_min); - for (int k = 0; k < scale.Size(); ++k) { - output->add_scales(scale[k].GetFloat()); - } - } - } - } - // Write protobuf.bin - if (protoFile) { - std::ofstream output(protoFile, std::ios::out | std::ios::binary); - if (!pipeline->SerializeToOstream(&output)) { - MNN_ERROR("->Error: Fail saving Json file to protobuf file\n"); - return 0; - } - MNN_PRINT("Finish convert json file to protobuf binary file\n"); - } - return 1; -} - diff --git a/tools/converter/source/common/convertToStaticModel.cpp b/tools/converter/source/common/convertToStaticModel.cpp index 2d8c120db..98dd2d058 100644 --- a/tools/converter/source/common/convertToStaticModel.cpp +++ b/tools/converter/source/common/convertToStaticModel.cpp @@ -336,7 +336,7 @@ void converToStaticModel(const Net* net, std::map>& // resize the session's info and store to buffer std::vector constTensors; GeometryComputerUtils::buildConstantTensors(infos); - GeometryComputerUtils::shapeComputeAndGeometryTransform(nullptr, infos, ctx, defaultBackend, runtime->onGetCompilerType()); + GeometryComputerUtils::shapeComputeAndGeometryTransform(runtime.get(), nullptr, infos, ctx, defaultBackend, runtime->onGetCompilerType()); std::map> tensorName; for (int i = 0; i < net->tensorName()->size(); i++) { tensorName[allTensors[i].get()] = std::make_pair(net->tensorName()->GetAsString(i)->str(), i); diff --git a/tools/converter/source/common/options.cpp b/tools/converter/source/common/options.cpp deleted file mode 100644 index 5b3f2e4b2..000000000 --- a/tools/converter/source/common/options.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// -// options.cpp -// MNNConverter -// -// Created by MNN on 2020/07/28. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#include "options.hpp" - -namespace common { - -Options DefaultOptions() { - Options opt; - opt.doCompress = false; - opt.compressionPipeline = compression::Pipeline(); - return opt; -} - -Options BuildOptions(const std::string& compressionFile) { - Options options; - if (!compressionFile.empty()) { - options.doCompress = true; - } else { - options.doCompress = false; - } - // TODO(): Check if file exist. - compression::PipelineBuilder builder(compressionFile); - options.compressionPipeline = builder.Build(); - return std::move(options); -} - -} // namespace common diff --git a/tools/converter/source/common/writeFb.cpp b/tools/converter/source/common/writeFb.cpp index 13c157238..560fcb84f 100644 --- a/tools/converter/source/common/writeFb.cpp +++ b/tools/converter/source/common/writeFb.cpp @@ -21,13 +21,12 @@ #include "cpp/ConfigFile.hpp" #include #include "cli.hpp" -#include "commonKit.hpp" #include "MNN_compression.pb.h" using namespace MNN; using namespace std; -static void _postTreatOp(std::unique_ptr& op, FileLoader* fl, MNN::Compression::Pipeline proto, const modelConfig& config, std::ofstream& weightPath, int64_t& offset, bool needExternalWeight) { +static void _postTreatOp(std::unique_ptr& op, FileLoader* fl, const PostTreatContext& context, const modelConfig& config, std::ofstream& weightPath, int64_t& offset, bool needExternalWeight) { loadExternalParam(op, fl); if (config.alignDenormalizedValue) { AlignDenormalizedValue(op); @@ -36,9 +35,9 @@ static void _postTreatOp(std::unique_ptr& op, FileLoader* fl, MNN::Compress CastParamsToHalf(op); } if (config.detectSparseSpeedUp) { - AddSparseInfo(op, proto); + AddSparseInfo(op, context.proto); } - WeightQuantAndCoding(op, config); + WeightQuantAndCoding(op, config, &context); if (needExternalWeight) { RemoveAndStoreParam(op, &weightPath, offset); } @@ -87,23 +86,12 @@ static bool _largeModel(const MNN::NetT* netT) { } return false; } -int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, const modelConfig& config) { +int postTreat(std::unique_ptr& netT, const modelConfig& config) { std::string compressFileName = config.compressionParamsFile; - Compression::Pipeline proto; - if (compressFileName != "") { - string jsonSuffix = "json"; - string suffix = compressFileName.substr(compressFileName.find_last_of('.') + 1); - if (jsonSuffix.compare(suffix) != 0) { // protobuf.bin file - std::fstream input(compressFileName.c_str(), std::ios::in | std::ios::binary); - if (!proto.ParseFromIstream(&input)) { - MNN_ERROR("Failed to parse compression pipeline proto.\n"); - } - } else { - CommonKit::json2protobuf(compressFileName.c_str(), nullptr, &proto); - } - } + auto& proto = config.compressInfo->proto; + auto MNNModelFile = config.MNNModel; - addUUID(netT, proto); + addUUID(netT, config.compressInfo->proto); // add version info to model netT->extraInfo.reset(new ExtraInfoT); @@ -112,11 +100,14 @@ int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, c // add auth code to model netT->extraInfo->name = config.authCode; } - - if (config.compressionParamsFile != "") { + bool useOriginQuant = config.compressInfo->proto.algo_size() > 0 && (!config.compressInfo->write); + if (config.compressInfo->proto.has_for_guide() && config.compressInfo->proto.for_guide()) { + useOriginQuant = false; + } + if (useOriginQuant) { channelPruneConvert(netT, proto); } - if (config.compressionParamsFile != "") { + if (useOriginQuant) { fullQuantAndCoding(netT, proto); } // Check If need external weight @@ -126,7 +117,7 @@ int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, c } std::ofstream externalWeightOs; if (needExternalWeight) { - auto weightName = MNNModelFile + ".weight"; + auto weightName = config.MNNModel + ".weight"; MNN_PRINT("Save Weight to %s\n", weightName.c_str()); externalWeightOs.open(weightName.c_str()); if (externalWeightOs.fail()) { @@ -134,20 +125,62 @@ int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, c } } { + bool findQuant = false; + auto& context = *config.compressInfo; + for (int i=0; iset_type( MNN::Compression::CompressionAlgo_CompressionType_QUANTIZE); + context.quantMutableInfo = algo->mutable_quant_params(); + } int64_t offset = 0; FileLoader fl(".__convert_external_data.bin"); for (auto& op : netT->oplists) { - _postTreatOp(op, &fl, proto, config, externalWeightOs, offset, needExternalWeight); + _postTreatOp(op, &fl, context, config, externalWeightOs, offset, needExternalWeight); } for (auto& subgraph : netT->subgraphs) { + context.subgraph = subgraph->name; for (auto& op : subgraph->nodes) { - _postTreatOp(op, &fl, proto, config, externalWeightOs, offset, needExternalWeight); + _postTreatOp(op, &fl, context, config, externalWeightOs, offset, needExternalWeight); } } } { MNNRemoveFile(".__convert_external_data.bin"); } + if (config.compressInfo->write) { + CommonKit::protobuf2json(compressFileName.c_str(), &proto); + } + return 0; +} +int writeFb(std::unique_ptr& netT, const modelConfig& config) { + postTreat(netT, config); std::set notSupportOps; auto CheckIfNotSupported = [&] (const std::unique_ptr& op) { if (op->type == MNN::OpType_Extra) { @@ -227,9 +260,9 @@ int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, c } } const Net* net = flatbuffers::GetRoot(bufferOutput); - converToStaticModel(net, inputConfig, MNNModelFile); + converToStaticModel(net, inputConfig, config.MNNModel); } else { - std::ofstream output(MNNModelFile, std::ofstream::binary); + std::ofstream output(config.MNNModel, std::ofstream::binary); output.write((const char*)bufferOutput, sizeOutput); } if (!netT->subgraphs.empty()) { diff --git a/tools/converter/source/compression/MNN_compression.proto b/tools/converter/source/compression/MNN_compression.proto index 62685d0d4..5098c668a 100644 --- a/tools/converter/source/compression/MNN_compression.proto +++ b/tools/converter/source/compression/MNN_compression.proto @@ -42,6 +42,8 @@ message LayerQuantizeParams { repeated float scales = 3; optional int32 clamp_min = 4 [default = -128]; optional int32 clamp_max = 5 [default = 127]; + optional bool asymmetric = 6 [default = false]; + optional int32 block_size = 7 [default = 0]; } message WinogradParams { @@ -56,11 +58,20 @@ message LayerQuantizeParams { WinogradAware = 2; } + message ConvolutionInfo { + required int32 input_channel = 1; + required int32 output_channel = 2; + repeated int32 kernel_size = 3; + } + repeated ActivationParams input = 1; repeated WeightParams weight = 2; repeated ActivationParams output = 3; optional QuantMethod method = 4 [default = QAT]; optional WinogradParams wino_params = 5; + optional string op_name = 6; + optional string subgraph_name = 7; + optional ConvolutionInfo conv = 8; } message LevelPrunerParams { @@ -104,4 +115,5 @@ message Pipeline { repeated CompressionAlgo algo = 2; optional string mnn_uuid = 3; + optional bool for_guide = 4 [default = false]; } diff --git a/tools/converter/source/compression/PipelineBuilder.cpp b/tools/converter/source/compression/PipelineBuilder.cpp deleted file mode 100644 index 2589c4394..000000000 --- a/tools/converter/source/compression/PipelineBuilder.cpp +++ /dev/null @@ -1,132 +0,0 @@ -// -// PipelineBuilder.cpp -// MNNConverter -// -// Created by MNN on 2020/07/28. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#include - -#include "PipelineBuilder.hpp" -#include "MNN/MNNDefine.h" -#include -#include "rapidjson/document.h" -#include "cli.hpp" -#include "commonKit.hpp" - -namespace compression { - -PipelineBuilder::PipelineBuilder(const std::string& filename) - : filename_(filename) {} - -Pipeline PipelineBuilder::Build() const { - Pipeline pipeline; - if (!filename_.empty()) { - MNN::Compression::Pipeline proto; - std::string jsonSuffix = "json"; - std::string suffix = filename_.substr(filename_.find_last_of('.') + 1); - if (jsonSuffix.compare(suffix) != 0) { // protobuf.bin - std::fstream input(filename_.c_str(), std::ios::in | std::ios::binary); - if (!proto.ParseFromIstream(&input)) { - MNN_ERROR("Failed to parse compression pipeline proto.\n"); - } else { - ParsePipeline(proto, &pipeline); - } - } else { - CommonKit::json2protobuf(filename_.c_str(), nullptr, &proto); - ParsePipeline(proto, &pipeline); - } - } - return std::move(pipeline); -} - -bool PipelineBuilder::ParsePipeline(const MNN::Compression::Pipeline& proto, - Pipeline* pipeline) const { - for (const auto& algo : proto.algo()) { - Progress progress; - progress.type = algo.type(); - switch (progress.type) { - case CompressionAlgo::QUANTIZE: { - ParseQuantization(algo.quant_params(), - &(progress.quant_params)); - break; - } - case CompressionAlgo::PRUNE: - default: { - MNN_ERROR("Unsupported compression type: %d.\n", progress.type); - } - } - pipeline->progress_.push_back(std::move(progress)); - } - return true; -} - -Quantization::TensorParams PipelineBuilder::ParseActivationQuantization( - const LayerQuantizeParams::ActivationParams& proto) const { - Quantization::TensorParams tensor_params; - tensor_params.nbit = proto.bits(); - int size = proto.scales().size(); - tensor_params.scale.resize(size); - for (int i = 0; i < size; ++i) { - tensor_params.scale[i] = proto.scales(i); - } - tensor_params.zero_point = proto.zero_point(); - tensor_params.clamp_min = proto.clamp_min(); - tensor_params.clamp_max = proto.clamp_max(); - return std::move(tensor_params); -} - -Quantization::TensorParams PipelineBuilder::ParseWeightQuantization( - const LayerQuantizeParams::WeightParams& proto) const { - Quantization::TensorParams tensor_params; - tensor_params.nbit = proto.bits(); - int size = proto.scales().size(); - tensor_params.scale.resize(size); - for (int i = 0; i < size; ++i) { - tensor_params.scale[i] = proto.scales(i); - } - tensor_params.zero_point = 0.f; - return std::move(tensor_params); -} - -bool PipelineBuilder::ParseQuantization( - const MNN::Compression::QuantizeParams& proto, - Quantization* quant_params) const { - quant_params->round_mode = proto.round_mode(); - for (const auto& layer_proto : proto.layer()) { - auto method = layer_proto.method(); - for (const auto& input : layer_proto.input()) { - const std::string& tensor_name = input.name(); - Quantization::TensorParams tensor_params = - ParseActivationQuantization(input); - tensor_params.method = method; - quant_params->tensors[tensor_name].push_back(tensor_params); - } - int length = 0; - for (const auto& weight : layer_proto.weight()) { - const std::string& tensor_name = weight.name(); - Quantization::TensorParams tensor_params = - ParseWeightQuantization(weight); - // TODO(): FIXME - // quant_params->tensors[tensor_name].push_back(tensor_params); - length = tensor_params.scale.size(); - } - for (const auto& output : layer_proto.output()) { - const std::string& tensor_name = output.name(); - Quantization::TensorParams tensor_params = - ParseActivationQuantization(output); - if (tensor_params.scale.size() != length) { - MNN_ERROR("Output(%s) scale and weight scale length are " - "mismatch, (%d vs %d).\n", tensor_name.c_str(), - int(tensor_params.scale.size()), length); - MNN_ASSERT(false); - } - tensor_params.method = method; - quant_params->tensors[tensor_name].push_back(tensor_params); - } - } - return true; -} - -}; diff --git a/tools/converter/source/compression/PipelineBuilder.hpp b/tools/converter/source/compression/PipelineBuilder.hpp deleted file mode 100644 index f4c0bafd0..000000000 --- a/tools/converter/source/compression/PipelineBuilder.hpp +++ /dev/null @@ -1,72 +0,0 @@ -// -// PipelineBuilder.hpp -// MNNConverter -// -// Created by MNN on 2020/07/28. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#ifndef MNN_CONVERTER_COMPRESSION_PIPELINE_HPP_ -#define MNN_CONVERTER_COMPRESSION_PIPELINE_HPP_ - -#include "quantization.hpp" -#include "MNN_compression.pb.h" - -typedef MNN::Compression::CompressionAlgo CompressionAlgo; -typedef MNN::Compression::LayerQuantizeParams LayerQuantizeParams; - -namespace compression { - -class PipelineBuilder; - -typedef struct Progress { - CompressionAlgo::CompressionType type; - - // TODO(): Support prune. - Quantization quant_params; -} Progress; - -class Pipeline { -public: - Pipeline() = default; - virtual ~Pipeline() = default; - - const std::vector& progress() const { - return progress_; - } - -private: - friend class PipelineBuilder; - std::vector progress_; -}; - -class PipelineBuilder { -public: - PipelineBuilder() = delete; - - explicit PipelineBuilder(const std::string& filename); - - virtual ~PipelineBuilder() = default; - - Pipeline Build() const; - -private: - bool ParsePipeline(const MNN::Compression::Pipeline& proto, - Pipeline* pipeline) const; - - bool ParseQuantization(const MNN::Compression::QuantizeParams& proto, - Quantization* quant_params) const; - - Quantization::TensorParams ParseWeightQuantization( - const LayerQuantizeParams::WeightParams& proto) const; - - Quantization::TensorParams ParseActivationQuantization( - const LayerQuantizeParams::ActivationParams& proto) const; - -private: - std::string filename_ = ""; -}; - -}; - -#endif // MNN_CONVERTER_COMPRESSION_PIPELINE_HPP_ diff --git a/tools/converter/source/compression/generated/MNN_compression.pb.cc b/tools/converter/source/compression/generated/MNN_compression.pb.cc index f2cecec9f..63f8138bb 100644 --- a/tools/converter/source/compression/generated/MNN_compression.pb.cc +++ b/tools/converter/source/compression/generated/MNN_compression.pb.cc @@ -53,6 +53,8 @@ constexpr LayerQuantizeParams_WeightParams::LayerQuantizeParams_WeightParams( ::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized) : scales_() , name_(&::PROTOBUF_NAMESPACE_ID::internal::fixed_address_empty_string) + , asymmetric_(false) + , block_size_(0) , clamp_max_(127) , bits_(8) , clamp_min_(-128){} @@ -78,12 +80,29 @@ struct LayerQuantizeParams_WinogradParamsDefaultTypeInternal { }; }; PROTOBUF_ATTRIBUTE_NO_DESTROY PROTOBUF_CONSTINIT LayerQuantizeParams_WinogradParamsDefaultTypeInternal _LayerQuantizeParams_WinogradParams_default_instance_; +constexpr LayerQuantizeParams_ConvolutionInfo::LayerQuantizeParams_ConvolutionInfo( + ::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized) + : kernel_size_() + , input_channel_(0) + , output_channel_(0){} +struct LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal { + constexpr LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal() + : _instance(::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized{}) {} + ~LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal() {} + union { + LayerQuantizeParams_ConvolutionInfo _instance; + }; +}; +PROTOBUF_ATTRIBUTE_NO_DESTROY PROTOBUF_CONSTINIT LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal _LayerQuantizeParams_ConvolutionInfo_default_instance_; constexpr LayerQuantizeParams::LayerQuantizeParams( ::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized) : input_() , weight_() , output_() + , op_name_(&::PROTOBUF_NAMESPACE_ID::internal::fixed_address_empty_string) + , subgraph_name_(&::PROTOBUF_NAMESPACE_ID::internal::fixed_address_empty_string) , wino_params_(nullptr) + , conv_(nullptr) , method_(0) {} struct LayerQuantizeParamsDefaultTypeInternal { @@ -158,7 +177,8 @@ constexpr Pipeline::Pipeline( ::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized) : algo_() , version_(nullptr) - , mnn_uuid_(&::PROTOBUF_NAMESPACE_ID::internal::fixed_address_empty_string){} + , mnn_uuid_(&::PROTOBUF_NAMESPACE_ID::internal::fixed_address_empty_string) + , for_guide_(false){} struct PipelineDefaultTypeInternal { constexpr PipelineDefaultTypeInternal() : _instance(::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized{}) {} @@ -170,7 +190,7 @@ struct PipelineDefaultTypeInternal { PROTOBUF_ATTRIBUTE_NO_DESTROY PROTOBUF_CONSTINIT PipelineDefaultTypeInternal _Pipeline_default_instance_; } // namespace Compression } // namespace MNN -static ::PROTOBUF_NAMESPACE_ID::Metadata file_level_metadata_MNN_5fcompression_2eproto[10]; +static ::PROTOBUF_NAMESPACE_ID::Metadata file_level_metadata_MNN_5fcompression_2eproto[11]; static const ::PROTOBUF_NAMESPACE_ID::EnumDescriptor* file_level_enum_descriptors_MNN_5fcompression_2eproto[4]; static constexpr ::PROTOBUF_NAMESPACE_ID::ServiceDescriptor const** file_level_service_descriptors_MNN_5fcompression_2eproto = nullptr; @@ -214,11 +234,15 @@ const uint32_t TableStruct_MNN_5fcompression_2eproto::offsets[] PROTOBUF_SECTION PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WeightParams, scales_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WeightParams, clamp_min_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WeightParams, clamp_max_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WeightParams, asymmetric_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WeightParams, block_size_), 0, - 2, + 4, ~0u, + 5, 3, 1, + 2, PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WinogradParams, _has_bits_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WinogradParams, _internal_metadata_), ~0u, // no _extensions_ @@ -229,6 +253,18 @@ const uint32_t TableStruct_MNN_5fcompression_2eproto::offsets[] PROTOBUF_SECTION PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_WinogradParams, units_attr_), 0, ~0u, + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo, _has_bits_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo, _internal_metadata_), + ~0u, // no _extensions_ + ~0u, // no _oneof_case_ + ~0u, // no _weak_field_map_ + ~0u, // no _inlined_string_donated_ + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo, input_channel_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo, output_channel_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo, kernel_size_), + 0, + 1, + ~0u, PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, _has_bits_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, _internal_metadata_), ~0u, // no _extensions_ @@ -240,11 +276,17 @@ const uint32_t TableStruct_MNN_5fcompression_2eproto::offsets[] PROTOBUF_SECTION PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, output_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, method_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, wino_params_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, op_name_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, subgraph_name_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::LayerQuantizeParams, conv_), ~0u, ~0u, ~0u, - 1, + 4, + 2, 0, + 1, + 3, ~0u, // no _has_bits_ PROTOBUF_FIELD_OFFSET(::MNN::Compression::LevelPrunerParams, _internal_metadata_), ~0u, // no _extensions_ @@ -297,21 +339,24 @@ const uint32_t TableStruct_MNN_5fcompression_2eproto::offsets[] PROTOBUF_SECTION PROTOBUF_FIELD_OFFSET(::MNN::Compression::Pipeline, version_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::Pipeline, algo_), PROTOBUF_FIELD_OFFSET(::MNN::Compression::Pipeline, mnn_uuid_), + PROTOBUF_FIELD_OFFSET(::MNN::Compression::Pipeline, for_guide_), 0, ~0u, 1, + 2, }; static const ::PROTOBUF_NAMESPACE_ID::internal::MigrationSchema schemas[] PROTOBUF_SECTION_VARIABLE(protodesc_cold) = { { 0, 8, -1, sizeof(::MNN::Compression::QuantizeParams)}, { 10, 22, -1, sizeof(::MNN::Compression::LayerQuantizeParams_ActivationParams)}, - { 28, 39, -1, sizeof(::MNN::Compression::LayerQuantizeParams_WeightParams)}, - { 44, 52, -1, sizeof(::MNN::Compression::LayerQuantizeParams_WinogradParams)}, - { 54, 65, -1, sizeof(::MNN::Compression::LayerQuantizeParams)}, - { 70, -1, -1, sizeof(::MNN::Compression::LevelPrunerParams)}, - { 79, -1, -1, sizeof(::MNN::Compression::SIMDOCPrunerParams)}, - { 89, 98, -1, sizeof(::MNN::Compression::PruneParams)}, - { 101, 110, -1, sizeof(::MNN::Compression::CompressionAlgo)}, - { 113, 122, -1, sizeof(::MNN::Compression::Pipeline)}, + { 28, 41, -1, sizeof(::MNN::Compression::LayerQuantizeParams_WeightParams)}, + { 48, 56, -1, sizeof(::MNN::Compression::LayerQuantizeParams_WinogradParams)}, + { 58, 67, -1, sizeof(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo)}, + { 70, 84, -1, sizeof(::MNN::Compression::LayerQuantizeParams)}, + { 92, -1, -1, sizeof(::MNN::Compression::LevelPrunerParams)}, + { 101, -1, -1, sizeof(::MNN::Compression::SIMDOCPrunerParams)}, + { 111, 120, -1, sizeof(::MNN::Compression::PruneParams)}, + { 123, 132, -1, sizeof(::MNN::Compression::CompressionAlgo)}, + { 135, 145, -1, sizeof(::MNN::Compression::Pipeline)}, }; static ::PROTOBUF_NAMESPACE_ID::Message const * const file_default_instances[] = { @@ -319,6 +364,7 @@ static ::PROTOBUF_NAMESPACE_ID::Message const * const file_default_instances[] = reinterpret_cast(&::MNN::Compression::_LayerQuantizeParams_ActivationParams_default_instance_), reinterpret_cast(&::MNN::Compression::_LayerQuantizeParams_WeightParams_default_instance_), reinterpret_cast(&::MNN::Compression::_LayerQuantizeParams_WinogradParams_default_instance_), + reinterpret_cast(&::MNN::Compression::_LayerQuantizeParams_ConvolutionInfo_default_instance_), reinterpret_cast(&::MNN::Compression::_LayerQuantizeParams_default_instance_), reinterpret_cast(&::MNN::Compression::_LevelPrunerParams_default_instance_), reinterpret_cast(&::MNN::Compression::_SIMDOCPrunerParams_default_instance_), @@ -335,7 +381,7 @@ const char descriptor_table_protodef_MNN_5fcompression_2eproto[] PROTOBUF_SECTIO "$.MNN.Compression.LayerQuantizeParams\"U\n" "\tRoundMode\022\026\n\022ROUND_TOWARDS_ZERO\020\000\022\030\n\024RO" "UND_AWAY_FROM_ZERO\020\001\022\026\n\022ROUND_HALF_TO_EV" - "EN\020\002\"\352\005\n\023LayerQuantizeParams\022D\n\005input\030\001 " + "EN\020\002\"\340\007\n\023LayerQuantizeParams\022D\n\005input\030\001 " "\003(\01325.MNN.Compression.LayerQuantizeParam" "s.ActivationParams\022A\n\006weight\030\002 \003(\01321.MNN" ".Compression.LayerQuantizeParams.WeightP" @@ -344,42 +390,48 @@ const char descriptor_table_protodef_MNN_5fcompression_2eproto[] PROTOBUF_SECTIO "method\030\004 \001(\01620.MNN.Compression.LayerQuan" "tizeParams.QuantMethod:\003QAT\022H\n\013wino_para" "ms\030\005 \001(\01323.MNN.Compression.LayerQuantize" - "Params.WinogradParams\032\211\001\n\020ActivationPara" - "ms\022\014\n\004name\030\001 \002(\t\022\017\n\004bits\030\002 \001(\005:\0018\022\016\n\006sca" - "les\030\003 \003(\002\022\025\n\nzero_point\030\004 \001(\005:\0010\022\027\n\tclam" - "p_min\030\005 \001(\005:\004-128\022\026\n\tclamp_max\030\006 \001(\005:\00312" - "7\032n\n\014WeightParams\022\014\n\004name\030\001 \002(\t\022\017\n\004bits\030" - "\002 \001(\005:\0018\022\016\n\006scales\030\003 \003(\002\022\027\n\tclamp_min\030\004 " - "\001(\005:\004-128\022\026\n\tclamp_max\030\005 \001(\005:\003127\0328\n\016Win" - "ogradParams\022\022\n\007version\030\001 \002(\005:\0010\022\022\n\nunits" - "_attr\030\004 \003(\005\"<\n\013QuantMethod\022\007\n\003QAT\020\000\022\021\n\rO" - "verflowAware\020\001\022\021\n\rWinogradAware\020\002\"[\n\021Lev" - "elPrunerParams\022\033\n\023weight_tensor_names\030\001 " - "\003(\t\022\024\n\014prune_ratios\030\002 \003(\002\022\023\n\013layer_names" - "\030\003 \003(\t\"o\n\022SIMDOCPrunerParams\022\033\n\023weight_t" - "ensor_names\030\001 \003(\t\022\024\n\014prune_ratios\030\002 \003(\002\022" - "\023\n\013layer_names\030\003 \003(\t\022\021\n\toc_blocks\030\004 \003(\005\"" - "\202\002\n\013PruneParams\022<\n\004type\030\001 \001(\0162&.MNN.Comp" - "ression.PruneParams.PruneType:\006RANDOM\022\?\n" - "\023level_pruner_params\030\002 \001(\0132\".MNN.Compres" - "sion.LevelPrunerParams\022B\n\025simd_oc_pruner" - "_params\030\003 \001(\0132#.MNN.Compression.SIMDOCPr" - "unerParams\"0\n\tPruneType\022\n\n\006RANDOM\020\000\022\013\n\007S" - "IMD_OC\020\001\022\n\n\006FILTER\020\002\"\362\001\n\017CompressionAlgo" - "\022H\n\004type\030\001 \001(\01620.MNN.Compression.Compres" - "sionAlgo.CompressionType:\010QUANTIZE\0225\n\014qu" - "ant_params\030\002 \001(\0132\037.MNN.Compression.Quant" - "izeParams\0222\n\014prune_params\030\003 \001(\0132\034.MNN.Co" - "mpression.PruneParams\"*\n\017CompressionType" - "\022\014\n\010QUANTIZE\020\000\022\t\n\005PRUNE\020\001\"d\n\010Pipeline\022\026\n" - "\007version\030\001 \002(\t:\0050.0.0\022.\n\004algo\030\002 \003(\0132 .MN" - "N.Compression.CompressionAlgo\022\020\n\010mnn_uui" - "d\030\003 \001(\t" + "Params.WinogradParams\022\017\n\007op_name\030\006 \001(\t\022\025" + "\n\rsubgraph_name\030\007 \001(\t\022B\n\004conv\030\010 \001(\01324.MN" + "N.Compression.LayerQuantizeParams.Convol" + "utionInfo\032\211\001\n\020ActivationParams\022\014\n\004name\030\001" + " \002(\t\022\017\n\004bits\030\002 \001(\005:\0018\022\016\n\006scales\030\003 \003(\002\022\025\n" + "\nzero_point\030\004 \001(\005:\0010\022\027\n\tclamp_min\030\005 \001(\005:" + "\004-128\022\026\n\tclamp_max\030\006 \001(\005:\003127\032\240\001\n\014Weight" + "Params\022\014\n\004name\030\001 \002(\t\022\017\n\004bits\030\002 \001(\005:\0018\022\016\n" + "\006scales\030\003 \003(\002\022\027\n\tclamp_min\030\004 \001(\005:\004-128\022\026" + "\n\tclamp_max\030\005 \001(\005:\003127\022\031\n\nasymmetric\030\006 \001" + "(\010:\005false\022\025\n\nblock_size\030\007 \001(\005:\0010\0328\n\016Wino" + "gradParams\022\022\n\007version\030\001 \002(\005:\0010\022\022\n\nunits_" + "attr\030\004 \003(\005\032U\n\017ConvolutionInfo\022\025\n\rinput_c" + "hannel\030\001 \002(\005\022\026\n\016output_channel\030\002 \002(\005\022\023\n\013" + "kernel_size\030\003 \003(\005\"<\n\013QuantMethod\022\007\n\003QAT\020" + "\000\022\021\n\rOverflowAware\020\001\022\021\n\rWinogradAware\020\002\"" + "[\n\021LevelPrunerParams\022\033\n\023weight_tensor_na" + "mes\030\001 \003(\t\022\024\n\014prune_ratios\030\002 \003(\002\022\023\n\013layer" + "_names\030\003 \003(\t\"o\n\022SIMDOCPrunerParams\022\033\n\023we" + "ight_tensor_names\030\001 \003(\t\022\024\n\014prune_ratios\030" + "\002 \003(\002\022\023\n\013layer_names\030\003 \003(\t\022\021\n\toc_blocks\030" + "\004 \003(\005\"\202\002\n\013PruneParams\022<\n\004type\030\001 \001(\0162&.MN" + "N.Compression.PruneParams.PruneType:\006RAN" + "DOM\022\?\n\023level_pruner_params\030\002 \001(\0132\".MNN.C" + "ompression.LevelPrunerParams\022B\n\025simd_oc_" + "pruner_params\030\003 \001(\0132#.MNN.Compression.SI" + "MDOCPrunerParams\"0\n\tPruneType\022\n\n\006RANDOM\020" + "\000\022\013\n\007SIMD_OC\020\001\022\n\n\006FILTER\020\002\"\362\001\n\017Compressi" + "onAlgo\022H\n\004type\030\001 \001(\01620.MNN.Compression.C" + "ompressionAlgo.CompressionType:\010QUANTIZE" + "\0225\n\014quant_params\030\002 \001(\0132\037.MNN.Compression" + ".QuantizeParams\0222\n\014prune_params\030\003 \001(\0132\034." + "MNN.Compression.PruneParams\"*\n\017Compressi" + "onType\022\014\n\010QUANTIZE\020\000\022\t\n\005PRUNE\020\001\"~\n\010Pipel" + "ine\022\026\n\007version\030\001 \002(\t:\0050.0.0\022.\n\004algo\030\002 \003(" + "\0132 .MNN.Compression.CompressionAlgo\022\020\n\010m" + "nn_uuid\030\003 \001(\t\022\030\n\tfor_guide\030\004 \001(\010:\005false" ; static ::PROTOBUF_NAMESPACE_ID::internal::once_flag descriptor_table_MNN_5fcompression_2eproto_once; const ::PROTOBUF_NAMESPACE_ID::internal::DescriptorTable descriptor_table_MNN_5fcompression_2eproto = { - false, false, 1847, descriptor_table_protodef_MNN_5fcompression_2eproto, "MNN_compression.proto", - &descriptor_table_MNN_5fcompression_2eproto_once, nullptr, 0, 10, + false, false, 2119, descriptor_table_protodef_MNN_5fcompression_2eproto, "MNN_compression.proto", + &descriptor_table_MNN_5fcompression_2eproto_once, nullptr, 0, 11, schemas, file_default_instances, TableStruct_MNN_5fcompression_2eproto::offsets, file_level_metadata_MNN_5fcompression_2eproto, file_level_enum_descriptors_MNN_5fcompression_2eproto, file_level_service_descriptors_MNN_5fcompression_2eproto, }; @@ -1107,14 +1159,20 @@ class LayerQuantizeParams_WeightParams::_Internal { (*has_bits)[0] |= 1u; } static void set_has_bits(HasBits* has_bits) { - (*has_bits)[0] |= 4u; + (*has_bits)[0] |= 16u; } static void set_has_clamp_min(HasBits* has_bits) { - (*has_bits)[0] |= 8u; + (*has_bits)[0] |= 32u; } static void set_has_clamp_max(HasBits* has_bits) { + (*has_bits)[0] |= 8u; + } + static void set_has_asymmetric(HasBits* has_bits) { (*has_bits)[0] |= 2u; } + static void set_has_block_size(HasBits* has_bits) { + (*has_bits)[0] |= 4u; + } static bool MissingRequiredFields(const HasBits& has_bits) { return ((has_bits[0] & 0x00000001) ^ 0x00000001) != 0; } @@ -1143,9 +1201,9 @@ LayerQuantizeParams_WeightParams::LayerQuantizeParams_WeightParams(const LayerQu name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, from._internal_name(), GetArenaForAllocation()); } - ::memcpy(&clamp_max_, &from.clamp_max_, + ::memcpy(&asymmetric_, &from.asymmetric_, static_cast(reinterpret_cast(&clamp_min_) - - reinterpret_cast(&clamp_max_)) + sizeof(clamp_min_)); + reinterpret_cast(&asymmetric_)) + sizeof(clamp_min_)); // @@protoc_insertion_point(copy_constructor:MNN.Compression.LayerQuantizeParams.WeightParams) } @@ -1154,6 +1212,10 @@ name_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlready #ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); #endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING +::memset(reinterpret_cast(this) + static_cast( + reinterpret_cast(&asymmetric_) - reinterpret_cast(this)), + 0, static_cast(reinterpret_cast(&block_size_) - + reinterpret_cast(&asymmetric_)) + sizeof(block_size_)); clamp_max_ = 127; bits_ = 8; clamp_min_ = -128; @@ -1189,10 +1251,13 @@ void LayerQuantizeParams_WeightParams::Clear() { scales_.Clear(); cached_has_bits = _has_bits_[0]; - if (cached_has_bits & 0x0000000fu) { - if (cached_has_bits & 0x00000001u) { - name_.ClearNonDefaultToEmpty(); - } + if (cached_has_bits & 0x00000001u) { + name_.ClearNonDefaultToEmpty(); + } + if (cached_has_bits & 0x0000003eu) { + ::memset(&asymmetric_, 0, static_cast( + reinterpret_cast(&block_size_) - + reinterpret_cast(&asymmetric_)) + sizeof(block_size_)); clamp_max_ = 127; bits_ = 8; clamp_min_ = -128; @@ -1263,6 +1328,24 @@ const char* LayerQuantizeParams_WeightParams::_InternalParse(const char* ptr, :: } else goto handle_unusual; continue; + // optional bool asymmetric = 6 [default = false]; + case 6: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 48)) { + _Internal::set_has_asymmetric(&has_bits); + asymmetric_ = ::PROTOBUF_NAMESPACE_ID::internal::ReadVarint64(&ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; + // optional int32 block_size = 7 [default = 0]; + case 7: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 56)) { + _Internal::set_has_block_size(&has_bits); + block_size_ = ::PROTOBUF_NAMESPACE_ID::internal::ReadVarint32(&ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; default: goto handle_unusual; } // switch @@ -1305,7 +1388,7 @@ uint8_t* LayerQuantizeParams_WeightParams::_InternalSerialize( } // optional int32 bits = 2 [default = 8]; - if (cached_has_bits & 0x00000004u) { + if (cached_has_bits & 0x00000010u) { target = stream->EnsureSpace(target); target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(2, this->_internal_bits(), target); } @@ -1317,17 +1400,29 @@ uint8_t* LayerQuantizeParams_WeightParams::_InternalSerialize( } // optional int32 clamp_min = 4 [default = -128]; - if (cached_has_bits & 0x00000008u) { + if (cached_has_bits & 0x00000020u) { target = stream->EnsureSpace(target); target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(4, this->_internal_clamp_min(), target); } // optional int32 clamp_max = 5 [default = 127]; - if (cached_has_bits & 0x00000002u) { + if (cached_has_bits & 0x00000008u) { target = stream->EnsureSpace(target); target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(5, this->_internal_clamp_max(), target); } + // optional bool asymmetric = 6 [default = false]; + if (cached_has_bits & 0x00000002u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteBoolToArray(6, this->_internal_asymmetric(), target); + } + + // optional int32 block_size = 7 [default = 0]; + if (cached_has_bits & 0x00000004u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(7, this->_internal_block_size(), target); + } + if (PROTOBUF_PREDICT_FALSE(_internal_metadata_.have_unknown_fields())) { target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::InternalSerializeUnknownFieldsToArray( _internal_metadata_.unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(::PROTOBUF_NAMESPACE_ID::UnknownFieldSet::default_instance), target, stream); @@ -1360,19 +1455,29 @@ size_t LayerQuantizeParams_WeightParams::ByteSizeLong() const { } cached_has_bits = _has_bits_[0]; - if (cached_has_bits & 0x0000000eu) { - // optional int32 clamp_max = 5 [default = 127]; + if (cached_has_bits & 0x0000003eu) { + // optional bool asymmetric = 6 [default = false]; if (cached_has_bits & 0x00000002u) { + total_size += 1 + 1; + } + + // optional int32 block_size = 7 [default = 0]; + if (cached_has_bits & 0x00000004u) { + total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_block_size()); + } + + // optional int32 clamp_max = 5 [default = 127]; + if (cached_has_bits & 0x00000008u) { total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_clamp_max()); } // optional int32 bits = 2 [default = 8]; - if (cached_has_bits & 0x00000004u) { + if (cached_has_bits & 0x00000010u) { total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_bits()); } // optional int32 clamp_min = 4 [default = -128]; - if (cached_has_bits & 0x00000008u) { + if (cached_has_bits & 0x00000020u) { total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_clamp_min()); } @@ -1401,17 +1506,23 @@ void LayerQuantizeParams_WeightParams::MergeFrom(const LayerQuantizeParams_Weigh scales_.MergeFrom(from.scales_); cached_has_bits = from._has_bits_[0]; - if (cached_has_bits & 0x0000000fu) { + if (cached_has_bits & 0x0000003fu) { if (cached_has_bits & 0x00000001u) { _internal_set_name(from._internal_name()); } if (cached_has_bits & 0x00000002u) { - clamp_max_ = from.clamp_max_; + asymmetric_ = from.asymmetric_; } if (cached_has_bits & 0x00000004u) { - bits_ = from.bits_; + block_size_ = from.block_size_; } if (cached_has_bits & 0x00000008u) { + clamp_max_ = from.clamp_max_; + } + if (cached_has_bits & 0x00000010u) { + bits_ = from.bits_; + } + if (cached_has_bits & 0x00000020u) { clamp_min_ = from.clamp_min_; } _has_bits_[0] |= cached_has_bits; @@ -1443,6 +1554,12 @@ void LayerQuantizeParams_WeightParams::InternalSwap(LayerQuantizeParams_WeightPa &name_, lhs_arena, &other->name_, rhs_arena ); + ::PROTOBUF_NAMESPACE_ID::internal::memswap< + PROTOBUF_FIELD_OFFSET(LayerQuantizeParams_WeightParams, block_size_) + + sizeof(LayerQuantizeParams_WeightParams::block_size_) + - PROTOBUF_FIELD_OFFSET(LayerQuantizeParams_WeightParams, asymmetric_)>( + reinterpret_cast(&asymmetric_), + reinterpret_cast(&other->asymmetric_)); swap(clamp_max_, other->clamp_max_); swap(bits_, other->bits_); swap(clamp_min_, other->clamp_min_); @@ -1684,22 +1801,326 @@ ::PROTOBUF_NAMESPACE_ID::Metadata LayerQuantizeParams_WinogradParams::GetMetadat // =================================================================== +class LayerQuantizeParams_ConvolutionInfo::_Internal { + public: + using HasBits = decltype(std::declval()._has_bits_); + static void set_has_input_channel(HasBits* has_bits) { + (*has_bits)[0] |= 1u; + } + static void set_has_output_channel(HasBits* has_bits) { + (*has_bits)[0] |= 2u; + } + static bool MissingRequiredFields(const HasBits& has_bits) { + return ((has_bits[0] & 0x00000003) ^ 0x00000003) != 0; + } +}; + +LayerQuantizeParams_ConvolutionInfo::LayerQuantizeParams_ConvolutionInfo(::PROTOBUF_NAMESPACE_ID::Arena* arena, + bool is_message_owned) + : ::PROTOBUF_NAMESPACE_ID::Message(arena, is_message_owned), + kernel_size_(arena) { + SharedCtor(); + if (!is_message_owned) { + RegisterArenaDtor(arena); + } + // @@protoc_insertion_point(arena_constructor:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) +} +LayerQuantizeParams_ConvolutionInfo::LayerQuantizeParams_ConvolutionInfo(const LayerQuantizeParams_ConvolutionInfo& from) + : ::PROTOBUF_NAMESPACE_ID::Message(), + _has_bits_(from._has_bits_), + kernel_size_(from.kernel_size_) { + _internal_metadata_.MergeFrom<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(from._internal_metadata_); + ::memcpy(&input_channel_, &from.input_channel_, + static_cast(reinterpret_cast(&output_channel_) - + reinterpret_cast(&input_channel_)) + sizeof(output_channel_)); + // @@protoc_insertion_point(copy_constructor:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) +} + +inline void LayerQuantizeParams_ConvolutionInfo::SharedCtor() { +::memset(reinterpret_cast(this) + static_cast( + reinterpret_cast(&input_channel_) - reinterpret_cast(this)), + 0, static_cast(reinterpret_cast(&output_channel_) - + reinterpret_cast(&input_channel_)) + sizeof(output_channel_)); +} + +LayerQuantizeParams_ConvolutionInfo::~LayerQuantizeParams_ConvolutionInfo() { + // @@protoc_insertion_point(destructor:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + if (GetArenaForAllocation() != nullptr) return; + SharedDtor(); + _internal_metadata_.Delete<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(); +} + +inline void LayerQuantizeParams_ConvolutionInfo::SharedDtor() { + GOOGLE_DCHECK(GetArenaForAllocation() == nullptr); +} + +void LayerQuantizeParams_ConvolutionInfo::ArenaDtor(void* object) { + LayerQuantizeParams_ConvolutionInfo* _this = reinterpret_cast< LayerQuantizeParams_ConvolutionInfo* >(object); + (void)_this; +} +void LayerQuantizeParams_ConvolutionInfo::RegisterArenaDtor(::PROTOBUF_NAMESPACE_ID::Arena*) { +} +void LayerQuantizeParams_ConvolutionInfo::SetCachedSize(int size) const { + _cached_size_.Set(size); +} + +void LayerQuantizeParams_ConvolutionInfo::Clear() { +// @@protoc_insertion_point(message_clear_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + uint32_t cached_has_bits = 0; + // Prevent compiler warnings about cached_has_bits being unused + (void) cached_has_bits; + + kernel_size_.Clear(); + cached_has_bits = _has_bits_[0]; + if (cached_has_bits & 0x00000003u) { + ::memset(&input_channel_, 0, static_cast( + reinterpret_cast(&output_channel_) - + reinterpret_cast(&input_channel_)) + sizeof(output_channel_)); + } + _has_bits_.Clear(); + _internal_metadata_.Clear<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(); +} + +const char* LayerQuantizeParams_ConvolutionInfo::_InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::internal::ParseContext* ctx) { +#define CHK_(x) if (PROTOBUF_PREDICT_FALSE(!(x))) goto failure + _Internal::HasBits has_bits{}; + while (!ctx->Done(&ptr)) { + uint32_t tag; + ptr = ::PROTOBUF_NAMESPACE_ID::internal::ReadTag(ptr, &tag); + switch (tag >> 3) { + // required int32 input_channel = 1; + case 1: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 8)) { + _Internal::set_has_input_channel(&has_bits); + input_channel_ = ::PROTOBUF_NAMESPACE_ID::internal::ReadVarint32(&ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; + // required int32 output_channel = 2; + case 2: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 16)) { + _Internal::set_has_output_channel(&has_bits); + output_channel_ = ::PROTOBUF_NAMESPACE_ID::internal::ReadVarint32(&ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; + // repeated int32 kernel_size = 3; + case 3: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 24)) { + ptr -= 1; + do { + ptr += 1; + _internal_add_kernel_size(::PROTOBUF_NAMESPACE_ID::internal::ReadVarint32(&ptr)); + CHK_(ptr); + if (!ctx->DataAvailable(ptr)) break; + } while (::PROTOBUF_NAMESPACE_ID::internal::ExpectTag<24>(ptr)); + } else if (static_cast(tag) == 26) { + ptr = ::PROTOBUF_NAMESPACE_ID::internal::PackedInt32Parser(_internal_mutable_kernel_size(), ptr, ctx); + CHK_(ptr); + } else + goto handle_unusual; + continue; + default: + goto handle_unusual; + } // switch + handle_unusual: + if ((tag == 0) || ((tag & 7) == 4)) { + CHK_(ptr); + ctx->SetLastTag(tag); + goto message_done; + } + ptr = UnknownFieldParse( + tag, + _internal_metadata_.mutable_unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(), + ptr, ctx); + CHK_(ptr != nullptr); + } // while +message_done: + _has_bits_.Or(has_bits); + return ptr; +failure: + ptr = nullptr; + goto message_done; +#undef CHK_ +} + +uint8_t* LayerQuantizeParams_ConvolutionInfo::_InternalSerialize( + uint8_t* target, ::PROTOBUF_NAMESPACE_ID::io::EpsCopyOutputStream* stream) const { + // @@protoc_insertion_point(serialize_to_array_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + uint32_t cached_has_bits = 0; + (void) cached_has_bits; + + cached_has_bits = _has_bits_[0]; + // required int32 input_channel = 1; + if (cached_has_bits & 0x00000001u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(1, this->_internal_input_channel(), target); + } + + // required int32 output_channel = 2; + if (cached_has_bits & 0x00000002u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(2, this->_internal_output_channel(), target); + } + + // repeated int32 kernel_size = 3; + for (int i = 0, n = this->_internal_kernel_size_size(); i < n; i++) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteInt32ToArray(3, this->_internal_kernel_size(i), target); + } + + if (PROTOBUF_PREDICT_FALSE(_internal_metadata_.have_unknown_fields())) { + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::InternalSerializeUnknownFieldsToArray( + _internal_metadata_.unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(::PROTOBUF_NAMESPACE_ID::UnknownFieldSet::default_instance), target, stream); + } + // @@protoc_insertion_point(serialize_to_array_end:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + return target; +} + +size_t LayerQuantizeParams_ConvolutionInfo::RequiredFieldsByteSizeFallback() const { +// @@protoc_insertion_point(required_fields_byte_size_fallback_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + size_t total_size = 0; + + if (_internal_has_input_channel()) { + // required int32 input_channel = 1; + total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_input_channel()); + } + + if (_internal_has_output_channel()) { + // required int32 output_channel = 2; + total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_output_channel()); + } + + return total_size; +} +size_t LayerQuantizeParams_ConvolutionInfo::ByteSizeLong() const { +// @@protoc_insertion_point(message_byte_size_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + size_t total_size = 0; + + if (((_has_bits_[0] & 0x00000003) ^ 0x00000003) == 0) { // All required fields are present. + // required int32 input_channel = 1; + total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_input_channel()); + + // required int32 output_channel = 2; + total_size += ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::Int32SizePlusOne(this->_internal_output_channel()); + + } else { + total_size += RequiredFieldsByteSizeFallback(); + } + uint32_t cached_has_bits = 0; + // Prevent compiler warnings about cached_has_bits being unused + (void) cached_has_bits; + + // repeated int32 kernel_size = 3; + { + size_t data_size = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite:: + Int32Size(this->kernel_size_); + total_size += 1 * + ::PROTOBUF_NAMESPACE_ID::internal::FromIntSize(this->_internal_kernel_size_size()); + total_size += data_size; + } + + return MaybeComputeUnknownFieldsSize(total_size, &_cached_size_); +} + +const ::PROTOBUF_NAMESPACE_ID::Message::ClassData LayerQuantizeParams_ConvolutionInfo::_class_data_ = { + ::PROTOBUF_NAMESPACE_ID::Message::CopyWithSizeCheck, + LayerQuantizeParams_ConvolutionInfo::MergeImpl +}; +const ::PROTOBUF_NAMESPACE_ID::Message::ClassData*LayerQuantizeParams_ConvolutionInfo::GetClassData() const { return &_class_data_; } + +void LayerQuantizeParams_ConvolutionInfo::MergeImpl(::PROTOBUF_NAMESPACE_ID::Message* to, + const ::PROTOBUF_NAMESPACE_ID::Message& from) { + static_cast(to)->MergeFrom( + static_cast(from)); +} + + +void LayerQuantizeParams_ConvolutionInfo::MergeFrom(const LayerQuantizeParams_ConvolutionInfo& from) { +// @@protoc_insertion_point(class_specific_merge_from_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + GOOGLE_DCHECK_NE(&from, this); + uint32_t cached_has_bits = 0; + (void) cached_has_bits; + + kernel_size_.MergeFrom(from.kernel_size_); + cached_has_bits = from._has_bits_[0]; + if (cached_has_bits & 0x00000003u) { + if (cached_has_bits & 0x00000001u) { + input_channel_ = from.input_channel_; + } + if (cached_has_bits & 0x00000002u) { + output_channel_ = from.output_channel_; + } + _has_bits_[0] |= cached_has_bits; + } + _internal_metadata_.MergeFrom<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(from._internal_metadata_); +} + +void LayerQuantizeParams_ConvolutionInfo::CopyFrom(const LayerQuantizeParams_ConvolutionInfo& from) { +// @@protoc_insertion_point(class_specific_copy_from_start:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + if (&from == this) return; + Clear(); + MergeFrom(from); +} + +bool LayerQuantizeParams_ConvolutionInfo::IsInitialized() const { + if (_Internal::MissingRequiredFields(_has_bits_)) return false; + return true; +} + +void LayerQuantizeParams_ConvolutionInfo::InternalSwap(LayerQuantizeParams_ConvolutionInfo* other) { + using std::swap; + _internal_metadata_.InternalSwap(&other->_internal_metadata_); + swap(_has_bits_[0], other->_has_bits_[0]); + kernel_size_.InternalSwap(&other->kernel_size_); + ::PROTOBUF_NAMESPACE_ID::internal::memswap< + PROTOBUF_FIELD_OFFSET(LayerQuantizeParams_ConvolutionInfo, output_channel_) + + sizeof(LayerQuantizeParams_ConvolutionInfo::output_channel_) + - PROTOBUF_FIELD_OFFSET(LayerQuantizeParams_ConvolutionInfo, input_channel_)>( + reinterpret_cast(&input_channel_), + reinterpret_cast(&other->input_channel_)); +} + +::PROTOBUF_NAMESPACE_ID::Metadata LayerQuantizeParams_ConvolutionInfo::GetMetadata() const { + return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( + &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, + file_level_metadata_MNN_5fcompression_2eproto[4]); +} + +// =================================================================== + class LayerQuantizeParams::_Internal { public: using HasBits = decltype(std::declval()._has_bits_); static void set_has_method(HasBits* has_bits) { - (*has_bits)[0] |= 2u; + (*has_bits)[0] |= 16u; } static const ::MNN::Compression::LayerQuantizeParams_WinogradParams& wino_params(const LayerQuantizeParams* msg); static void set_has_wino_params(HasBits* has_bits) { + (*has_bits)[0] |= 4u; + } + static void set_has_op_name(HasBits* has_bits) { (*has_bits)[0] |= 1u; } + static void set_has_subgraph_name(HasBits* has_bits) { + (*has_bits)[0] |= 2u; + } + static const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& conv(const LayerQuantizeParams* msg); + static void set_has_conv(HasBits* has_bits) { + (*has_bits)[0] |= 8u; + } }; const ::MNN::Compression::LayerQuantizeParams_WinogradParams& LayerQuantizeParams::_Internal::wino_params(const LayerQuantizeParams* msg) { return *msg->wino_params_; } +const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& +LayerQuantizeParams::_Internal::conv(const LayerQuantizeParams* msg) { + return *msg->conv_; +} LayerQuantizeParams::LayerQuantizeParams(::PROTOBUF_NAMESPACE_ID::Arena* arena, bool is_message_owned) : ::PROTOBUF_NAMESPACE_ID::Message(arena, is_message_owned), @@ -1719,16 +2140,45 @@ LayerQuantizeParams::LayerQuantizeParams(const LayerQuantizeParams& from) weight_(from.weight_), output_(from.output_) { _internal_metadata_.MergeFrom<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(from._internal_metadata_); + op_name_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); + #ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + op_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + #endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (from._internal_has_op_name()) { + op_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, from._internal_op_name(), + GetArenaForAllocation()); + } + subgraph_name_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); + #ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + subgraph_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + #endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (from._internal_has_subgraph_name()) { + subgraph_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, from._internal_subgraph_name(), + GetArenaForAllocation()); + } if (from._internal_has_wino_params()) { wino_params_ = new ::MNN::Compression::LayerQuantizeParams_WinogradParams(*from.wino_params_); } else { wino_params_ = nullptr; } + if (from._internal_has_conv()) { + conv_ = new ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo(*from.conv_); + } else { + conv_ = nullptr; + } method_ = from.method_; // @@protoc_insertion_point(copy_constructor:MNN.Compression.LayerQuantizeParams) } inline void LayerQuantizeParams::SharedCtor() { +op_name_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + op_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING +subgraph_name_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + subgraph_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING ::memset(reinterpret_cast(this) + static_cast( reinterpret_cast(&wino_params_) - reinterpret_cast(this)), 0, static_cast(reinterpret_cast(&method_) - @@ -1744,7 +2194,10 @@ LayerQuantizeParams::~LayerQuantizeParams() { inline void LayerQuantizeParams::SharedDtor() { GOOGLE_DCHECK(GetArenaForAllocation() == nullptr); + op_name_.DestroyNoArena(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); + subgraph_name_.DestroyNoArena(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited()); if (this != internal_default_instance()) delete wino_params_; + if (this != internal_default_instance()) delete conv_; } void LayerQuantizeParams::ArenaDtor(void* object) { @@ -1767,9 +2220,21 @@ void LayerQuantizeParams::Clear() { weight_.Clear(); output_.Clear(); cached_has_bits = _has_bits_[0]; - if (cached_has_bits & 0x00000001u) { - GOOGLE_DCHECK(wino_params_ != nullptr); - wino_params_->Clear(); + if (cached_has_bits & 0x0000000fu) { + if (cached_has_bits & 0x00000001u) { + op_name_.ClearNonDefaultToEmpty(); + } + if (cached_has_bits & 0x00000002u) { + subgraph_name_.ClearNonDefaultToEmpty(); + } + if (cached_has_bits & 0x00000004u) { + GOOGLE_DCHECK(wino_params_ != nullptr); + wino_params_->Clear(); + } + if (cached_has_bits & 0x00000008u) { + GOOGLE_DCHECK(conv_ != nullptr); + conv_->Clear(); + } } method_ = 0; _has_bits_.Clear(); @@ -1843,6 +2308,38 @@ const char* LayerQuantizeParams::_InternalParse(const char* ptr, ::PROTOBUF_NAME } else goto handle_unusual; continue; + // optional string op_name = 6; + case 6: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 50)) { + auto str = _internal_mutable_op_name(); + ptr = ::PROTOBUF_NAMESPACE_ID::internal::InlineGreedyStringParser(str, ptr, ctx); + #ifndef NDEBUG + ::PROTOBUF_NAMESPACE_ID::internal::VerifyUTF8(str, "MNN.Compression.LayerQuantizeParams.op_name"); + #endif // !NDEBUG + CHK_(ptr); + } else + goto handle_unusual; + continue; + // optional string subgraph_name = 7; + case 7: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 58)) { + auto str = _internal_mutable_subgraph_name(); + ptr = ::PROTOBUF_NAMESPACE_ID::internal::InlineGreedyStringParser(str, ptr, ctx); + #ifndef NDEBUG + ::PROTOBUF_NAMESPACE_ID::internal::VerifyUTF8(str, "MNN.Compression.LayerQuantizeParams.subgraph_name"); + #endif // !NDEBUG + CHK_(ptr); + } else + goto handle_unusual; + continue; + // optional .MNN.Compression.LayerQuantizeParams.ConvolutionInfo conv = 8; + case 8: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 66)) { + ptr = ctx->ParseMessage(_internal_mutable_conv(), ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; default: goto handle_unusual; } // switch @@ -1899,20 +2396,48 @@ uint8_t* LayerQuantizeParams::_InternalSerialize( cached_has_bits = _has_bits_[0]; // optional .MNN.Compression.LayerQuantizeParams.QuantMethod method = 4 [default = QAT]; - if (cached_has_bits & 0x00000002u) { + if (cached_has_bits & 0x00000010u) { target = stream->EnsureSpace(target); target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteEnumToArray( 4, this->_internal_method(), target); } // optional .MNN.Compression.LayerQuantizeParams.WinogradParams wino_params = 5; - if (cached_has_bits & 0x00000001u) { + if (cached_has_bits & 0x00000004u) { target = stream->EnsureSpace(target); target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite:: InternalWriteMessage( 5, _Internal::wino_params(this), target, stream); } + // optional string op_name = 6; + if (cached_has_bits & 0x00000001u) { + ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::VerifyUTF8StringNamedField( + this->_internal_op_name().data(), static_cast(this->_internal_op_name().length()), + ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::SERIALIZE, + "MNN.Compression.LayerQuantizeParams.op_name"); + target = stream->WriteStringMaybeAliased( + 6, this->_internal_op_name(), target); + } + + // optional string subgraph_name = 7; + if (cached_has_bits & 0x00000002u) { + ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::VerifyUTF8StringNamedField( + this->_internal_subgraph_name().data(), static_cast(this->_internal_subgraph_name().length()), + ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::SERIALIZE, + "MNN.Compression.LayerQuantizeParams.subgraph_name"); + target = stream->WriteStringMaybeAliased( + 7, this->_internal_subgraph_name(), target); + } + + // optional .MNN.Compression.LayerQuantizeParams.ConvolutionInfo conv = 8; + if (cached_has_bits & 0x00000008u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite:: + InternalWriteMessage( + 8, _Internal::conv(this), target, stream); + } + if (PROTOBUF_PREDICT_FALSE(_internal_metadata_.have_unknown_fields())) { target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::InternalSerializeUnknownFieldsToArray( _internal_metadata_.unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(::PROTOBUF_NAMESPACE_ID::UnknownFieldSet::default_instance), target, stream); @@ -1951,16 +2476,37 @@ size_t LayerQuantizeParams::ByteSizeLong() const { } cached_has_bits = _has_bits_[0]; - if (cached_has_bits & 0x00000003u) { - // optional .MNN.Compression.LayerQuantizeParams.WinogradParams wino_params = 5; + if (cached_has_bits & 0x0000001fu) { + // optional string op_name = 6; if (cached_has_bits & 0x00000001u) { + total_size += 1 + + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::StringSize( + this->_internal_op_name()); + } + + // optional string subgraph_name = 7; + if (cached_has_bits & 0x00000002u) { + total_size += 1 + + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::StringSize( + this->_internal_subgraph_name()); + } + + // optional .MNN.Compression.LayerQuantizeParams.WinogradParams wino_params = 5; + if (cached_has_bits & 0x00000004u) { total_size += 1 + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::MessageSize( *wino_params_); } + // optional .MNN.Compression.LayerQuantizeParams.ConvolutionInfo conv = 8; + if (cached_has_bits & 0x00000008u) { + total_size += 1 + + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::MessageSize( + *conv_); + } + // optional .MNN.Compression.LayerQuantizeParams.QuantMethod method = 4 [default = QAT]; - if (cached_has_bits & 0x00000002u) { + if (cached_has_bits & 0x00000010u) { total_size += 1 + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::EnumSize(this->_internal_method()); } @@ -1992,11 +2538,20 @@ void LayerQuantizeParams::MergeFrom(const LayerQuantizeParams& from) { weight_.MergeFrom(from.weight_); output_.MergeFrom(from.output_); cached_has_bits = from._has_bits_[0]; - if (cached_has_bits & 0x00000003u) { + if (cached_has_bits & 0x0000001fu) { if (cached_has_bits & 0x00000001u) { - _internal_mutable_wino_params()->::MNN::Compression::LayerQuantizeParams_WinogradParams::MergeFrom(from._internal_wino_params()); + _internal_set_op_name(from._internal_op_name()); } if (cached_has_bits & 0x00000002u) { + _internal_set_subgraph_name(from._internal_subgraph_name()); + } + if (cached_has_bits & 0x00000004u) { + _internal_mutable_wino_params()->::MNN::Compression::LayerQuantizeParams_WinogradParams::MergeFrom(from._internal_wino_params()); + } + if (cached_has_bits & 0x00000008u) { + _internal_mutable_conv()->::MNN::Compression::LayerQuantizeParams_ConvolutionInfo::MergeFrom(from._internal_conv()); + } + if (cached_has_bits & 0x00000010u) { method_ = from.method_; } _has_bits_[0] |= cached_has_bits; @@ -2021,16 +2576,31 @@ bool LayerQuantizeParams::IsInitialized() const { if (_internal_has_wino_params()) { if (!wino_params_->IsInitialized()) return false; } + if (_internal_has_conv()) { + if (!conv_->IsInitialized()) return false; + } return true; } void LayerQuantizeParams::InternalSwap(LayerQuantizeParams* other) { using std::swap; + auto* lhs_arena = GetArenaForAllocation(); + auto* rhs_arena = other->GetArenaForAllocation(); _internal_metadata_.InternalSwap(&other->_internal_metadata_); swap(_has_bits_[0], other->_has_bits_[0]); input_.InternalSwap(&other->input_); weight_.InternalSwap(&other->weight_); output_.InternalSwap(&other->output_); + ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::InternalSwap( + &::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), + &op_name_, lhs_arena, + &other->op_name_, rhs_arena + ); + ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::InternalSwap( + &::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), + &subgraph_name_, lhs_arena, + &other->subgraph_name_, rhs_arena + ); ::PROTOBUF_NAMESPACE_ID::internal::memswap< PROTOBUF_FIELD_OFFSET(LayerQuantizeParams, method_) + sizeof(LayerQuantizeParams::method_) @@ -2042,7 +2612,7 @@ void LayerQuantizeParams::InternalSwap(LayerQuantizeParams* other) { ::PROTOBUF_NAMESPACE_ID::Metadata LayerQuantizeParams::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[4]); + file_level_metadata_MNN_5fcompression_2eproto[5]); } // =================================================================== @@ -2310,7 +2880,7 @@ void LevelPrunerParams::InternalSwap(LevelPrunerParams* other) { ::PROTOBUF_NAMESPACE_ID::Metadata LevelPrunerParams::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[5]); + file_level_metadata_MNN_5fcompression_2eproto[6]); } // =================================================================== @@ -2614,7 +3184,7 @@ void SIMDOCPrunerParams::InternalSwap(SIMDOCPrunerParams* other) { ::PROTOBUF_NAMESPACE_ID::Metadata SIMDOCPrunerParams::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[6]); + file_level_metadata_MNN_5fcompression_2eproto[7]); } // =================================================================== @@ -2915,7 +3485,7 @@ void PruneParams::InternalSwap(PruneParams* other) { ::PROTOBUF_NAMESPACE_ID::Metadata PruneParams::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[7]); + file_level_metadata_MNN_5fcompression_2eproto[8]); } // =================================================================== @@ -3219,7 +3789,7 @@ void CompressionAlgo::InternalSwap(CompressionAlgo* other) { ::PROTOBUF_NAMESPACE_ID::Metadata CompressionAlgo::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[8]); + file_level_metadata_MNN_5fcompression_2eproto[9]); } // =================================================================== @@ -3233,6 +3803,9 @@ class Pipeline::_Internal { static void set_has_mnn_uuid(HasBits* has_bits) { (*has_bits)[0] |= 2u; } + static void set_has_for_guide(HasBits* has_bits) { + (*has_bits)[0] |= 4u; + } static bool MissingRequiredFields(const HasBits& has_bits) { return ((has_bits[0] & 0x00000001) ^ 0x00000001) != 0; } @@ -3267,6 +3840,7 @@ Pipeline::Pipeline(const Pipeline& from) mnn_uuid_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, from._internal_mnn_uuid(), GetArenaForAllocation()); } + for_guide_ = from.for_guide_; // @@protoc_insertion_point(copy_constructor:MNN.Compression.Pipeline) } @@ -3276,6 +3850,7 @@ mnn_uuid_.UnsafeSetDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlr #ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING mnn_uuid_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); #endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING +for_guide_ = false; } Pipeline::~Pipeline() { @@ -3317,6 +3892,7 @@ void Pipeline::Clear() { mnn_uuid_.ClearNonDefaultToEmpty(); } } + for_guide_ = false; _has_bits_.Clear(); _internal_metadata_.Clear<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(); } @@ -3365,6 +3941,15 @@ const char* Pipeline::_InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::i } else goto handle_unusual; continue; + // optional bool for_guide = 4 [default = false]; + case 4: + if (PROTOBUF_PREDICT_TRUE(static_cast(tag) == 32)) { + _Internal::set_has_for_guide(&has_bits); + for_guide_ = ::PROTOBUF_NAMESPACE_ID::internal::ReadVarint64(&ptr); + CHK_(ptr); + } else + goto handle_unusual; + continue; default: goto handle_unusual; } // switch @@ -3424,6 +4009,12 @@ uint8_t* Pipeline::_InternalSerialize( 3, this->_internal_mnn_uuid(), target); } + // optional bool for_guide = 4 [default = false]; + if (cached_has_bits & 0x00000004u) { + target = stream->EnsureSpace(target); + target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::WriteBoolToArray(4, this->_internal_for_guide(), target); + } + if (PROTOBUF_PREDICT_FALSE(_internal_metadata_.have_unknown_fields())) { target = ::PROTOBUF_NAMESPACE_ID::internal::WireFormat::InternalSerializeUnknownFieldsToArray( _internal_metadata_.unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(::PROTOBUF_NAMESPACE_ID::UnknownFieldSet::default_instance), target, stream); @@ -3453,14 +4044,21 @@ size_t Pipeline::ByteSizeLong() const { ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::MessageSize(msg); } - // optional string mnn_uuid = 3; cached_has_bits = _has_bits_[0]; - if (cached_has_bits & 0x00000002u) { - total_size += 1 + - ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::StringSize( - this->_internal_mnn_uuid()); - } + if (cached_has_bits & 0x00000006u) { + // optional string mnn_uuid = 3; + if (cached_has_bits & 0x00000002u) { + total_size += 1 + + ::PROTOBUF_NAMESPACE_ID::internal::WireFormatLite::StringSize( + this->_internal_mnn_uuid()); + } + // optional bool for_guide = 4 [default = false]; + if (cached_has_bits & 0x00000004u) { + total_size += 1 + 1; + } + + } return MaybeComputeUnknownFieldsSize(total_size, &_cached_size_); } @@ -3485,13 +4083,17 @@ void Pipeline::MergeFrom(const Pipeline& from) { algo_.MergeFrom(from.algo_); cached_has_bits = from._has_bits_[0]; - if (cached_has_bits & 0x00000003u) { + if (cached_has_bits & 0x00000007u) { if (cached_has_bits & 0x00000001u) { _internal_set_version(from._internal_version()); } if (cached_has_bits & 0x00000002u) { _internal_set_mnn_uuid(from._internal_mnn_uuid()); } + if (cached_has_bits & 0x00000004u) { + for_guide_ = from.for_guide_; + } + _has_bits_[0] |= cached_has_bits; } _internal_metadata_.MergeFrom<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(from._internal_metadata_); } @@ -3527,12 +4129,13 @@ void Pipeline::InternalSwap(Pipeline* other) { &mnn_uuid_, lhs_arena, &other->mnn_uuid_, rhs_arena ); + swap(for_guide_, other->for_guide_); } ::PROTOBUF_NAMESPACE_ID::Metadata Pipeline::GetMetadata() const { return ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors( &descriptor_table_MNN_5fcompression_2eproto_getter, &descriptor_table_MNN_5fcompression_2eproto_once, - file_level_metadata_MNN_5fcompression_2eproto[9]); + file_level_metadata_MNN_5fcompression_2eproto[10]); } // @@protoc_insertion_point(namespace_scope) @@ -3551,6 +4154,9 @@ template<> PROTOBUF_NOINLINE ::MNN::Compression::LayerQuantizeParams_WeightParam template<> PROTOBUF_NOINLINE ::MNN::Compression::LayerQuantizeParams_WinogradParams* Arena::CreateMaybeMessage< ::MNN::Compression::LayerQuantizeParams_WinogradParams >(Arena* arena) { return Arena::CreateMessageInternal< ::MNN::Compression::LayerQuantizeParams_WinogradParams >(arena); } +template<> PROTOBUF_NOINLINE ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* Arena::CreateMaybeMessage< ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo >(Arena* arena) { + return Arena::CreateMessageInternal< ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo >(arena); +} template<> PROTOBUF_NOINLINE ::MNN::Compression::LayerQuantizeParams* Arena::CreateMaybeMessage< ::MNN::Compression::LayerQuantizeParams >(Arena* arena) { return Arena::CreateMessageInternal< ::MNN::Compression::LayerQuantizeParams >(arena); } diff --git a/tools/converter/source/compression/generated/MNN_compression.pb.h b/tools/converter/source/compression/generated/MNN_compression.pb.h index 657953914..805b82447 100644 --- a/tools/converter/source/compression/generated/MNN_compression.pb.h +++ b/tools/converter/source/compression/generated/MNN_compression.pb.h @@ -47,7 +47,7 @@ struct TableStruct_MNN_5fcompression_2eproto { PROTOBUF_SECTION_VARIABLE(protodesc_cold); static const ::PROTOBUF_NAMESPACE_ID::internal::AuxiliaryParseTableField aux[] PROTOBUF_SECTION_VARIABLE(protodesc_cold); - static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTable schema[10] + static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTable schema[11] PROTOBUF_SECTION_VARIABLE(protodesc_cold); static const ::PROTOBUF_NAMESPACE_ID::internal::FieldMetadata field_metadata[]; static const ::PROTOBUF_NAMESPACE_ID::internal::SerializationTable serialization_table[]; @@ -65,6 +65,9 @@ extern LayerQuantizeParamsDefaultTypeInternal _LayerQuantizeParams_default_insta class LayerQuantizeParams_ActivationParams; struct LayerQuantizeParams_ActivationParamsDefaultTypeInternal; extern LayerQuantizeParams_ActivationParamsDefaultTypeInternal _LayerQuantizeParams_ActivationParams_default_instance_; +class LayerQuantizeParams_ConvolutionInfo; +struct LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal; +extern LayerQuantizeParams_ConvolutionInfoDefaultTypeInternal _LayerQuantizeParams_ConvolutionInfo_default_instance_; class LayerQuantizeParams_WeightParams; struct LayerQuantizeParams_WeightParamsDefaultTypeInternal; extern LayerQuantizeParams_WeightParamsDefaultTypeInternal _LayerQuantizeParams_WeightParams_default_instance_; @@ -92,6 +95,7 @@ PROTOBUF_NAMESPACE_OPEN template<> ::MNN::Compression::CompressionAlgo* Arena::CreateMaybeMessage<::MNN::Compression::CompressionAlgo>(Arena*); template<> ::MNN::Compression::LayerQuantizeParams* Arena::CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams>(Arena*); template<> ::MNN::Compression::LayerQuantizeParams_ActivationParams* Arena::CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_ActivationParams>(Arena*); +template<> ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* Arena::CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_ConvolutionInfo>(Arena*); template<> ::MNN::Compression::LayerQuantizeParams_WeightParams* Arena::CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_WeightParams>(Arena*); template<> ::MNN::Compression::LayerQuantizeParams_WinogradParams* Arena::CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_WinogradParams>(Arena*); template<> ::MNN::Compression::LevelPrunerParams* Arena::CreateMaybeMessage<::MNN::Compression::LevelPrunerParams>(Arena*); @@ -788,6 +792,8 @@ class LayerQuantizeParams_WeightParams final : enum : int { kScalesFieldNumber = 3, kNameFieldNumber = 1, + kAsymmetricFieldNumber = 6, + kBlockSizeFieldNumber = 7, kClampMaxFieldNumber = 5, kBitsFieldNumber = 2, kClampMinFieldNumber = 4, @@ -832,6 +838,32 @@ class LayerQuantizeParams_WeightParams final : std::string* _internal_mutable_name(); public: + // optional bool asymmetric = 6 [default = false]; + bool has_asymmetric() const; + private: + bool _internal_has_asymmetric() const; + public: + void clear_asymmetric(); + bool asymmetric() const; + void set_asymmetric(bool value); + private: + bool _internal_asymmetric() const; + void _internal_set_asymmetric(bool value); + public: + + // optional int32 block_size = 7 [default = 0]; + bool has_block_size() const; + private: + bool _internal_has_block_size() const; + public: + void clear_block_size(); + int32_t block_size() const; + void set_block_size(int32_t value); + private: + int32_t _internal_block_size() const; + void _internal_set_block_size(int32_t value); + public: + // optional int32 clamp_max = 5 [default = 127]; bool has_clamp_max() const; private: @@ -882,6 +914,8 @@ class LayerQuantizeParams_WeightParams final : mutable ::PROTOBUF_NAMESPACE_ID::internal::CachedSize _cached_size_; ::PROTOBUF_NAMESPACE_ID::RepeatedField< float > scales_; ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr name_; + bool asymmetric_; + int32_t block_size_; int32_t clamp_max_; int32_t bits_; int32_t clamp_min_; @@ -1071,6 +1105,206 @@ class LayerQuantizeParams_WinogradParams final : }; // ------------------------------------------------------------------- +class LayerQuantizeParams_ConvolutionInfo final : + public ::PROTOBUF_NAMESPACE_ID::Message /* @@protoc_insertion_point(class_definition:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) */ { + public: + inline LayerQuantizeParams_ConvolutionInfo() : LayerQuantizeParams_ConvolutionInfo(nullptr) {} + ~LayerQuantizeParams_ConvolutionInfo() override; + explicit constexpr LayerQuantizeParams_ConvolutionInfo(::PROTOBUF_NAMESPACE_ID::internal::ConstantInitialized); + + LayerQuantizeParams_ConvolutionInfo(const LayerQuantizeParams_ConvolutionInfo& from); + LayerQuantizeParams_ConvolutionInfo(LayerQuantizeParams_ConvolutionInfo&& from) noexcept + : LayerQuantizeParams_ConvolutionInfo() { + *this = ::std::move(from); + } + + inline LayerQuantizeParams_ConvolutionInfo& operator=(const LayerQuantizeParams_ConvolutionInfo& from) { + CopyFrom(from); + return *this; + } + inline LayerQuantizeParams_ConvolutionInfo& operator=(LayerQuantizeParams_ConvolutionInfo&& from) noexcept { + if (this == &from) return *this; + if (GetOwningArena() == from.GetOwningArena() + #ifdef PROTOBUF_FORCE_COPY_IN_MOVE + && GetOwningArena() != nullptr + #endif // !PROTOBUF_FORCE_COPY_IN_MOVE + ) { + InternalSwap(&from); + } else { + CopyFrom(from); + } + return *this; + } + + inline const ::PROTOBUF_NAMESPACE_ID::UnknownFieldSet& unknown_fields() const { + return _internal_metadata_.unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(::PROTOBUF_NAMESPACE_ID::UnknownFieldSet::default_instance); + } + inline ::PROTOBUF_NAMESPACE_ID::UnknownFieldSet* mutable_unknown_fields() { + return _internal_metadata_.mutable_unknown_fields<::PROTOBUF_NAMESPACE_ID::UnknownFieldSet>(); + } + + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* descriptor() { + return GetDescriptor(); + } + static const ::PROTOBUF_NAMESPACE_ID::Descriptor* GetDescriptor() { + return default_instance().GetMetadata().descriptor; + } + static const ::PROTOBUF_NAMESPACE_ID::Reflection* GetReflection() { + return default_instance().GetMetadata().reflection; + } + static const LayerQuantizeParams_ConvolutionInfo& default_instance() { + return *internal_default_instance(); + } + static inline const LayerQuantizeParams_ConvolutionInfo* internal_default_instance() { + return reinterpret_cast( + &_LayerQuantizeParams_ConvolutionInfo_default_instance_); + } + static constexpr int kIndexInFileMessages = + 4; + + friend void swap(LayerQuantizeParams_ConvolutionInfo& a, LayerQuantizeParams_ConvolutionInfo& b) { + a.Swap(&b); + } + inline void Swap(LayerQuantizeParams_ConvolutionInfo* other) { + if (other == this) return; + #ifdef PROTOBUF_FORCE_COPY_IN_SWAP + if (GetOwningArena() != nullptr && + GetOwningArena() == other->GetOwningArena()) { + #else // PROTOBUF_FORCE_COPY_IN_SWAP + if (GetOwningArena() == other->GetOwningArena()) { + #endif // !PROTOBUF_FORCE_COPY_IN_SWAP + InternalSwap(other); + } else { + ::PROTOBUF_NAMESPACE_ID::internal::GenericSwap(this, other); + } + } + void UnsafeArenaSwap(LayerQuantizeParams_ConvolutionInfo* other) { + if (other == this) return; + GOOGLE_DCHECK(GetOwningArena() == other->GetOwningArena()); + InternalSwap(other); + } + + // implements Message ---------------------------------------------- + + LayerQuantizeParams_ConvolutionInfo* New(::PROTOBUF_NAMESPACE_ID::Arena* arena = nullptr) const final { + return CreateMaybeMessage(arena); + } + using ::PROTOBUF_NAMESPACE_ID::Message::CopyFrom; + void CopyFrom(const LayerQuantizeParams_ConvolutionInfo& from); + using ::PROTOBUF_NAMESPACE_ID::Message::MergeFrom; + void MergeFrom(const LayerQuantizeParams_ConvolutionInfo& from); + private: + static void MergeImpl(::PROTOBUF_NAMESPACE_ID::Message* to, const ::PROTOBUF_NAMESPACE_ID::Message& from); + public: + PROTOBUF_ATTRIBUTE_REINITIALIZES void Clear() final; + bool IsInitialized() const final; + + size_t ByteSizeLong() const final; + const char* _InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::internal::ParseContext* ctx) final; + uint8_t* _InternalSerialize( + uint8_t* target, ::PROTOBUF_NAMESPACE_ID::io::EpsCopyOutputStream* stream) const final; + int GetCachedSize() const final { return _cached_size_.Get(); } + + private: + void SharedCtor(); + void SharedDtor(); + void SetCachedSize(int size) const final; + void InternalSwap(LayerQuantizeParams_ConvolutionInfo* other); + + private: + friend class ::PROTOBUF_NAMESPACE_ID::internal::AnyMetadata; + static ::PROTOBUF_NAMESPACE_ID::StringPiece FullMessageName() { + return "MNN.Compression.LayerQuantizeParams.ConvolutionInfo"; + } + protected: + explicit LayerQuantizeParams_ConvolutionInfo(::PROTOBUF_NAMESPACE_ID::Arena* arena, + bool is_message_owned = false); + private: + static void ArenaDtor(void* object); + inline void RegisterArenaDtor(::PROTOBUF_NAMESPACE_ID::Arena* arena); + public: + + static const ClassData _class_data_; + const ::PROTOBUF_NAMESPACE_ID::Message::ClassData*GetClassData() const final; + + ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadata() const final; + + // nested types ---------------------------------------------------- + + // accessors ------------------------------------------------------- + + enum : int { + kKernelSizeFieldNumber = 3, + kInputChannelFieldNumber = 1, + kOutputChannelFieldNumber = 2, + }; + // repeated int32 kernel_size = 3; + int kernel_size_size() const; + private: + int _internal_kernel_size_size() const; + public: + void clear_kernel_size(); + private: + int32_t _internal_kernel_size(int index) const; + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >& + _internal_kernel_size() const; + void _internal_add_kernel_size(int32_t value); + ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >* + _internal_mutable_kernel_size(); + public: + int32_t kernel_size(int index) const; + void set_kernel_size(int index, int32_t value); + void add_kernel_size(int32_t value); + const ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >& + kernel_size() const; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >* + mutable_kernel_size(); + + // required int32 input_channel = 1; + bool has_input_channel() const; + private: + bool _internal_has_input_channel() const; + public: + void clear_input_channel(); + int32_t input_channel() const; + void set_input_channel(int32_t value); + private: + int32_t _internal_input_channel() const; + void _internal_set_input_channel(int32_t value); + public: + + // required int32 output_channel = 2; + bool has_output_channel() const; + private: + bool _internal_has_output_channel() const; + public: + void clear_output_channel(); + int32_t output_channel() const; + void set_output_channel(int32_t value); + private: + int32_t _internal_output_channel() const; + void _internal_set_output_channel(int32_t value); + public: + + // @@protoc_insertion_point(class_scope:MNN.Compression.LayerQuantizeParams.ConvolutionInfo) + private: + class _Internal; + + // helper for ByteSizeLong() + size_t RequiredFieldsByteSizeFallback() const; + + template friend class ::PROTOBUF_NAMESPACE_ID::Arena::InternalHelper; + typedef void InternalArenaConstructable_; + typedef void DestructorSkippable_; + ::PROTOBUF_NAMESPACE_ID::internal::HasBits<1> _has_bits_; + mutable ::PROTOBUF_NAMESPACE_ID::internal::CachedSize _cached_size_; + ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t > kernel_size_; + int32_t input_channel_; + int32_t output_channel_; + friend struct ::TableStruct_MNN_5fcompression_2eproto; +}; +// ------------------------------------------------------------------- + class LayerQuantizeParams final : public ::PROTOBUF_NAMESPACE_ID::Message /* @@protoc_insertion_point(class_definition:MNN.Compression.LayerQuantizeParams) */ { public: @@ -1126,7 +1360,7 @@ class LayerQuantizeParams final : &_LayerQuantizeParams_default_instance_); } static constexpr int kIndexInFileMessages = - 4; + 5; friend void swap(LayerQuantizeParams& a, LayerQuantizeParams& b) { a.Swap(&b); @@ -1200,6 +1434,7 @@ class LayerQuantizeParams final : typedef LayerQuantizeParams_ActivationParams ActivationParams; typedef LayerQuantizeParams_WeightParams WeightParams; typedef LayerQuantizeParams_WinogradParams WinogradParams; + typedef LayerQuantizeParams_ConvolutionInfo ConvolutionInfo; typedef LayerQuantizeParams_QuantMethod QuantMethod; static constexpr QuantMethod QAT = @@ -1239,7 +1474,10 @@ class LayerQuantizeParams final : kInputFieldNumber = 1, kWeightFieldNumber = 2, kOutputFieldNumber = 3, + kOpNameFieldNumber = 6, + kSubgraphNameFieldNumber = 7, kWinoParamsFieldNumber = 5, + kConvFieldNumber = 8, kMethodFieldNumber = 4, }; // repeated .MNN.Compression.LayerQuantizeParams.ActivationParams input = 1; @@ -1296,6 +1534,42 @@ class LayerQuantizeParams final : const ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField< ::MNN::Compression::LayerQuantizeParams_ActivationParams >& output() const; + // optional string op_name = 6; + bool has_op_name() const; + private: + bool _internal_has_op_name() const; + public: + void clear_op_name(); + const std::string& op_name() const; + template + void set_op_name(ArgT0&& arg0, ArgT... args); + std::string* mutable_op_name(); + PROTOBUF_NODISCARD std::string* release_op_name(); + void set_allocated_op_name(std::string* op_name); + private: + const std::string& _internal_op_name() const; + inline PROTOBUF_ALWAYS_INLINE void _internal_set_op_name(const std::string& value); + std::string* _internal_mutable_op_name(); + public: + + // optional string subgraph_name = 7; + bool has_subgraph_name() const; + private: + bool _internal_has_subgraph_name() const; + public: + void clear_subgraph_name(); + const std::string& subgraph_name() const; + template + void set_subgraph_name(ArgT0&& arg0, ArgT... args); + std::string* mutable_subgraph_name(); + PROTOBUF_NODISCARD std::string* release_subgraph_name(); + void set_allocated_subgraph_name(std::string* subgraph_name); + private: + const std::string& _internal_subgraph_name() const; + inline PROTOBUF_ALWAYS_INLINE void _internal_set_subgraph_name(const std::string& value); + std::string* _internal_mutable_subgraph_name(); + public: + // optional .MNN.Compression.LayerQuantizeParams.WinogradParams wino_params = 5; bool has_wino_params() const; private: @@ -1314,6 +1588,24 @@ class LayerQuantizeParams final : ::MNN::Compression::LayerQuantizeParams_WinogradParams* wino_params); ::MNN::Compression::LayerQuantizeParams_WinogradParams* unsafe_arena_release_wino_params(); + // optional .MNN.Compression.LayerQuantizeParams.ConvolutionInfo conv = 8; + bool has_conv() const; + private: + bool _internal_has_conv() const; + public: + void clear_conv(); + const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& conv() const; + PROTOBUF_NODISCARD ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* release_conv(); + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* mutable_conv(); + void set_allocated_conv(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* conv); + private: + const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& _internal_conv() const; + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* _internal_mutable_conv(); + public: + void unsafe_arena_set_allocated_conv( + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* conv); + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* unsafe_arena_release_conv(); + // optional .MNN.Compression.LayerQuantizeParams.QuantMethod method = 4 [default = QAT]; bool has_method() const; private: @@ -1339,7 +1631,10 @@ class LayerQuantizeParams final : ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField< ::MNN::Compression::LayerQuantizeParams_ActivationParams > input_; ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField< ::MNN::Compression::LayerQuantizeParams_WeightParams > weight_; ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField< ::MNN::Compression::LayerQuantizeParams_ActivationParams > output_; + ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr op_name_; + ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr subgraph_name_; ::MNN::Compression::LayerQuantizeParams_WinogradParams* wino_params_; + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* conv_; int method_; friend struct ::TableStruct_MNN_5fcompression_2eproto; }; @@ -1400,7 +1695,7 @@ class LevelPrunerParams final : &_LevelPrunerParams_default_instance_); } static constexpr int kIndexInFileMessages = - 5; + 6; friend void swap(LevelPrunerParams& a, LevelPrunerParams& b) { a.Swap(&b); @@ -1618,7 +1913,7 @@ class SIMDOCPrunerParams final : &_SIMDOCPrunerParams_default_instance_); } static constexpr int kIndexInFileMessages = - 6; + 7; friend void swap(SIMDOCPrunerParams& a, SIMDOCPrunerParams& b) { a.Swap(&b); @@ -1860,7 +2155,7 @@ class PruneParams final : &_PruneParams_default_instance_); } static constexpr int kIndexInFileMessages = - 7; + 8; friend void swap(PruneParams& a, PruneParams& b) { a.Swap(&b); @@ -2090,7 +2385,7 @@ class CompressionAlgo final : &_CompressionAlgo_default_instance_); } static constexpr int kIndexInFileMessages = - 8; + 9; friend void swap(CompressionAlgo& a, CompressionAlgo& b) { a.Swap(&b); @@ -2318,7 +2613,7 @@ class Pipeline final : &_Pipeline_default_instance_); } static constexpr int kIndexInFileMessages = - 9; + 10; friend void swap(Pipeline& a, Pipeline& b) { a.Swap(&b); @@ -2395,6 +2690,7 @@ class Pipeline final : kAlgoFieldNumber = 2, kVersionFieldNumber = 1, kMnnUuidFieldNumber = 3, + kForGuideFieldNumber = 4, }; // repeated .MNN.Compression.CompressionAlgo algo = 2; int algo_size() const; @@ -2450,6 +2746,19 @@ class Pipeline final : std::string* _internal_mutable_mnn_uuid(); public: + // optional bool for_guide = 4 [default = false]; + bool has_for_guide() const; + private: + bool _internal_has_for_guide() const; + public: + void clear_for_guide(); + bool for_guide() const; + void set_for_guide(bool value); + private: + bool _internal_for_guide() const; + void _internal_set_for_guide(bool value); + public: + // @@protoc_insertion_point(class_scope:MNN.Compression.Pipeline) private: class _Internal; @@ -2463,6 +2772,7 @@ class Pipeline final : static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_version_; ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr version_; ::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr mnn_uuid_; + bool for_guide_; friend struct ::TableStruct_MNN_5fcompression_2eproto; }; // =================================================================== @@ -2852,7 +3162,7 @@ inline void LayerQuantizeParams_WeightParams::set_allocated_name(std::string* na // optional int32 bits = 2 [default = 8]; inline bool LayerQuantizeParams_WeightParams::_internal_has_bits() const { - bool value = (_has_bits_[0] & 0x00000004u) != 0; + bool value = (_has_bits_[0] & 0x00000010u) != 0; return value; } inline bool LayerQuantizeParams_WeightParams::has_bits() const { @@ -2860,7 +3170,7 @@ inline bool LayerQuantizeParams_WeightParams::has_bits() const { } inline void LayerQuantizeParams_WeightParams::clear_bits() { bits_ = 8; - _has_bits_[0] &= ~0x00000004u; + _has_bits_[0] &= ~0x00000010u; } inline int32_t LayerQuantizeParams_WeightParams::_internal_bits() const { return bits_; @@ -2870,7 +3180,7 @@ inline int32_t LayerQuantizeParams_WeightParams::bits() const { return _internal_bits(); } inline void LayerQuantizeParams_WeightParams::_internal_set_bits(int32_t value) { - _has_bits_[0] |= 0x00000004u; + _has_bits_[0] |= 0x00000010u; bits_ = value; } inline void LayerQuantizeParams_WeightParams::set_bits(int32_t value) { @@ -2927,7 +3237,7 @@ LayerQuantizeParams_WeightParams::mutable_scales() { // optional int32 clamp_min = 4 [default = -128]; inline bool LayerQuantizeParams_WeightParams::_internal_has_clamp_min() const { - bool value = (_has_bits_[0] & 0x00000008u) != 0; + bool value = (_has_bits_[0] & 0x00000020u) != 0; return value; } inline bool LayerQuantizeParams_WeightParams::has_clamp_min() const { @@ -2935,7 +3245,7 @@ inline bool LayerQuantizeParams_WeightParams::has_clamp_min() const { } inline void LayerQuantizeParams_WeightParams::clear_clamp_min() { clamp_min_ = -128; - _has_bits_[0] &= ~0x00000008u; + _has_bits_[0] &= ~0x00000020u; } inline int32_t LayerQuantizeParams_WeightParams::_internal_clamp_min() const { return clamp_min_; @@ -2945,7 +3255,7 @@ inline int32_t LayerQuantizeParams_WeightParams::clamp_min() const { return _internal_clamp_min(); } inline void LayerQuantizeParams_WeightParams::_internal_set_clamp_min(int32_t value) { - _has_bits_[0] |= 0x00000008u; + _has_bits_[0] |= 0x00000020u; clamp_min_ = value; } inline void LayerQuantizeParams_WeightParams::set_clamp_min(int32_t value) { @@ -2955,7 +3265,7 @@ inline void LayerQuantizeParams_WeightParams::set_clamp_min(int32_t value) { // optional int32 clamp_max = 5 [default = 127]; inline bool LayerQuantizeParams_WeightParams::_internal_has_clamp_max() const { - bool value = (_has_bits_[0] & 0x00000002u) != 0; + bool value = (_has_bits_[0] & 0x00000008u) != 0; return value; } inline bool LayerQuantizeParams_WeightParams::has_clamp_max() const { @@ -2963,7 +3273,7 @@ inline bool LayerQuantizeParams_WeightParams::has_clamp_max() const { } inline void LayerQuantizeParams_WeightParams::clear_clamp_max() { clamp_max_ = 127; - _has_bits_[0] &= ~0x00000002u; + _has_bits_[0] &= ~0x00000008u; } inline int32_t LayerQuantizeParams_WeightParams::_internal_clamp_max() const { return clamp_max_; @@ -2973,7 +3283,7 @@ inline int32_t LayerQuantizeParams_WeightParams::clamp_max() const { return _internal_clamp_max(); } inline void LayerQuantizeParams_WeightParams::_internal_set_clamp_max(int32_t value) { - _has_bits_[0] |= 0x00000002u; + _has_bits_[0] |= 0x00000008u; clamp_max_ = value; } inline void LayerQuantizeParams_WeightParams::set_clamp_max(int32_t value) { @@ -2981,6 +3291,62 @@ inline void LayerQuantizeParams_WeightParams::set_clamp_max(int32_t value) { // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.WeightParams.clamp_max) } +// optional bool asymmetric = 6 [default = false]; +inline bool LayerQuantizeParams_WeightParams::_internal_has_asymmetric() const { + bool value = (_has_bits_[0] & 0x00000002u) != 0; + return value; +} +inline bool LayerQuantizeParams_WeightParams::has_asymmetric() const { + return _internal_has_asymmetric(); +} +inline void LayerQuantizeParams_WeightParams::clear_asymmetric() { + asymmetric_ = false; + _has_bits_[0] &= ~0x00000002u; +} +inline bool LayerQuantizeParams_WeightParams::_internal_asymmetric() const { + return asymmetric_; +} +inline bool LayerQuantizeParams_WeightParams::asymmetric() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.WeightParams.asymmetric) + return _internal_asymmetric(); +} +inline void LayerQuantizeParams_WeightParams::_internal_set_asymmetric(bool value) { + _has_bits_[0] |= 0x00000002u; + asymmetric_ = value; +} +inline void LayerQuantizeParams_WeightParams::set_asymmetric(bool value) { + _internal_set_asymmetric(value); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.WeightParams.asymmetric) +} + +// optional int32 block_size = 7 [default = 0]; +inline bool LayerQuantizeParams_WeightParams::_internal_has_block_size() const { + bool value = (_has_bits_[0] & 0x00000004u) != 0; + return value; +} +inline bool LayerQuantizeParams_WeightParams::has_block_size() const { + return _internal_has_block_size(); +} +inline void LayerQuantizeParams_WeightParams::clear_block_size() { + block_size_ = 0; + _has_bits_[0] &= ~0x00000004u; +} +inline int32_t LayerQuantizeParams_WeightParams::_internal_block_size() const { + return block_size_; +} +inline int32_t LayerQuantizeParams_WeightParams::block_size() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.WeightParams.block_size) + return _internal_block_size(); +} +inline void LayerQuantizeParams_WeightParams::_internal_set_block_size(int32_t value) { + _has_bits_[0] |= 0x00000004u; + block_size_ = value; +} +inline void LayerQuantizeParams_WeightParams::set_block_size(int32_t value) { + _internal_set_block_size(value); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.WeightParams.block_size) +} + // ------------------------------------------------------------------- // LayerQuantizeParams_WinogradParams @@ -3062,6 +3428,113 @@ LayerQuantizeParams_WinogradParams::mutable_units_attr() { // ------------------------------------------------------------------- +// LayerQuantizeParams_ConvolutionInfo + +// required int32 input_channel = 1; +inline bool LayerQuantizeParams_ConvolutionInfo::_internal_has_input_channel() const { + bool value = (_has_bits_[0] & 0x00000001u) != 0; + return value; +} +inline bool LayerQuantizeParams_ConvolutionInfo::has_input_channel() const { + return _internal_has_input_channel(); +} +inline void LayerQuantizeParams_ConvolutionInfo::clear_input_channel() { + input_channel_ = 0; + _has_bits_[0] &= ~0x00000001u; +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::_internal_input_channel() const { + return input_channel_; +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::input_channel() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.input_channel) + return _internal_input_channel(); +} +inline void LayerQuantizeParams_ConvolutionInfo::_internal_set_input_channel(int32_t value) { + _has_bits_[0] |= 0x00000001u; + input_channel_ = value; +} +inline void LayerQuantizeParams_ConvolutionInfo::set_input_channel(int32_t value) { + _internal_set_input_channel(value); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.input_channel) +} + +// required int32 output_channel = 2; +inline bool LayerQuantizeParams_ConvolutionInfo::_internal_has_output_channel() const { + bool value = (_has_bits_[0] & 0x00000002u) != 0; + return value; +} +inline bool LayerQuantizeParams_ConvolutionInfo::has_output_channel() const { + return _internal_has_output_channel(); +} +inline void LayerQuantizeParams_ConvolutionInfo::clear_output_channel() { + output_channel_ = 0; + _has_bits_[0] &= ~0x00000002u; +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::_internal_output_channel() const { + return output_channel_; +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::output_channel() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.output_channel) + return _internal_output_channel(); +} +inline void LayerQuantizeParams_ConvolutionInfo::_internal_set_output_channel(int32_t value) { + _has_bits_[0] |= 0x00000002u; + output_channel_ = value; +} +inline void LayerQuantizeParams_ConvolutionInfo::set_output_channel(int32_t value) { + _internal_set_output_channel(value); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.output_channel) +} + +// repeated int32 kernel_size = 3; +inline int LayerQuantizeParams_ConvolutionInfo::_internal_kernel_size_size() const { + return kernel_size_.size(); +} +inline int LayerQuantizeParams_ConvolutionInfo::kernel_size_size() const { + return _internal_kernel_size_size(); +} +inline void LayerQuantizeParams_ConvolutionInfo::clear_kernel_size() { + kernel_size_.Clear(); +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::_internal_kernel_size(int index) const { + return kernel_size_.Get(index); +} +inline int32_t LayerQuantizeParams_ConvolutionInfo::kernel_size(int index) const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.kernel_size) + return _internal_kernel_size(index); +} +inline void LayerQuantizeParams_ConvolutionInfo::set_kernel_size(int index, int32_t value) { + kernel_size_.Set(index, value); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.kernel_size) +} +inline void LayerQuantizeParams_ConvolutionInfo::_internal_add_kernel_size(int32_t value) { + kernel_size_.Add(value); +} +inline void LayerQuantizeParams_ConvolutionInfo::add_kernel_size(int32_t value) { + _internal_add_kernel_size(value); + // @@protoc_insertion_point(field_add:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.kernel_size) +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >& +LayerQuantizeParams_ConvolutionInfo::_internal_kernel_size() const { + return kernel_size_; +} +inline const ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >& +LayerQuantizeParams_ConvolutionInfo::kernel_size() const { + // @@protoc_insertion_point(field_list:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.kernel_size) + return _internal_kernel_size(); +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >* +LayerQuantizeParams_ConvolutionInfo::_internal_mutable_kernel_size() { + return &kernel_size_; +} +inline ::PROTOBUF_NAMESPACE_ID::RepeatedField< int32_t >* +LayerQuantizeParams_ConvolutionInfo::mutable_kernel_size() { + // @@protoc_insertion_point(field_mutable_list:MNN.Compression.LayerQuantizeParams.ConvolutionInfo.kernel_size) + return _internal_mutable_kernel_size(); +} + +// ------------------------------------------------------------------- + // LayerQuantizeParams // repeated .MNN.Compression.LayerQuantizeParams.ActivationParams input = 1; @@ -3186,7 +3659,7 @@ LayerQuantizeParams::output() const { // optional .MNN.Compression.LayerQuantizeParams.QuantMethod method = 4 [default = QAT]; inline bool LayerQuantizeParams::_internal_has_method() const { - bool value = (_has_bits_[0] & 0x00000002u) != 0; + bool value = (_has_bits_[0] & 0x00000010u) != 0; return value; } inline bool LayerQuantizeParams::has_method() const { @@ -3194,7 +3667,7 @@ inline bool LayerQuantizeParams::has_method() const { } inline void LayerQuantizeParams::clear_method() { method_ = 0; - _has_bits_[0] &= ~0x00000002u; + _has_bits_[0] &= ~0x00000010u; } inline ::MNN::Compression::LayerQuantizeParams_QuantMethod LayerQuantizeParams::_internal_method() const { return static_cast< ::MNN::Compression::LayerQuantizeParams_QuantMethod >(method_); @@ -3205,7 +3678,7 @@ inline ::MNN::Compression::LayerQuantizeParams_QuantMethod LayerQuantizeParams:: } inline void LayerQuantizeParams::_internal_set_method(::MNN::Compression::LayerQuantizeParams_QuantMethod value) { assert(::MNN::Compression::LayerQuantizeParams_QuantMethod_IsValid(value)); - _has_bits_[0] |= 0x00000002u; + _has_bits_[0] |= 0x00000010u; method_ = value; } inline void LayerQuantizeParams::set_method(::MNN::Compression::LayerQuantizeParams_QuantMethod value) { @@ -3215,7 +3688,7 @@ inline void LayerQuantizeParams::set_method(::MNN::Compression::LayerQuantizePar // optional .MNN.Compression.LayerQuantizeParams.WinogradParams wino_params = 5; inline bool LayerQuantizeParams::_internal_has_wino_params() const { - bool value = (_has_bits_[0] & 0x00000001u) != 0; + bool value = (_has_bits_[0] & 0x00000004u) != 0; PROTOBUF_ASSUME(!value || wino_params_ != nullptr); return value; } @@ -3224,7 +3697,7 @@ inline bool LayerQuantizeParams::has_wino_params() const { } inline void LayerQuantizeParams::clear_wino_params() { if (wino_params_ != nullptr) wino_params_->Clear(); - _has_bits_[0] &= ~0x00000001u; + _has_bits_[0] &= ~0x00000004u; } inline const ::MNN::Compression::LayerQuantizeParams_WinogradParams& LayerQuantizeParams::_internal_wino_params() const { const ::MNN::Compression::LayerQuantizeParams_WinogradParams* p = wino_params_; @@ -3242,14 +3715,14 @@ inline void LayerQuantizeParams::unsafe_arena_set_allocated_wino_params( } wino_params_ = wino_params; if (wino_params) { - _has_bits_[0] |= 0x00000001u; + _has_bits_[0] |= 0x00000004u; } else { - _has_bits_[0] &= ~0x00000001u; + _has_bits_[0] &= ~0x00000004u; } // @@protoc_insertion_point(field_unsafe_arena_set_allocated:MNN.Compression.LayerQuantizeParams.wino_params) } inline ::MNN::Compression::LayerQuantizeParams_WinogradParams* LayerQuantizeParams::release_wino_params() { - _has_bits_[0] &= ~0x00000001u; + _has_bits_[0] &= ~0x00000004u; ::MNN::Compression::LayerQuantizeParams_WinogradParams* temp = wino_params_; wino_params_ = nullptr; #ifdef PROTOBUF_FORCE_COPY_IN_RELEASE @@ -3265,13 +3738,13 @@ inline ::MNN::Compression::LayerQuantizeParams_WinogradParams* LayerQuantizePara } inline ::MNN::Compression::LayerQuantizeParams_WinogradParams* LayerQuantizeParams::unsafe_arena_release_wino_params() { // @@protoc_insertion_point(field_release:MNN.Compression.LayerQuantizeParams.wino_params) - _has_bits_[0] &= ~0x00000001u; + _has_bits_[0] &= ~0x00000004u; ::MNN::Compression::LayerQuantizeParams_WinogradParams* temp = wino_params_; wino_params_ = nullptr; return temp; } inline ::MNN::Compression::LayerQuantizeParams_WinogradParams* LayerQuantizeParams::_internal_mutable_wino_params() { - _has_bits_[0] |= 0x00000001u; + _has_bits_[0] |= 0x00000004u; if (wino_params_ == nullptr) { auto* p = CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_WinogradParams>(GetArenaForAllocation()); wino_params_ = p; @@ -3295,14 +3768,242 @@ inline void LayerQuantizeParams::set_allocated_wino_params(::MNN::Compression::L wino_params = ::PROTOBUF_NAMESPACE_ID::internal::GetOwnedMessage( message_arena, wino_params, submessage_arena); } - _has_bits_[0] |= 0x00000001u; + _has_bits_[0] |= 0x00000004u; } else { - _has_bits_[0] &= ~0x00000001u; + _has_bits_[0] &= ~0x00000004u; } wino_params_ = wino_params; // @@protoc_insertion_point(field_set_allocated:MNN.Compression.LayerQuantizeParams.wino_params) } +// optional string op_name = 6; +inline bool LayerQuantizeParams::_internal_has_op_name() const { + bool value = (_has_bits_[0] & 0x00000001u) != 0; + return value; +} +inline bool LayerQuantizeParams::has_op_name() const { + return _internal_has_op_name(); +} +inline void LayerQuantizeParams::clear_op_name() { + op_name_.ClearToEmpty(); + _has_bits_[0] &= ~0x00000001u; +} +inline const std::string& LayerQuantizeParams::op_name() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.op_name) + return _internal_op_name(); +} +template +inline PROTOBUF_ALWAYS_INLINE +void LayerQuantizeParams::set_op_name(ArgT0&& arg0, ArgT... args) { + _has_bits_[0] |= 0x00000001u; + op_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, static_cast(arg0), args..., GetArenaForAllocation()); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.op_name) +} +inline std::string* LayerQuantizeParams::mutable_op_name() { + std::string* _s = _internal_mutable_op_name(); + // @@protoc_insertion_point(field_mutable:MNN.Compression.LayerQuantizeParams.op_name) + return _s; +} +inline const std::string& LayerQuantizeParams::_internal_op_name() const { + return op_name_.Get(); +} +inline void LayerQuantizeParams::_internal_set_op_name(const std::string& value) { + _has_bits_[0] |= 0x00000001u; + op_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, value, GetArenaForAllocation()); +} +inline std::string* LayerQuantizeParams::_internal_mutable_op_name() { + _has_bits_[0] |= 0x00000001u; + return op_name_.Mutable(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, GetArenaForAllocation()); +} +inline std::string* LayerQuantizeParams::release_op_name() { + // @@protoc_insertion_point(field_release:MNN.Compression.LayerQuantizeParams.op_name) + if (!_internal_has_op_name()) { + return nullptr; + } + _has_bits_[0] &= ~0x00000001u; + auto* p = op_name_.ReleaseNonDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), GetArenaForAllocation()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (op_name_.IsDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited())) { + op_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + } +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + return p; +} +inline void LayerQuantizeParams::set_allocated_op_name(std::string* op_name) { + if (op_name != nullptr) { + _has_bits_[0] |= 0x00000001u; + } else { + _has_bits_[0] &= ~0x00000001u; + } + op_name_.SetAllocated(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), op_name, + GetArenaForAllocation()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (op_name_.IsDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited())) { + op_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + } +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + // @@protoc_insertion_point(field_set_allocated:MNN.Compression.LayerQuantizeParams.op_name) +} + +// optional string subgraph_name = 7; +inline bool LayerQuantizeParams::_internal_has_subgraph_name() const { + bool value = (_has_bits_[0] & 0x00000002u) != 0; + return value; +} +inline bool LayerQuantizeParams::has_subgraph_name() const { + return _internal_has_subgraph_name(); +} +inline void LayerQuantizeParams::clear_subgraph_name() { + subgraph_name_.ClearToEmpty(); + _has_bits_[0] &= ~0x00000002u; +} +inline const std::string& LayerQuantizeParams::subgraph_name() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.subgraph_name) + return _internal_subgraph_name(); +} +template +inline PROTOBUF_ALWAYS_INLINE +void LayerQuantizeParams::set_subgraph_name(ArgT0&& arg0, ArgT... args) { + _has_bits_[0] |= 0x00000002u; + subgraph_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, static_cast(arg0), args..., GetArenaForAllocation()); + // @@protoc_insertion_point(field_set:MNN.Compression.LayerQuantizeParams.subgraph_name) +} +inline std::string* LayerQuantizeParams::mutable_subgraph_name() { + std::string* _s = _internal_mutable_subgraph_name(); + // @@protoc_insertion_point(field_mutable:MNN.Compression.LayerQuantizeParams.subgraph_name) + return _s; +} +inline const std::string& LayerQuantizeParams::_internal_subgraph_name() const { + return subgraph_name_.Get(); +} +inline void LayerQuantizeParams::_internal_set_subgraph_name(const std::string& value) { + _has_bits_[0] |= 0x00000002u; + subgraph_name_.Set(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, value, GetArenaForAllocation()); +} +inline std::string* LayerQuantizeParams::_internal_mutable_subgraph_name() { + _has_bits_[0] |= 0x00000002u; + return subgraph_name_.Mutable(::PROTOBUF_NAMESPACE_ID::internal::ArenaStringPtr::EmptyDefault{}, GetArenaForAllocation()); +} +inline std::string* LayerQuantizeParams::release_subgraph_name() { + // @@protoc_insertion_point(field_release:MNN.Compression.LayerQuantizeParams.subgraph_name) + if (!_internal_has_subgraph_name()) { + return nullptr; + } + _has_bits_[0] &= ~0x00000002u; + auto* p = subgraph_name_.ReleaseNonDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), GetArenaForAllocation()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (subgraph_name_.IsDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited())) { + subgraph_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + } +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + return p; +} +inline void LayerQuantizeParams::set_allocated_subgraph_name(std::string* subgraph_name) { + if (subgraph_name != nullptr) { + _has_bits_[0] |= 0x00000002u; + } else { + _has_bits_[0] &= ~0x00000002u; + } + subgraph_name_.SetAllocated(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), subgraph_name, + GetArenaForAllocation()); +#ifdef PROTOBUF_FORCE_COPY_DEFAULT_STRING + if (subgraph_name_.IsDefault(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited())) { + subgraph_name_.Set(&::PROTOBUF_NAMESPACE_ID::internal::GetEmptyStringAlreadyInited(), "", GetArenaForAllocation()); + } +#endif // PROTOBUF_FORCE_COPY_DEFAULT_STRING + // @@protoc_insertion_point(field_set_allocated:MNN.Compression.LayerQuantizeParams.subgraph_name) +} + +// optional .MNN.Compression.LayerQuantizeParams.ConvolutionInfo conv = 8; +inline bool LayerQuantizeParams::_internal_has_conv() const { + bool value = (_has_bits_[0] & 0x00000008u) != 0; + PROTOBUF_ASSUME(!value || conv_ != nullptr); + return value; +} +inline bool LayerQuantizeParams::has_conv() const { + return _internal_has_conv(); +} +inline void LayerQuantizeParams::clear_conv() { + if (conv_ != nullptr) conv_->Clear(); + _has_bits_[0] &= ~0x00000008u; +} +inline const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& LayerQuantizeParams::_internal_conv() const { + const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* p = conv_; + return p != nullptr ? *p : reinterpret_cast( + ::MNN::Compression::_LayerQuantizeParams_ConvolutionInfo_default_instance_); +} +inline const ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo& LayerQuantizeParams::conv() const { + // @@protoc_insertion_point(field_get:MNN.Compression.LayerQuantizeParams.conv) + return _internal_conv(); +} +inline void LayerQuantizeParams::unsafe_arena_set_allocated_conv( + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* conv) { + if (GetArenaForAllocation() == nullptr) { + delete reinterpret_cast<::PROTOBUF_NAMESPACE_ID::MessageLite*>(conv_); + } + conv_ = conv; + if (conv) { + _has_bits_[0] |= 0x00000008u; + } else { + _has_bits_[0] &= ~0x00000008u; + } + // @@protoc_insertion_point(field_unsafe_arena_set_allocated:MNN.Compression.LayerQuantizeParams.conv) +} +inline ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* LayerQuantizeParams::release_conv() { + _has_bits_[0] &= ~0x00000008u; + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* temp = conv_; + conv_ = nullptr; +#ifdef PROTOBUF_FORCE_COPY_IN_RELEASE + auto* old = reinterpret_cast<::PROTOBUF_NAMESPACE_ID::MessageLite*>(temp); + temp = ::PROTOBUF_NAMESPACE_ID::internal::DuplicateIfNonNull(temp); + if (GetArenaForAllocation() == nullptr) { delete old; } +#else // PROTOBUF_FORCE_COPY_IN_RELEASE + if (GetArenaForAllocation() != nullptr) { + temp = ::PROTOBUF_NAMESPACE_ID::internal::DuplicateIfNonNull(temp); + } +#endif // !PROTOBUF_FORCE_COPY_IN_RELEASE + return temp; +} +inline ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* LayerQuantizeParams::unsafe_arena_release_conv() { + // @@protoc_insertion_point(field_release:MNN.Compression.LayerQuantizeParams.conv) + _has_bits_[0] &= ~0x00000008u; + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* temp = conv_; + conv_ = nullptr; + return temp; +} +inline ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* LayerQuantizeParams::_internal_mutable_conv() { + _has_bits_[0] |= 0x00000008u; + if (conv_ == nullptr) { + auto* p = CreateMaybeMessage<::MNN::Compression::LayerQuantizeParams_ConvolutionInfo>(GetArenaForAllocation()); + conv_ = p; + } + return conv_; +} +inline ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* LayerQuantizeParams::mutable_conv() { + ::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* _msg = _internal_mutable_conv(); + // @@protoc_insertion_point(field_mutable:MNN.Compression.LayerQuantizeParams.conv) + return _msg; +} +inline void LayerQuantizeParams::set_allocated_conv(::MNN::Compression::LayerQuantizeParams_ConvolutionInfo* conv) { + ::PROTOBUF_NAMESPACE_ID::Arena* message_arena = GetArenaForAllocation(); + if (message_arena == nullptr) { + delete conv_; + } + if (conv) { + ::PROTOBUF_NAMESPACE_ID::Arena* submessage_arena = + ::PROTOBUF_NAMESPACE_ID::Arena::InternalHelper<::MNN::Compression::LayerQuantizeParams_ConvolutionInfo>::GetOwningArena(conv); + if (message_arena != submessage_arena) { + conv = ::PROTOBUF_NAMESPACE_ID::internal::GetOwnedMessage( + message_arena, conv, submessage_arena); + } + _has_bits_[0] |= 0x00000008u; + } else { + _has_bits_[0] &= ~0x00000008u; + } + conv_ = conv; + // @@protoc_insertion_point(field_set_allocated:MNN.Compression.LayerQuantizeParams.conv) +} + // ------------------------------------------------------------------- // LevelPrunerParams @@ -4351,6 +5052,34 @@ inline void Pipeline::set_allocated_mnn_uuid(std::string* mnn_uuid) { // @@protoc_insertion_point(field_set_allocated:MNN.Compression.Pipeline.mnn_uuid) } +// optional bool for_guide = 4 [default = false]; +inline bool Pipeline::_internal_has_for_guide() const { + bool value = (_has_bits_[0] & 0x00000004u) != 0; + return value; +} +inline bool Pipeline::has_for_guide() const { + return _internal_has_for_guide(); +} +inline void Pipeline::clear_for_guide() { + for_guide_ = false; + _has_bits_[0] &= ~0x00000004u; +} +inline bool Pipeline::_internal_for_guide() const { + return for_guide_; +} +inline bool Pipeline::for_guide() const { + // @@protoc_insertion_point(field_get:MNN.Compression.Pipeline.for_guide) + return _internal_for_guide(); +} +inline void Pipeline::_internal_set_for_guide(bool value) { + _has_bits_[0] |= 0x00000004u; + for_guide_ = value; +} +inline void Pipeline::set_for_guide(bool value) { + _internal_set_for_guide(value); + // @@protoc_insertion_point(field_set:MNN.Compression.Pipeline.for_guide) +} + #ifdef __GNUC__ #pragma GCC diagnostic pop #endif // __GNUC__ @@ -4372,6 +5101,8 @@ inline void Pipeline::set_allocated_mnn_uuid(std::string* mnn_uuid) { // ------------------------------------------------------------------- +// ------------------------------------------------------------------- + // @@protoc_insertion_point(namespace_scope) diff --git a/tools/converter/source/onnx/CastOnnx.cpp b/tools/converter/source/onnx/CastOnnx.cpp index 67bf756dd..9f8b037c1 100644 --- a/tools/converter/source/onnx/CastOnnx.cpp +++ b/tools/converter/source/onnx/CastOnnx.cpp @@ -35,6 +35,9 @@ void CastOnnx::run(MNN::OpT *dstOp, const onnx::NodeProto *onnxNode, } castParam->dstT = onnxOpConverter::convertDataType(castTo); + if (castTo == onnx::TensorProto_DataType_FLOAT16) { + castParam->dstT = MNN::DataType_DT_FLOAT; + } dstOp->main.value = castParam.release(); } diff --git a/tools/converter/source/onnx/EluOnnx.cpp b/tools/converter/source/onnx/EluOnnx.cpp index 700aea0a4..8bed80808 100644 --- a/tools/converter/source/onnx/EluOnnx.cpp +++ b/tools/converter/source/onnx/EluOnnx.cpp @@ -46,7 +46,7 @@ void SEluOnnx::run(MNN::OpT *dstOp, const onnx::NodeProto *onnxNode, OnnxScope* auto seluParam = new MNN::SeluT; - float alpha = 1.67326, gamma = 1.0507; + float alpha = 1.6732632423543772848170429916717f, gamma = 1.0507009873554804934193349852946f; for (int i = 0; i < onnxNode->attribute_size(); ++i) { const auto &attributeProto = onnxNode->attribute(i); const auto &attributeName = attributeProto.name(); diff --git a/tools/converter/source/optimizer/merge/Conv1dSqueezeMove.cpp b/tools/converter/source/optimizer/merge/Conv1dSqueezeMove.cpp index ec0d739c7..888285c1b 100644 --- a/tools/converter/source/optimizer/merge/Conv1dSqueezeMove.cpp +++ b/tools/converter/source/optimizer/merge/Conv1dSqueezeMove.cpp @@ -10,7 +10,7 @@ #include "MNN_generated.h" #include "MergeHelpers.hpp" #include "cli.hpp" -#include "commonKit.hpp" +#include "../../common/CommonUtils.hpp" #include "MNN_compression.pb.h" #include @@ -36,19 +36,7 @@ auto getConv1dPostCase = [](EXPRP expr) { auto gConverterConfig = Global::Get(); std::string compressFileName = gConverterConfig->compressionParamsFile; - Compression::Pipeline proto; - if (compressFileName != "") { - std::string jsonSuffix = "json"; - std::string suffix = compressFileName.substr(compressFileName.find_last_of('.') + 1); - if (suffix.compare(jsonSuffix) != 0) { - std::fstream input(compressFileName.c_str(), std::ios::in | std::ios::binary); - if (!proto.ParseFromIstream(&input)) { - MNN_ERROR("Failed to parse compression pipeline proto.\n"); - } - } else { - CommonKit::json2protobuf(compressFileName.c_str(), nullptr, &proto); - } - } + auto& proto = gConverterConfig->compressInfo->proto; auto findQuantParameters = [&](Compression::Pipeline& proto, std::string outputTensorName) { for (const auto& algo : proto.algo()) { diff --git a/tools/converter/source/optimizer/merge/ConvBNReluFuseToConvInt8.cpp b/tools/converter/source/optimizer/merge/ConvBNReluFuseToConvInt8.cpp index eb4496376..948ab2b0e 100644 --- a/tools/converter/source/optimizer/merge/ConvBNReluFuseToConvInt8.cpp +++ b/tools/converter/source/optimizer/merge/ConvBNReluFuseToConvInt8.cpp @@ -12,7 +12,7 @@ #include "MNN_generated.h" #include "MNN_compression.pb.h" #include "cli.hpp" -#include "commonKit.hpp" +#include "../../common/CommonUtils.hpp" #include namespace MNN { @@ -57,19 +57,7 @@ static auto gRegister = []() { auto transform = [](EXPRP expr) { auto gConverterConfig = Global::Get(); std::string compressFileName = gConverterConfig->compressionParamsFile; - Compression::Pipeline proto; - if (compressFileName != "") { - std::string jsonSuffix = "json"; - std::string suffix = compressFileName.substr(compressFileName.find_last_of('.') + 1); - if (suffix.compare(jsonSuffix) != 0) { - std::fstream input(compressFileName.c_str(), std::ios::in | std::ios::binary); - if (!proto.ParseFromIstream(&input)) { - MNN_ERROR("Failed to parse compression pipeline proto.\n"); - } - } else { - CommonKit::json2protobuf(compressFileName.c_str(), nullptr, &proto); - } - } + auto& proto = gConverterConfig->compressInfo->proto; auto convert1 = expr->inputs()[0]; auto convert1_expr = convert1->expr().first; auto convOutput = convert1_expr->inputs()[0]; diff --git a/tools/converter/source/optimizer/merge/Convolution3DTurn2D.cpp b/tools/converter/source/optimizer/merge/Convolution3DTurn2D.cpp new file mode 100644 index 000000000..162fa0943 --- /dev/null +++ b/tools/converter/source/optimizer/merge/Convolution3DTurn2D.cpp @@ -0,0 +1,145 @@ +// +// ConvBiasAdd.cpp +// MNNConverter +// +// Created by MNN on 2019/09/16. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "../TemplateMerge.hpp" +#include +#include "MNN_generated.h" + +namespace MNN { +namespace Express { +static EXPRP _transformConv3DWithConv2D(EXPRP expr) { + std::unique_ptr originOp(expr->get()->UnPack()); + auto common = originOp->main.AsConvolution3D()->common.get(); + auto weightPtr = originOp->main.AsConvolution3D()->weight.data(); + auto biasDataPtr = originOp->main.AsConvolution3D()->bias.data(); + auto input = expr->inputs()[0]; + // Im2Col + auto one = _Unsqueeze(_Scalar(1), {0}); + auto negone = _Unsqueeze(_Scalar(-1), {0}); + auto sx = _Shape(input, true); + auto kernelD = common->kernels[0]; + auto kernelH = common->kernels[1]; + auto kernelW = common->kernels[2]; + auto inputChannel = common->inputCount; + auto outputChannel = common->outputCount; + auto kdv = _Unsqueeze(_Scalar(kernelD), {0}); + auto w = _Slice(sx, _Unsqueeze(_Scalar(4), {0}), one); + auto h = _Slice(sx, _Unsqueeze(_Scalar(3), {0}), one); + auto d = _Slice(sx, _Unsqueeze(_Scalar(2), {0}), one); + auto ic = _Slice(sx, _Unsqueeze(_Scalar(1), {0}), one); + auto b = _Slice(sx, _Unsqueeze(_Scalar(0), {0}), one); + auto im2colinput = _Reshape(input, _Concat({one, b*ic, d, w*h}, 0)); + auto im2coloutput = _Im2Col(im2colinput, {1, kernelD}, {1, common->dilates[0]}, {common->pads[0], 0, common->pads[3], 0}, {1, common->strides[0]}); + // Reshape and Unpack + std::vector convInputs; + { + // use -1 to compute od + auto value = _Reshape(im2coloutput, _Concat({b, ic * kdv, negone, h, w}, 0)); + // Merge od and batch + value = _Transpose(value, {0, 2, 1, 3, 4}); + value = _Reshape(value, _Concat({negone, ic, kdv, h, w}, 0)); + std::unique_ptr op(new OpT); + op->type = OpType_Unpack; + auto axisParam = new AxisT; + axisParam->axis = 2; + op->main.type = OpParameter_Axis; + op->main.value = axisParam; + EXPRP packexpr = Expr::create(std::move(op), {value}, kernelD); + convInputs.resize(kernelD); + for (int i=0; i convOutputs(kernelD); + for (int kd=0; kd op(new OpT); + op->type = OpType_Convolution; + op->main.type = OpParameter_Convolution2D; + op->main.value = new Convolution2DT; + auto conv2D = op->main.AsConvolution2D(); + conv2D->common.reset(new Convolution2DCommonT); + // Copy common + auto common2d = conv2D->common.get(); + common2d->inputCount = common->inputCount; + common2d->outputCount = common->outputCount; + common2d->hasOutputShape = common->hasOutputShape; + common2d->dilateX = common->dilates[2]; + common2d->dilateY = common->dilates[1]; + common2d->strideX = common->strides[2]; + common2d->strideY = common->strides[1]; + common2d->pads = {common->pads[1], common->pads[2], common->pads[4], common->pads[5]}; + common2d->kernelX = common->kernels[2]; + common2d->kernelY = common->kernels[1]; + common2d->group = common->group; + common2d->padMode = common->padMode; + // Split Weight + int weightGroupSize = inputChannel*outputChannel / common->group; + conv2D->weight.resize(kernelH * kernelW * weightGroupSize); + for (int i=0; iweight.data() + kernelH * kernelW * i, weightPtr + i * kernelD * kernelH * kernelW + kd * kernelH * kernelW, kernelH * kernelW * sizeof(float)); + } + conv2D->bias.resize(outputChannel); + ::memset(conv2D->bias.data(), 0, outputChannel * sizeof(float)); + if (kd == kernelD - 1) { + ::memcpy(conv2D->bias.data(), biasDataPtr, outputChannel * sizeof(float)); + } + auto convExpr = Expr::create(std::move(op), {convInputs[kd]}, 1); + convOutputs[kd] = Variable::create(convExpr); + convOutputs[kd]->setName(expr->name() + "__" + std::to_string(kd)); + } + VARP output; + if (kernelD > 1) { + std::unique_ptr op(new OpT); + op->type = OpType_Eltwise; + op->main.type = OpParameter_Eltwise; + op->main.value = new EltwiseT; + op->main.AsEltwise()->type = EltwiseType_SUM; + auto eltExpr = Expr::create(std::move(op), convOutputs); + output = Variable::create(eltExpr); + } else { + output = convOutputs[0]; + } + if (common->relu) { + output = _Relu(output); + } else if (common->relu6) { + output = _Relu6(output); + } + + // Split od and batch + sx = _Shape(output, true); + w = _Slice(sx, _Unsqueeze(_Scalar(3), {0}), one); + h = _Slice(sx, _Unsqueeze(_Scalar(2), {0}), one); + auto oc = _Unsqueeze(_Scalar(outputChannel), {0}); + output = _Reshape(output, _Concat({b, negone, oc, h, w}, 0)); + output = _Transpose(output, {0, 2, 1, 3, 4}); + output->expr().first->setName(expr->name()); + return output->expr().first; +} + +static auto gRegister = []() { + auto compare = [](EXPRP expr) { + if (nullptr == expr->get()) { + return false; + } + if (expr->get()->type() != OpType_Convolution3D) { + return false; + } + return expr->get()->type() == OpType_Convolution3D && expr->inputs().size() == 1; + }; + auto modify = [](EXPRP expr) { + auto newExpr = _transformConv3DWithConv2D(expr); + newExpr->setName(expr->name()); + Expr::replace(expr, newExpr); + return true; + }; + TemplateMerge::getInstance("Merge").insertTemplate("Convolution3DTurn2D", compare, modify, PASS_PRIORITY_MIDDLE); + return true; +}(); +} +} // namespace MNN diff --git a/tools/converter/source/optimizer/merge/MergeHelpers.cpp b/tools/converter/source/optimizer/merge/MergeHelpers.cpp index 1b4cdcdaa..f0c21e9a7 100644 --- a/tools/converter/source/optimizer/merge/MergeHelpers.cpp +++ b/tools/converter/source/optimizer/merge/MergeHelpers.cpp @@ -9,7 +9,6 @@ #include #include -#include "../../common/Common.hpp" #include "MNN_generated.h" #include "MergeHelpers.hpp" @@ -17,6 +16,18 @@ using namespace MNN::Express; namespace MNN { namespace helpers { +static MNN_DATA_FORMAT convertFormat(Express::Dimensionformat format) { + switch (format) { + case Express::NCHW: + return MNN_DATA_FORMAT_NCHW; + case Express::NHWC: + return MNN_DATA_FORMAT_NHWC; + case Express::NC4HW4: + return MNN_DATA_FORMAT_NC4HW4; + default: + return MNN_DATA_FORMAT_UNKNOWN; + } +} bool IsConstant(EXPRP expr) { const Op* op = expr->get(); diff --git a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp index f902a64f0..e2356c7c9 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp @@ -283,34 +283,74 @@ class OnnxLayerNormTransform : public OnnxExtraManager::Transform { } } } - auto axisVar = _Scalar(axis); - // Add negative protect, may decrease performance - auto rankVar = _Rank(inputs[0]); - axisVar = _Mod(axisVar + rankVar, rankVar); - auto reduceAxis = _Range(axisVar, rankVar, _Scalar(1)); - auto mean = _ReduceMeanMutable(input, reduceAxis, true); - auto sub = input - mean; - auto normal = _Rsqrt(_ReduceMeanMutable(_Square(sub), reduceAxis, true) + _Scalar(eps)); - auto y = sub * normal * inputs[1]; - if (inputs.size() > 2) { - y = y + inputs[2]; - } - std::vector identityOutputs = {y}; if (expr->outputSize() > 1) { - identityOutputs.emplace_back(mean); + auto axisVar = _Scalar(axis); + // Add negative protect, may decrease performance + auto rankVar = _Rank(inputs[0]); + axisVar = _Mod(axisVar + rankVar, rankVar); + auto reduceAxis = _Range(axisVar, rankVar, _Scalar(1)); + auto mean = _ReduceMeanMutable(input, reduceAxis, true); + auto sub = input - mean; + auto normal = _Rsqrt(_ReduceMeanMutable(_Square(sub), reduceAxis, true) + _Scalar(eps)); + auto y = sub * normal * inputs[1]; + if (inputs.size() > 2) { + y = y + inputs[2]; + } + std::vector identityOutputs = {y}; + if (expr->outputSize() > 1) { + identityOutputs.emplace_back(mean); + } + if (expr->outputSize() > 2) { + identityOutputs.emplace_back(normal); + } + std::unique_ptr copyOp(new OpT); + copyOp->type = OpType_Identity; + auto resultExpr = Expr::create(copyOp.get(), identityOutputs, identityOutputs.size()); + resultExpr->setName(expr->name()); + for (int i=0; ioutputSize(); ++i) { + auto var = MNN::Express::Variable::create(resultExpr, i); + var->setName(expr->outputName(i)); + } + return resultExpr; + } + std::shared_ptr layernorm(new MNN::OpT); + layernorm->type = OpType_LayerNorm; + layernorm->main.value = new LayerNormT; + layernorm->main.type = OpParameter_LayerNorm; + auto param = layernorm->main.AsLayerNorm(); + param->axis = {axis}; + param->epsilon = eps; + const float* scalePtr = nullptr; + const float* biasPtr = nullptr; + if (inputs.size() > 1) { + scalePtr = inputs[1]->readMap(); } - if (expr->outputSize() > 2) { - identityOutputs.emplace_back(normal); + if (nullptr != scalePtr) { + param->gamma.resize(inputs[1]->getInfo()->size); + ::memcpy(param->gamma.data(), scalePtr, param->gamma.size() * sizeof(float)); + param->beta.resize(inputs[1]->getInfo()->size); + ::memset(param->beta.data(), 0, param->gamma.size() * sizeof(float)); } - std::unique_ptr copyOp(new OpT); - copyOp->type = OpType_Identity; - auto resultExpr = Expr::create(copyOp.get(), identityOutputs, identityOutputs.size()); - resultExpr->setName(expr->name()); - for (int i=0; ioutputSize(); ++i) { - auto var = MNN::Express::Variable::create(resultExpr, i); - var->setName(expr->outputName(i)); + if (inputs.size() > 2 && nullptr != scalePtr) { + biasPtr = inputs[2]->readMap(); + } + if (nullptr != biasPtr) { + ::memcpy(param->beta.data(), biasPtr, param->gamma.size() * sizeof(float)); + } + auto layerexpr = Expr::create(layernorm.get(), {input}); + auto output = Variable::create(layerexpr); + if (scalePtr == nullptr) { + if (inputs.size() > 1) { + output = output * inputs[1]; + } + } + if (biasPtr == nullptr) { + if (inputs.size() > 2) { + output = output + inputs[2]; + } } - return resultExpr; + output->setName(expr->name()); + return output->expr().first; } }; diff --git a/tools/converter/source/optimizer/onnxextra/OnnxConvolutionMerge.cpp b/tools/converter/source/optimizer/onnxextra/OnnxConvolutionMerge.cpp index ec43deee9..5e875a277 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxConvolutionMerge.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxConvolutionMerge.cpp @@ -34,8 +34,9 @@ static EXPRP _transformConv3D(EXPRP expr) { auto weight = inputs[1]; auto weightInfo = weight->getInfo(); - if (nullptr == weightInfo) { - MNN_ERROR("Convolution3D should know weight shape infromation!\n"); + auto weightPtr = weight->readMap(); + if (nullptr == weightInfo || nullptr == weightPtr) { + MNN_ERROR("Convolution3D should has constant weight!\n"); return nullptr; } auto& weightShape = weightInfo->dim; @@ -52,19 +53,7 @@ static EXPRP _transformConv3D(EXPRP expr) { co = weightShape[1]; ci = weightShape[0]; } - std::unique_ptr conv3d(new MNN::Convolution3DT); - const float* weightDataPtr = weight->readMap(); - conv3d->weight.resize(weightInfo->size); - ::memcpy(conv3d->weight.data(), weightDataPtr, weightInfo->size * sizeof(float)); - conv3d->bias.resize(co); - std::fill(conv3d->bias.begin(), conv3d->bias.end(), 0.0f); - if (inputSize == 3) { - auto biasDataPtr = inputs[2]->readMap(); - ::memcpy(conv3d->bias.data(), biasDataPtr, co * sizeof(float)); - } - - conv3d->common.reset(new MNN::Convolution3DCommonT); - auto common = conv3d->common.get(); + std::unique_ptr common(new MNN::Convolution3DCommonT); common->pads = {0, 0, 0, 0, 0, 0}; common->dilates = {1, 1, 1}; common->kernels = {1, 1, 1}; @@ -110,7 +99,22 @@ static EXPRP _transformConv3D(EXPRP expr) { common->outputCount = co; common->inputCount = ci * common->group; // conv set inputCount to be ci, dw to be group } - common->kernels = std::vector({depth, kh, kw}); + common->kernels = std::vector({depth, kh, kw}); + std::unique_ptr conv3d(new MNN::Convolution3DT); + conv3d->weight.resize(weightInfo->size); + ::memcpy(conv3d->weight.data(), weightPtr, weightInfo->size * sizeof(float)); + conv3d->bias.resize(co); + std::fill(conv3d->bias.begin(), conv3d->bias.end(), 0.0f); + bool needExtraBias = false; + if (inputSize == 3) { + auto biasDataPtr = inputs[2]->readMap(); + if (nullptr != biasDataPtr) { + ::memcpy(conv3d->bias.data(), biasDataPtr, co * sizeof(float)); + } else { + needExtraBias = true; + } + } + conv3d->common.reset(common.release());; std::unique_ptr newOp(new OpT); newOp->name = expr->name(); @@ -119,6 +123,10 @@ static EXPRP _transformConv3D(EXPRP expr) { newOp->main.value = conv3d.release(); auto newExpr = Expr::create(newOp.get(), {inputs[0]}, 1); + if (needExtraBias) { + auto newVar = _Add(Variable::create(newExpr), inputs[2]); + newExpr = newVar->expr().first; + } return newExpr; } diff --git a/tools/converter/source/tensorflow/TfModelOptimizer.cpp b/tools/converter/source/tensorflow/TfModelOptimizer.cpp deleted file mode 100644 index b360a330c..000000000 --- a/tools/converter/source/tensorflow/TfModelOptimizer.cpp +++ /dev/null @@ -1,689 +0,0 @@ -// -// TfModelOptimizer.cpp -// MNNConverter -// -// Created by MNN on 2019/01/31. -// Copyright © 2018, Alibaba Group Holding Limited -// - -#include -#include -#include "TfUtils.hpp" -#include "logkit.h" -#include - -namespace TFModelOptimizer { -int FoldMoments(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def) { - std::map inputs_to_rename; - GraphDef replaced_graph_def; - ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"Mean", - { - {"Mul", - { - {"Sub", - { - {"*"}, - {"Mean", - { - {"*"}, - {"Const"} - } - } - } - }, - {"*"} - } - }, - {"Const"} - } - }, // clang-format on - [&inputs_to_rename](const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, std::vector* new_nodes) { - // Find all the nodes we expect in the subgraph. - const NodeDef& variance_mean_node = match.node; - const NodeDef& mul_node = match.inputs[0].node; - const NodeDef& sub_node = match.inputs[0].inputs[0].node; - const NodeDef& mean_node = match.inputs[0].inputs[0].inputs[1].node; - const NodeDef& mean_node_input_node = match.inputs[0].inputs[0].inputs[1].inputs[0].node; - const NodeDef& mean_reduction_indices_node = match.inputs[0].inputs[0].inputs[1].inputs[1].node; - CHECK_EQ(sub_node.input(0), mean_node.input(0)) << "sub and mean should have the same input!"; - - NodeDef moments_node; - moments_node.set_op("Moments"); - moments_node.set_name(mean_node.name() + "__moments"); - SetNodeAttr("T", DT_FLOAT, &moments_node); - CopyNodeAttr(mean_node, "keep_dims", "keep_dims", &moments_node); - CopyNodeAttr(mean_node, "Tidx", "Tidx", &moments_node); - - NodeDef moments_axes_node; - moments_axes_node.set_op("Const"); - moments_axes_node.set_name(mean_node.name() + "_axes"); - CopyNodeAttr(mean_reduction_indices_node, "dtype", "dtype", &moments_axes_node); - CopyNodeAttr(mean_reduction_indices_node, "value", "value", &moments_axes_node); - - AddNodeInput(mean_node.input(0), &moments_node); - AddNodeInput(moments_axes_node.name(), &moments_node); - - inputs_to_rename[mean_node.name()] = moments_node.name() + ":0"; - inputs_to_rename[variance_mean_node.name()] = moments_node.name() + ":1"; - - new_nodes->push_back(moments_node); - new_nodes->push_back(moments_axes_node); - new_nodes->push_back(mean_node_input_node); - return 0; - }, - &replaced_graph_def); - - // Change the input_name of the nodes that use mean and variance. - RenameNodeInputs(replaced_graph_def, inputs_to_rename, std::unordered_set(), output_graph_def); - - return 0; -} - -REGISTER_GRAPH_TRANSFORM("fold_moments", FoldMoments); - -// Fold batchnorm which is unfolded into series of algebraic expressions -// For example:(x - mean) * rsqrt(variance + epsilon) * gamma + beta -// gamma and beta are Const nodes, mean and variance may be Const nodes, or come -// from the outputs of nn.moments() -int FoldBatchNormsAlgebraic(const GraphDef& input_graph_def, const TransformFuncContext& context, - GraphDef* output_graph_def) { - std::map inputs_to_rename; - GraphDef replaced_graph_def; - ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"Add", - { - {"Mul", // mul_1-->x * (rsqrt(variance + epsilon) * gamma) - { - {"*"}, - {"Mul", // mul-->rsqrt(variance + epsilon) * gamma - { - {"Rsqrt", - { - {"Add", // add-->variance + epsilon - { - {"*"}, // variance node - {"Const"} // epsilon - } - } - } - }, - {"Const"} // gamma const value - } - } - } - }, - {"Sub", // sub-->beta - (rsqrt(variance + epsilon) * gamma) * mean - { - {"Const"}, // beta const value - {"Mul", // mul_2-->(rsqrt(variance + epsilon) * gamma) * mean - { - {"*"}, // mean node - {"Mul"} // mul - } - } - } - } - } - }, // clang-format on - [&inputs_to_rename](const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, std::vector* new_nodes) { - // Find all the nodes we expect in the subgraph. - const NodeDef& add_node = match.node; - const NodeDef& mul1_node = match.inputs[0].node; - const NodeDef& sub_node = match.inputs[1].node; - const NodeDef& mul1_input0_node = match.inputs[0].inputs[0].node; - const NodeDef& mul_node = match.inputs[0].inputs[1].node; - const NodeDef& add_epsilon_node = match.inputs[0].inputs[1].inputs[0].inputs[0].node; - const NodeDef& epsilon_node = match.inputs[0].inputs[1].inputs[0].inputs[0].inputs[1].node; - const NodeDef& gamma_node = match.inputs[0].inputs[1].inputs[1].node; - const NodeDef& beta_node = match.inputs[1].inputs[0].node; - const NodeDef& mul2_node = match.inputs[1].inputs[1].node; - const NodeDef& mul_node_alias = match.inputs[1].inputs[1].inputs[1].node; - - const NodeDef& mean_node = match.inputs[1].inputs[1].inputs[0].node; - const NodeDef& variance_node = match.inputs[0].inputs[1].inputs[0].inputs[0].inputs[0].node; - CHECK_EQ(mul_node.name(), mul_node_alias.name()) << "Sub graph not matched!"; - - CHECK_EQ("Const", epsilon_node.op()) << "Sub graph not matched!"; - CHECK_EQ("Const", gamma_node.op()) << "You Should Apply remove_nodes(op=Identity) first!"; - CHECK_EQ("Const", beta_node.op()) << "You Should Apply remove_nodes(op=Identity) first!"; - - NodeDef instance_norms_node; - if (mean_node.op() == "Const" && variance_node.op() == "Const") { - instance_norms_node.set_op("FusedBatchNorm"); - instance_norms_node.set_name(add_node.name() + "__FusedBatchNorm"); - } else { - instance_norms_node.set_op("InstanceNorm"); - instance_norms_node.set_name(add_node.name() + "__InstanceNorm"); - } - SetNodeAttr("T", DT_FLOAT, &instance_norms_node); - // CopyNodeAttr(epsilon_node, "value", "epsilon", &instance_norms_node); - float epsilon = 0.001; - tensorflow::AttrValue value; - if (find_attr_value(&epsilon_node, "value", value)) { - epsilon = value.tensor().float_val(0); - } - SetNodeAttr("epsilon", epsilon, &instance_norms_node); - AddNodeInput(mul1_input0_node.name(), &instance_norms_node); - AddNodeInput(gamma_node.name(), &instance_norms_node); - AddNodeInput(beta_node.name(), &instance_norms_node); - AddNodeInput(mul2_node.input(0), &instance_norms_node); - AddNodeInput(add_epsilon_node.input(0), &instance_norms_node); - - new_nodes->push_back(instance_norms_node); - new_nodes->push_back(gamma_node); - new_nodes->push_back(beta_node); - new_nodes->push_back(mean_node); - new_nodes->push_back(variance_node); - new_nodes->push_back(mul1_input0_node); - - inputs_to_rename[add_node.name()] = instance_norms_node.name(); - return 0; - }, - &replaced_graph_def); - - // Chang the input_name which use nodes in this sub graph - RenameNodeInputs(replaced_graph_def, inputs_to_rename, std::unordered_set(), output_graph_def); - return 0; -} - -REGISTER_GRAPH_TRANSFORM("fold_batch_norms_algebraic", FoldBatchNormsAlgebraic); - -// Deletes any specified types of nodes, unless they're necessary for the -// graph's inputs or outputs. -int RemoveNodes(const GraphDef& input_graph_def, const TransformFuncContext& context, GraphDef* output_graph_def) { - if (!context.params.count("op")) { - LOG(FATAL) << "remove_nodes expects at least one 'op' argument, e.g. remove_nodes(op=Identity)"; - } - int32_t max_inputs = 1; - - // Make sure we don't get rid of any nodes used as graph inputs or outputs. - std::set required_nodes; - for (const std::string& input : context.input_names) { - required_nodes.insert(NodeNameFromInput(input)); - } - for (const std::string& output : context.output_names) { - required_nodes.insert(NodeNameFromInput(output)); - } - - std::vector ops_to_remove = context.params.at("op"); - GraphDef current_graph_def = input_graph_def; - for (const std::string& op : ops_to_remove) { - for (int num_inputs = 1; num_inputs <= max_inputs; ++num_inputs) { - // Look for a variable number of inputs. - OpTypePattern pattern = {op}; - pattern.inputs.resize(num_inputs); - for (int i = 0; i < num_inputs; ++i) { - pattern.inputs[i] = {"*"}; - } - // Keep looking for nodes to remove until there are no more changes. - bool any_nodes_removed; - do { - any_nodes_removed = false; - std::map inputs_to_rename; - GraphDef replaced_graph_def; - ReplaceMatchingOpTypes( - current_graph_def, pattern, - [&inputs_to_rename, &required_nodes, &any_nodes_removed]( - const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, std::vector* new_nodes) { - const NodeDef& replace_node = match.node; - // If this node is needed in the inputs or outputs don't replace - // it. - if (required_nodes.count(replace_node.name())) { - LOG(INFO) << "Skipping replacement for " << replace_node.name(); - CopyOriginalMatch(match, new_nodes); - return 0; - } - const NodeDef& input_node = match.inputs[0].node; - std::string target_name = input_node.name(); - for (const std::string& input : replace_node.input()) { - if (!input.compare(0, target_name.size(), target_name)) { - if (input.size() == target_name.size() || input[target_name.size()] == ':') { - target_name = input; - break; - } - } - } - inputs_to_rename[replace_node.name()] = target_name; - inputs_to_rename["^" + replace_node.name()] = "^" + input_node.name(); - new_nodes->push_back(input_node); - any_nodes_removed = true; - return 0; - }, - &replaced_graph_def); - // Make sure all references to removed nodes now point to their inputs. - RenameNodeInputs(replaced_graph_def, inputs_to_rename, std::unordered_set(), - ¤t_graph_def); - } while (any_nodes_removed); - } - } - *output_graph_def = current_graph_def; - return 0; -} - -REGISTER_GRAPH_TRANSFORM("remove_nodes", RemoveNodes); - -int ResolveRNNGRUCell(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def) { - // clang-format off - const OpTypePattern gru_cell_pattern = - {"Add", // Cell State at time (t) - { - {"Mul", - { - {"Split", // the same node as below the Split node, ref: r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) - { - {"Const"}, - {"Sigmoid", - { - {"BiasAdd", - { - {"MatMul", - { - {"ConcatV2|Concat", - { - {"*"}, // inputs at time (t) - {"*"}, // state - {"Const"} // axis - } - }, - {"Const"} - } - }, - {"Const"} - } - } - } - } - } - }, - {"Add"} // Cell State at time (t - 1) - } - }, - {"Mul", - { - {"Sub", - { - {"Const"}, - {"Split"} - } - }, - {"Tanh", - { - {"BiasAdd", - { - {"MatMul", - { - {"ConcatV2|Concat", - { - {"*"}, // inputs at time (t) - {"*"}, // state - {"Const"} // axis - } - }, - {"Const"} - } - }, - {"Const"} - } - } - } - } - } - } - } - }; - // clang-format on - - std::map> outputs_map; - MapNodesToOutputs(input_graph_def, &outputs_map); - // gru match constraint function - std::set rnn_outputs; // this is rnn mid outputs - match_constraint_fun gru_match_constraint = [&outputs_map, &rnn_outputs](const NodeDef& node, - const OpTypePattern& pattern, - const NodeMatch* match) { - if (node.op() == "Add") { - const auto& add_output_nodes = outputs_map[node.name()]; - const int add_output_nodes_size = add_output_nodes.size(); - if (add_output_nodes_size >= 3 && pattern.inputs.size() == 2) { - // when Add output 3 outputs(Mul,Mul,Concat|ConcatV2,[output]), set pattern_matched to be false - std::map op_types; - for (const auto output : add_output_nodes) { - if (op_types.count(output->op())) { - op_types[output->op()] += 1; - } else { - op_types[output->op()] = 1; - } - } - bool rnn_mid_state_output = op_types.size() >= 2 && op_types["Mul"] == 2 && op_types["ConcatV2"] == 1; - if (rnn_mid_state_output && add_output_nodes_size > 3) { - rnn_outputs.insert(add_output_nodes.back()->name()); - } - if (rnn_mid_state_output) { - return false; - } - } - } - return true; - }; - - // search this pattern in the tensorflow Graph - GraphMatcher matcher(input_graph_def, gru_match_constraint); - std::vector matches; - matcher.GetOpTypeMatches(gru_cell_pattern, &matches); - - const int matchedSize = matches.size(); - bool keep_all_outputs = false; - const int rnn_outputs_size = rnn_outputs.size(); - DCHECK(rnn_outputs_size <= matchedSize) << "RNN GRU Cell Output ERROR!"; - if (rnn_outputs_size >= 1) { - keep_all_outputs = true; - } - - if (matchedSize >= 1) { - // get one node from matched node to mark the matched-pattern, incase search thoes nodes again - std::set matched_nodes{}; - for (int i = 0; i < matchedSize; ++i) { - matched_nodes.insert(matches[i].node.name()); - } - - std::vector rnn_nodes; - // from the very last node(Add), collect all the nodes in the GRU cell sub-graph, - // and delete the nodes but keep the gate and candidate kernel(weight) - std::set ready_to_detele; - // replace the input node name - std::map inputs_to_rename; - - for (int i = 0; i < matchedSize; ++i) { - // this model has gru cell - // replace the GRU cell with RNNSequenceGRU - // DCHECK(matches.size() == 1) << "Now only recognise the static_rnn()"; - // [TODO] check the matches according to the same node in the matches(Split, inputs) - - const auto& the_very_last_node = matches[i].node; // this is the GRU Cell output node - const auto& gru_input_node = - matches[i].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[0].inputs[0].node; - const auto& gate_kernel_node = matches[i].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[1].node; - const auto& gate_bias_node = matches[i].inputs[0].inputs[0].inputs[1].inputs[0].inputs[1].node; - const auto& candidate_kernel_node = matches[i].inputs[1].inputs[1].inputs[0].inputs[0].inputs[1].node; - const auto& candidate_bias_node = matches[i].inputs[1].inputs[1].inputs[0].inputs[1].node; - const auto& gate_concat_node = matches[i].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[0].node; - - const auto& rnn_next_nodes = outputs_map[the_very_last_node.name()]; - // if keep all outputs(rnn mid output), the rnn next op is Pack[tf.stack], then delete this op - if (keep_all_outputs) { - DCHECK(rnn_next_nodes.size() == 1 && rnn_next_nodes[0]->op() == "Pack"); - auto pack_node = rnn_next_nodes[0]; - ready_to_detele.insert(pack_node->name()); - } - - DCHECK(gru_input_node.op() == "Unpack") - << "Now only support for getting input from one node, like form [tf.unstack]"; - DCHECK(gate_kernel_node.op() == "Const") << "Get RNN weight error"; - DCHECK(gate_bias_node.op() == "Const") << "Get RNN weight error"; - DCHECK(candidate_kernel_node.op() == "Const") << "Get RNN weight error"; - DCHECK(candidate_bias_node.op() == "Const") << "Get RNN weight error"; - std::map node_map; - MapNamesToNodes(input_graph_def, &node_map); - const std::vector input_nodes = {gru_input_node.name()}; - const std::vector output_nodes = {the_very_last_node.name()}; - CollectSubGraphNodes(input_nodes, output_nodes, node_map, ready_to_detele); - // keep kernel node - ready_to_detele.erase(gate_kernel_node.name()); - ready_to_detele.erase(gate_bias_node.name()); - ready_to_detele.erase(candidate_kernel_node.name()); - ready_to_detele.erase(candidate_bias_node.name()); - - // construct rnn gru node - NodeDef rnn_sequence_gru_node; - rnn_sequence_gru_node.set_op("RNNSequenceGRU"); - rnn_sequence_gru_node.set_name(the_very_last_node.name() + "__RNNSequenceGRU_" + flatbuffers::NumToString(i)); - SetNodeAttr("T", DT_FLOAT, &rnn_sequence_gru_node); - if (keep_all_outputs) { - SetNodeAttr("keep_all_outputs", true, &rnn_sequence_gru_node); - } else { - SetNodeAttr("keep_all_outputs", false, &rnn_sequence_gru_node); - } - // AddNodeInput(gru_input_node.name(), &rnn_sequence_gru_node); - - if (keep_all_outputs) { - inputs_to_rename[rnn_next_nodes[0]->name()] = rnn_sequence_gru_node.name(); - } - - bool is_bidirectional_rnn = false; - // check whether this RNN-GRU is bidirectional_rnn - { - // only one gru cell matched, then go on searching to check bidirectional_rnn - matcher.SetMatchedNodes(matched_nodes); - std::vector bid_matches; - matcher.GetOpTypeMatches(gru_cell_pattern, &bid_matches); - if (bid_matches.size() == 1) { - // this is bidirectional_rnn - is_bidirectional_rnn = true; - const auto& the_very_last_node_bid = bid_matches[0].node; // this is the GRU Cell output node - const auto& gru_input_node_bid = - bid_matches[0].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[0].inputs[0].node; - DCHECK(gru_input_node.name() == gru_input_node_bid.name()) - << "bidirectional_rnn fw and bw should share one input!"; - const auto& gate_kernel_node_bid = - bid_matches[0].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[1].node; - const auto& gate_bias_node_bid = - bid_matches[0].inputs[0].inputs[0].inputs[1].inputs[0].inputs[1].node; - const auto& candidate_kernel_node_bid = - bid_matches[0].inputs[1].inputs[1].inputs[0].inputs[0].inputs[1].node; - const auto& candidate_bias_node_bid = bid_matches[0].inputs[1].inputs[1].inputs[0].inputs[1].node; - - const auto& gate_concat_node_bid = - bid_matches[0].inputs[0].inputs[0].inputs[1].inputs[0].inputs[0].inputs[0].node; - - const std::vector input_nodes = {gru_input_node_bid.name()}; - const std::vector output_nodes = {the_very_last_node_bid.name()}; - CollectSubGraphNodes(input_nodes, output_nodes, node_map, ready_to_detele); - - // keep kernel node - ready_to_detele.erase(gate_kernel_node_bid.name()); - ready_to_detele.erase(gate_bias_node_bid.name()); - ready_to_detele.erase(candidate_kernel_node_bid.name()); - ready_to_detele.erase(candidate_bias_node_bid.name()); - - // delete the rnn's input node when input_node is Unpack(tf.unstack) - DCHECK(gru_input_node.input_size() == 1) << "Error"; - AddNodeInput(NodeNameFromInput(gru_input_node.input(0)), &rnn_sequence_gru_node); - ready_to_detele.insert(gru_input_node.name()); - - // check fw or bw? - std::string prefix; - std::string node_name; - std::string suffix; - DCHECK(gate_concat_node.input_size() == 3) << "Error!"; - NodeNamePartsFromInput(gate_concat_node.input(0), &prefix, &node_name, &suffix); - - std::string prefix_bid; - std::string node_name_bid; - std::string suffix_bid; - DCHECK(gate_concat_node_bid.input_size() == 3) << "Error!"; - NodeNamePartsFromInput(gate_concat_node_bid.input(0), &prefix_bid, &node_name_bid, &suffix_bid); - if (suffix != "" && suffix_bid == "") { - // the second match is bw - // fw - AddNodeInput(gate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_bias_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_bias_node.name(), &rnn_sequence_gru_node); - // bw weight - AddNodeInput(gate_kernel_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_bias_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_kernel_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_bias_node_bid.name(), &rnn_sequence_gru_node); - - inputs_to_rename[the_very_last_node.name()] = rnn_sequence_gru_node.name(); - inputs_to_rename[the_very_last_node_bid.name()] = rnn_sequence_gru_node.name() + ":1"; - } else if (suffix == "" && suffix_bid != "") { - AddNodeInput(gate_kernel_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_bias_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_kernel_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_bias_node_bid.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_bias_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_bias_node.name(), &rnn_sequence_gru_node); - - inputs_to_rename[the_very_last_node.name()] = rnn_sequence_gru_node.name() + ":1"; - inputs_to_rename[the_very_last_node_bid.name()] = rnn_sequence_gru_node.name(); - } else { - DLOG(FATAL) << "Now only support for getting input from one node, like form [tf.unstack]"; - } - } else { - // not bidirectional_rnn - { - // delete the rnn's input node when input_node is Unpack(tf.unstack) - DCHECK(gru_input_node.input_size() == 1) << "Error"; - AddNodeInput(NodeNameFromInput(gru_input_node.input(0)), &rnn_sequence_gru_node); - ready_to_detele.insert(gru_input_node.name()); - - AddNodeInput(gate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(gate_bias_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_kernel_node.name(), &rnn_sequence_gru_node); - AddNodeInput(candidate_bias_node.name(), &rnn_sequence_gru_node); - - inputs_to_rename[the_very_last_node.name()] = rnn_sequence_gru_node.name(); - } - } - } - if (is_bidirectional_rnn) { - SetNodeAttr("is_bidirectional_rnn", true, &rnn_sequence_gru_node); - } else { - SetNodeAttr("is_bidirectional_rnn", false, &rnn_sequence_gru_node); - } - - rnn_nodes.push_back(rnn_sequence_gru_node); - } - - // construct new graph - GraphDef replaced_graph_def; - replaced_graph_def.Clear(); - for (auto& node : rnn_nodes) { - NodeDef* gru_node = replaced_graph_def.mutable_node()->Add(); - *gru_node = node; - } - for (const NodeDef& input_node : input_graph_def.node()) { - if (ready_to_detele.count(input_node.name())) { - continue; - } - NodeDef* keep_node = replaced_graph_def.mutable_node()->Add(); - *keep_node = input_node; - } - - RenameNodeInputs(replaced_graph_def, inputs_to_rename, std::unordered_set(), output_graph_def); - - std::ofstream out("rnn_gru.pb"); - std::string outStr; - output_graph_def->SerializeToString(&outStr); - out << outStr; - out.close(); - - } else { - // keep all the graph - *output_graph_def = input_graph_def; - } - - return 0; -} - -REGISTER_GRAPH_TRANSFORM("ResolveRNNGRUCell", ResolveRNNGRUCell); - -int FuseConvPad(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def) { - GraphDef replaced_graph_def; - ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"Conv2D|DepthwiseConv2dNative", - { - {"Pad", - { - {"*"}, - {"*"} - } - }, - {"*"} - } - }, // clang-format on - [](const NodeMatch& match, const std::set& input_nodes, const std::set& output_nodes, - std::vector* new_nodes) { - const NodeDef& conv_node = match.node; - const NodeDef& pad_node = match.inputs[0].node; - const NodeDef& weight_node = match.inputs[1].node; - const NodeDef& input_node = match.inputs[0].inputs[0].node; - const NodeDef& pad_dims_node = match.inputs[0].inputs[1].node; - - new_nodes->push_back(weight_node); - new_nodes->push_back(input_node); - NodeDef fused_conv_pad; - const auto& originalOpType = conv_node.op(); - fused_conv_pad.set_op(originalOpType); - fused_conv_pad.set_name(conv_node.name()); - AddNodeInput(input_node.name(), &fused_conv_pad); - AddNodeInput(weight_node.name(), &fused_conv_pad); - CopyNodeAttr(conv_node, "T", "T", &fused_conv_pad); - CopyNodeAttr(conv_node, "data_format", "data_format", &fused_conv_pad); - CopyNodeAttr(conv_node, "strides", "strides", &fused_conv_pad); - CopyNodeAttr(conv_node, "dilations", "dilations", &fused_conv_pad); - SetNodeAttr("padding", "Symmetric", &fused_conv_pad); - new_nodes->push_back(fused_conv_pad); - - return 0; - }, - &replaced_graph_def); - *output_graph_def = replaced_graph_def; - return 0; -} - -REGISTER_GRAPH_TRANSFORM("FuseConvPad", FuseConvPad); - -int FuseRelu6(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def) { - std::map inputs_to_rename; - GraphDef replaced_graph_def; - ReplaceMatchingOpTypes( - input_graph_def, // clang-format off - {"Minimum", - { - {"Relu"}, - {"Const"} - } - }, // clang-format on - [&inputs_to_rename](const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, std::vector* new_nodes) { - const auto& minimun_node = match.node; - const auto& relu_node = match.inputs[0].node; - const auto& const_node = match.inputs[1].node; - - tensorflow::AttrValue value; - if (find_attr_value(&const_node, "value", value)) { - const float minimun_value = value.tensor().float_val(0); - DCHECK(6.0f == minimun_value) << "fuse relu6 failed!"; - } else { - DLOG(FATAL) << "fuse relu6 failed!"; - } - NodeDef relu6; - relu6.set_op("Relu6"); - relu6.set_name(relu_node.name()); - AddNodeInput(relu_node.input(0), &relu6); - new_nodes->push_back(relu6); - inputs_to_rename[minimun_node.name()] = relu6.name(); - return 0; - }, - &replaced_graph_def); - - RenameNodeInputs(replaced_graph_def, inputs_to_rename, std::unordered_set(), output_graph_def); - - return 0; -} - -} // namespace TFModelOptimizer diff --git a/tools/converter/source/tensorflow/TfUtils.cpp b/tools/converter/source/tensorflow/TfUtils.cpp index 2320a5939..387b899c3 100644 --- a/tools/converter/source/tensorflow/TfUtils.cpp +++ b/tools/converter/source/tensorflow/TfUtils.cpp @@ -64,486 +64,3 @@ bool convertDataFormat(const float* src, float* dst, int planeNumber, int CI, in return true; } - -namespace TFModelOptimizer { -static std::vector strSplit(const std::string input_str, std::string pattern) { - std::string::size_type pos; - std::vector result; - std::string str = input_str; - str += pattern; - const int size = str.size(); - - for (int i = 0; i < size; i++) { - pos = str.find(pattern, i); - if (pos < size) { - std::string s = str.substr(i, pos - i); - result.push_back(s); - i = pos + pattern.size() - 1; - } - } - return result; -} - -static bool strEndsWith(std::string text, std::string suffix) { - return suffix.empty() || (text.size() >= suffix.size() && - memcmp(text.data() + (text.size() - suffix.size()), suffix.data(), suffix.size()) == 0); -} - -void NodeNamePartsFromInput(const std::string& input_name, std::string* prefix, std::string* node_name, - std::string* suffix) { - auto input_parts = strSplit(input_name, ":"); - if (input_parts.size() < 2) { - *suffix = ""; - } else { - *suffix = ":" + input_parts[1]; - } - *node_name = input_parts[0]; - if ((*node_name)[0] == '^') { - *prefix = "^"; - node_name->erase(node_name->begin()); - } else { - *prefix = ""; - } -} - -std::string NodeNameFromInput(const std::string& input_name) { - std::string prefix; - std::string node_name; - std::string suffix; - NodeNamePartsFromInput(input_name, &prefix, &node_name, &suffix); - return node_name; -} - -std::string CanonicalInputName(const std::string& input_name) { - std::string prefix; - std::string node_name; - std::string suffix; - NodeNamePartsFromInput(input_name, &prefix, &node_name, &suffix); - if (suffix.empty()) { - suffix = ":0"; - } - return prefix + node_name + suffix; -} - -void MatchedNodesAsArray(const NodeMatch& match, std::vector* result) { - std::set found_nodes; - std::vector current_matches = {match}; - while (!current_matches.empty()) { - std::vector next_matches; - for (const NodeMatch& current_match : current_matches) { - if (found_nodes.count(current_match.node.name())) { - continue; - } - found_nodes.insert(current_match.node.name()); - result->push_back(current_match.node); - for (const NodeMatch& input_match : current_match.inputs) { - next_matches.push_back(input_match); - } - } - current_matches = next_matches; - } -} - -void RecordMatchedNodes(const NodeMatch& match, std::set* matchedNodes) { - matchedNodes->insert(match.node.name()); - for (const NodeMatch& input_match : match.inputs) { - RecordMatchedNodes(input_match, matchedNodes); - } -} - -std::string OpTypePattern::DebugString() const { - std::string result = "{" + op + ", {"; - for (const OpTypePattern& input : inputs) { - result += input.DebugString() + ","; - } - result += "}}"; - return result; -} - -std::string NodeMatch::DebugString() const { - std::string result = "{"; - result += node.DebugString(); - result += ", {"; - for (const NodeMatch& input : inputs) { - result += input.DebugString() + ","; - } - result += "}}"; - return result; -} - -void MapNamesToNodes(const GraphDef& graph_def, std::map* result) { - for (const NodeDef& node : graph_def.node()) { - (*result)[node.name()] = &node; - } -} - -void MapNodesToOutputs(const GraphDef& graph_def, std::map>* result) { - // std::map node_map; - // MapNamesToNodes(graph_def, &node_map); - for (const NodeDef& node : graph_def.node()) { - for (const std::string& input : node.input()) { - std::string input_node_name = NodeNameFromInput(input); - (*result)[input_node_name].push_back(&node); - } - } -} - -int SortByExecutionOrder(const GraphDef& input_graph_def, GraphDef* output_graph_def) { - const int num_nodes = input_graph_def.node_size(); - std::vector ready; - std::vector pending_count; - pending_count.reserve(num_nodes); - std::vector> outputs(num_nodes); - std::map name_index; - for (int i = 0; i < input_graph_def.node_size(); ++i) { - const NodeDef& node(input_graph_def.node(i)); - name_index[node.name()] = i; - } - - for (int n = 0; n < num_nodes; ++n) { - const NodeDef& node_def(input_graph_def.node(n)); - if (IsMerge(node_def)) { - int num_control_edges = 0; - for (int i = 0; i < node_def.input_size(); ++i) { - if (node_def.input(i)[0] == '^') { - num_control_edges++; - } - } - pending_count.push_back(num_control_edges + 1); - } else { - pending_count.push_back(node_def.input_size()); - } - if (node_def.input_size() == 0) { - ready.push_back(n); - continue; - } - for (int i = 0; i < node_def.input_size(); ++i) { - const std::string input_name = node_def.input(i); - const std::string input_node_name = NodeNameFromInput(input_name); - if (!name_index.count(input_node_name)) { - LOG(FATAL) << "Node '" << node_def.name() << "': Unknown input node ==> '" << node_def.input(i) << "'"; - } - outputs[name_index[input_node_name]].push_back(n); - } - } - - int processed = 0; - output_graph_def->Clear(); - while (!ready.empty()) { - int o = ready.back(); - ready.pop_back(); - ++processed; - const NodeDef& node_def(input_graph_def.node(o)); - *output_graph_def->mutable_node()->Add() = node_def; - - for (size_t i = 0; i < outputs[o].size(); ++i) { - const int output = outputs[o][i]; - pending_count[output]--; - if (pending_count[output] == 0) { - ready.push_back(output); - } - } - } - if (processed < num_nodes) { - LOG(FATAL) << "IN " << (num_nodes - processed) << " NODES IN A CYCLE"; - return -1; - } - return 0; -} - -GraphMatcher::GraphMatcher(const tensorflow::GraphDef& graph_def, match_constraint_fun func) { - SortByExecutionOrder(graph_def, &graph_def_); - MapNamesToNodes(graph_def_, &node_map_); - match_constraint_ = std::move(func); - matched_nodes_ = std::set(); -} - -int GraphMatcher::GetOpTypeMatches(const OpTypePattern& pattern, std::vector* matches) { - std::set matched_nodes = matched_nodes_; - for (const NodeDef& node : graph_def_.node()) { - // Skip any nodes that are already part of a match. - if (matched_nodes.count(node.name())) { - continue; - } - NodeMatch match; - if (DoesOpTypeMatch(node, pattern, matched_nodes, &match)) { - RecordMatchedNodes(match, &matched_nodes); - matches->push_back(match); - } - } - return 0; -} - -bool GraphMatcher::DoesOpTypeMatch(const NodeDef& node, const OpTypePattern& pattern, - const std::set& previously_matched_nodes, NodeMatch* match) { - // LOG(INFO) << "Looking at node " << node->DebugString(); - // LOG(INFO) << "Pattern=" << pattern.DebugString(); - - if (previously_matched_nodes.count(node.name())) { - return false; - } - bool pattern_matched = false; - if (pattern.op == "*") { - pattern_matched = true; - } else { - auto pattern_ops = strSplit(pattern.op, "|"); - for (const auto& pattern_op : pattern_ops) { - if (node.op() == pattern_op) { - // default - pattern_matched = true; - // call match constraint function - pattern_matched = match_constraint_(node, pattern, match); - } - } - } - if (!pattern_matched) { - return false; - } - - match->node = node; - // LOG(INFO) << "Match=" << match->DebugString(); - std::vector non_control_inputs; - for (const std::string& input : node.input()) { - if (!input.empty() && (input[0] != '^')) { - non_control_inputs.push_back(input); - } - } - - if (pattern.inputs.empty()) { - // the last one pattern - return true; - } - if (non_control_inputs.size() != pattern.inputs.size()) { - return false; - } - for (int i = 0; i < pattern.inputs.size(); ++i) { - const auto& input_node_name = NodeNameFromInput(non_control_inputs[i]); - const NodeDef& input_node = *(node_map_[input_node_name]); - const auto input_pattern = pattern.inputs[i]; - match->inputs.push_back(NodeMatch()); - NodeMatch* input_match = &(match->inputs.back()); - if (!DoesOpTypeMatch(input_node, input_pattern, previously_matched_nodes, input_match)) { - return false; - } - } - return true; -} - -void GraphMatcher::SetMatchConstraintFunction(match_constraint_fun func) { - match_constraint_ = std::move(func); -} - -void GraphMatcher::SetMatchedNodes(std::set& matched_nodes) { - matched_nodes_ = matched_nodes; -} - -int ReplaceMatchingOpTypes( - const GraphDef& input_graph_def, const OpTypePattern& pattern, - const std::function&, const std::set&, - std::vector*)>& node_generator, - GraphDef* output_graph_def) { - GraphMatcher matcher(input_graph_def, [](const NodeDef& node, const OpTypePattern& pattern, - const NodeMatch* match) { return true; }); - std::vector matches; - matcher.GetOpTypeMatches(pattern, &matches); - - std::set matched_nodes; - std::map matches_by_headName; - for (const NodeMatch& match : matches) { - matches_by_headName[match.node.name()] = &match; - RecordMatchedNodes(match, &matched_nodes); - } - std::map> outputs_map; - MapNodesToOutputs(input_graph_def, &outputs_map); - output_graph_def->Clear(); - for (const NodeDef& input_node : input_graph_def.node()) { - if (matches_by_headName.count(input_node.name())) { - const NodeMatch* match = matches_by_headName[input_node.name()]; - std::vector matched_nodes_array; - MatchedNodesAsArray(*match, &matched_nodes_array); - std::set matched_nodes_lookup; - for (const auto matched_node : matched_nodes_array) { - matched_nodes_lookup.insert(matched_node.name()); - } - std::set input_nodes; - std::set output_nodes; - for (const auto matched_node : matched_nodes_array) { - for (const auto& input_name : matched_node.input()) { - if (!matched_nodes_lookup.count(input_name)) { - input_nodes.insert(matched_node.name()); - } - } - - if (outputs_map.count(matched_node.name())) { - for (const auto& dependent_node : outputs_map[matched_node.name()]) { - if (!matched_nodes_lookup.count(dependent_node->name())) { - output_nodes.insert(matched_node.name()); - } - } - } - } - - std::vector new_nodes; - node_generator(*match, input_nodes, output_nodes, &new_nodes); - - std::set new_node_names; - for (const auto& new_node : new_nodes) { - new_node_names.insert(new_node.name()); - } - bool abort_replacement = false; - if (false) { - for (const auto& expected_output : output_nodes) { - if (!new_node_names.count(expected_output)) { - LOG(ERROR) << "Expected " << expected_output << " to be preserved."; - abort_replacement = true; - } - } - } - if (abort_replacement) { - LOG(ERROR) << "Generator function didn't preserve needed nodes."; - std::vector old_nodes; - MatchedNodesAsArray(*match, &old_nodes); - for (const NodeDef& old_node : old_nodes) { - NodeDef* added_node = output_graph_def->mutable_node()->Add(); - *added_node = old_node; - } - } else { - for (const NodeDef& new_node : new_nodes) { - NodeDef* added_node = output_graph_def->mutable_node()->Add(); - *added_node = new_node; - } - } - } else if (!matched_nodes.count(input_node.name())) { - NodeDef* added_node = output_graph_def->mutable_node()->Add(); - *added_node = input_node; - } else { - } - } - return 0; -} - -int RenameNodeInputs(const GraphDef& input_graph_def, const std::map& inputs_to_rename, - const std::unordered_set& nodes_to_ignore, GraphDef* output_graph_def) { - std::map>> canonical_inputs_to_rename; - for (const auto& input_to_rename : inputs_to_rename) { - canonical_inputs_to_rename[NodeNameFromInput(input_to_rename.first)].push_back( - {input_to_rename.first, input_to_rename.second}); - } - - output_graph_def->Clear(); - for (const NodeDef& node : input_graph_def.node()) { - NodeDef* new_node = output_graph_def->mutable_node()->Add(); - *new_node = node; - new_node->mutable_input()->Clear(); - for (const std::string& input_name : node.input()) { - std::set already_visited; - std::string new_input_name = input_name; - while (canonical_inputs_to_rename.count(NodeNameFromInput(new_input_name))) { - std::string input_node_name = NodeNameFromInput(new_input_name); - if (already_visited.count(input_node_name)) { - LOG(FATAL) << "RenameNodeInputs argument contains a cycle for " << input_node_name; - } - already_visited.insert(input_node_name); - if (nodes_to_ignore.count(node.name())) { - break; - } - bool any_match_found = false; - for (const std::pair& input_to_rename : - canonical_inputs_to_rename.at(input_node_name)) { - const std::string& source_name = input_to_rename.first; - const std::string& dest_name = input_to_rename.second; - bool is_match; - std::string match_name; - if (strEndsWith(source_name, ":*")) { - is_match = true; - std::string prefix; - std::string unused_node_name; - std::string suffix; - NodeNamePartsFromInput(new_input_name, &prefix, &unused_node_name, &suffix); - match_name = prefix + dest_name + suffix; - } else { - is_match = (CanonicalInputName(source_name) == CanonicalInputName(new_input_name)); - match_name = dest_name; - } - if (is_match) { - new_input_name = match_name; - any_match_found = true; - } - } - if (!any_match_found) { - break; - } - } - *(new_node->mutable_input()->Add()) = new_input_name; - } - } - return 0; -} - -void CollectSubGraphNodes(const std::vector& input_nodes, const std::vector& output_nodes, - std::map& node_map, std::set& sub_graph_nodes) { - if (input_nodes.empty() || output_nodes.empty()) { - return; - } - - std::vector visit_nodes = output_nodes; - while (!visit_nodes.empty()) { - sub_graph_nodes.insert(visit_nodes.back()); - const NodeDef* cur_output_node = node_map[visit_nodes.back()]; - - visit_nodes.pop_back(); - const int input_node_size = cur_output_node->input_size(); - if (input_node_size == 0) { - continue; - } - - for (int j = 0; j < input_node_size; ++j) { - const auto& input_node_name = NodeNameFromInput(cur_output_node->input(j)); - if (std::find(input_nodes.begin(), input_nodes.end(), input_node_name) != input_nodes.end()) { - continue; - } - if (std::find(visit_nodes.begin(), visit_nodes.end(), input_node_name) == visit_nodes.end() && - !sub_graph_nodes.count(input_node_name)) { - visit_nodes.push_back(input_node_name); - } - } - } -} - -void CopyOriginalMatch(const NodeMatch& match, std::vector* new_nodes) { - std::vector old_nodes; - MatchedNodesAsArray(match, &old_nodes); - for (const NodeDef& old_node : old_nodes) { - new_nodes->push_back(old_node); - } -} - -void SetAttrValue(tensorflow::DataType type, AttrValue* out) { - out->set_type(type); -} - -void SetAttrValue(bool value, AttrValue* out) { - out->set_b(value); -} - -void SetAttrValue(const std::string& value, AttrValue* out) { - out->set_s(value.c_str()); -} - -void SetAttrValue(float value, AttrValue* out) { - out->set_f(value); -} - -void AddNodeInput(const std::string& input_name, NodeDef* node) { - *(node->mutable_input()->Add()) = input_name; -} -void CopyNodeAttr(const NodeDef& source, const std::string& source_key, const std::string& dest_key, NodeDef* dest) { - CHECK_NE(0, source.attr().count(source_key)) << "No key '" << source_key << "' found in " << source.DebugString(); - (*(dest->mutable_attr()))[dest_key] = source.attr().at(source_key); -} -TransformRegistry* GetTransformRegistry() { - static TransformRegistry transformRegistry; - return &transformRegistry; -} - -} // namespace TFModelOptimizer diff --git a/tools/converter/source/tensorflow/TfUtils.hpp b/tools/converter/source/tensorflow/TfUtils.hpp index abdc55afb..bd07e26a4 100644 --- a/tools/converter/source/tensorflow/TfUtils.hpp +++ b/tools/converter/source/tensorflow/TfUtils.hpp @@ -26,155 +26,5 @@ bool find_attr_value(const tensorflow::NodeDef* node, const char* key, tensorflo // Convert weight format from [KH,KW,CI,CO] to [CO,CI,KH,KW] bool convertDataFormat(const float* src, float* dst, int planeNumber, int CI, int CO); -namespace TFModelOptimizer { -// namespace TFModelOptimizer comes from tensorflow transform graph tools -using namespace tensorflow; - -inline bool IsMerge(const NodeDef& node_def) { - return node_def.op() == "Merge" || node_def.op() == "RefMerge"; -} - -struct OpTypePattern { - std::string op; - std::vector inputs; - std::string DebugString() const; -}; - -struct NodeMatch { - NodeMatch() : node() { - } - NodeDef node; - std::vector inputs; - std::string DebugString() const; -}; - -void NodeNamePartsFromInput(const std::string& input_name, std::string* prefix, std::string* node_name, - std::string* suffix); -std::string NodeNameFromInput(const std::string& input_name); -std::string CanonicalInputName(const std::string& input_name); -void MapNamesToNodes(const GraphDef& graph_def, std::map* result); -void MapNodesToOutputs(const GraphDef& graph_def, std::map>* result); -int SortByExecutionOrder(const GraphDef& input_graph_def, GraphDef* output_graph_def); - -typedef std::function - match_constraint_fun; - -class GraphMatcher { -public: - GraphMatcher(const GraphDef& graph_def, match_constraint_fun func); - int GetOpTypeMatches(const OpTypePattern& pattern, std::vector* matches); - void SetMatchConstraintFunction(match_constraint_fun func); - void SetMatchedNodes(std::set& matched_nodes); - -private: - bool DoesOpTypeMatch(const NodeDef& node, const OpTypePattern& pattern, - const std::set& previously_matched_nodes, NodeMatch* match); - - GraphDef graph_def_; - std::map node_map_; - // std::map> outputs_map_; - match_constraint_fun match_constraint_; - std::set matched_nodes_; -}; - -int ReplaceMatchingOpTypes( - const GraphDef& input_graph_def, const OpTypePattern& pattern, - const std::function&, const std::set&, - std::vector*)>& node_generator, - GraphDef* output_graph_def); - -int RenameNodeInputs(const GraphDef& input_graph_def, const std::map& inputs_to_rename, - const std::unordered_set& nodes_to_ignore, GraphDef* output_graph_def); - -// Collect the sub-graph nodes(include output_nodes, but not input_nodes), from the output_nodes to input_nodes -void CollectSubGraphNodes(const std::vector& input_nodes, const std::vector& output_nodes, - std::map& node_map, std::set& sub_graph_nodes); - -// set attr value function -void SetAttrValue(tensorflow::DataType type, AttrValue* out); -void SetAttrValue(bool value, AttrValue* out); -void SetAttrValue(const std::string& value, AttrValue* out); -void SetAttrValue(float value, AttrValue* out); - -void AddNodeInput(const std::string& input_name, NodeDef* node); -// Copies an attribute from one NodeDef to another. -void CopyNodeAttr(const NodeDef& source, const std::string& source_key, const std::string& dest_key, NodeDef* dest); - -// Inserts a value into a NodeDef's map of attributes. -// This is a bit different than AddNodeAttr in node_def_util.h because it -// overwrites any existing attributes with the same key. -template -inline void SetNodeAttr(const std::string& key, const T& value, NodeDef* node) { - AttrValue attr_value; - SetAttrValue(value, &attr_value); - auto* attr_map = node->mutable_attr(); - (*attr_map)[key] = attr_value; -} - -void CopyOriginalMatch(const NodeMatch& match, std::vector* new_nodes); - -// Holds information that's needed for transform functions. -typedef std::map> TransformFuncParameters; -struct TransformFuncContext { - std::vector input_names; - std::vector output_names; - TransformFuncParameters params; - - // Returns how many occurrences of the given parameter are present. - int CountParameters(const std::string& name) const; - - // Gets a single instance of a parameter, using a default if it's not present. - void GetOneStringParameter(const std::string& name, const std::string& default_value, std::string* result) const; - - // Gets a single occurrence of a parameter as a 32-bit integer, falling back - // to a default if it isn't present and returning an error if it isn't - // convertible to a number. - void GetOneInt32Parameter(const std::string& name, int32_t default_value, int32_t* result) const; - - // Gets a single occurrence of a parameter as a 64-bit integer, falling back - // to a default if it isn't present and returning an error if it isn't - // convertible to a number. - void GetOneInt64Parameter(const std::string& name, int64_t default_value, int64_t* result) const; - - // Gets a single occurrence of a parameter as a floating point number, falling - // back to a default if it isn't present and returning an error if it isn't - // convertible to a number. - void GetOneFloatParameter(const std::string& name, float default_value, float* result) const; - - // Gets a single occurrence of a parameter as a boolean, falling back to a - // default if it isn't present and returning an error if it's not one of - // "true", "1", "false", or "0". - void GetOneBoolParameter(const std::string& name, bool default_value, bool* result) const; -}; - -typedef std::function TransformFunc; - -typedef std::map TransformRegistry; -TransformRegistry* GetTransformRegistry(); -class TransformRegistrar { -public: - TransformRegistrar(const std::string& name, TransformFunc transform_func) { - auto transform_registry = GetTransformRegistry(); - (*transform_registry)[name] = transform_func; - } -}; - -#define REGISTER_GRAPH_TRANSFORM(name, func) REGISTER_GRAPH_TRANSFORM_UNIQ_HELPER(__COUNTER__, name, func) -#define REGISTER_GRAPH_TRANSFORM_UNIQ_HELPER(ctr, name, func) REGISTER_GRAPH_TRANSFORM_UNIQ(ctr, name, func) -#define REGISTER_GRAPH_TRANSFORM_UNIQ(ctr, name, func) \ - static TransformRegistrar registrar__body__##ctr##__object(name, func); - -// tensorflow mode optimizerss -int FoldBatchNormsAlgebraic(const GraphDef& input_graph_def, const TransformFuncContext& context, - GraphDef* output_graph_def); -int FoldMoments(const GraphDef& input_graph_def, const TransformFuncContext& context, GraphDef* output_graph_def); -int RemoveNodes(const GraphDef& input_graph_def, const TransformFuncContext& context, GraphDef* output_graph_def); -int ResolveRNNGRUCell(const GraphDef& input_graph_def, const TransformFuncContext& context, GraphDef* output_graph_def); -int FuseConvPad(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def); -int FuseRelu6(const tensorflow::GraphDef& input_graph_def, const TransformFuncContext& context, - tensorflow::GraphDef* output_graph_def); - -} // namespace TFModelOptimizer #endif // TFUTILS_HPP diff --git a/tools/converter/tools/auto_quant.py b/tools/converter/tools/auto_quant.py new file mode 100644 index 000000000..480456f56 --- /dev/null +++ b/tools/converter/tools/auto_quant.py @@ -0,0 +1,263 @@ +import sys +import os +import json +import functools +mnnconvert = './MNNConvert' +if os.path.exists('MNNConvert'): + mnnconvert = './MNNConvert' +elif os.path.exists('MNNConvert.exe'): + mnnconvert = 'MNNConvert.exe' +else: + mnnconvert = 'mnnconvert' + +print('Use MNN Convert: ', mnnconvert) +import argparse +class QuantInfo: + def __init__(self, jsonPath): + compress = None + with open(jsonPath) as f: + compress = json.loads(f.read()) + if 'algo' not in compress: + print("Invalid Json") + return + self.dstjson = jsonPath + algos = compress['algo'] + self.compress = compress + self.conv_1x1 = [] + self.conv_other = [] + + for algo in algos: + if 'type' not in algo: + continue + if algo['type'] == 'QUANTIZE': + if 'quantParams' in algo: + quantalgo = algo['quantParams'] + if 'layer' in quantalgo: + layers = quantalgo['layer'] + for layer in layers: + if 'opName' not in layer: + continue + if 'conv' not in layer: + continue + conv = layer['conv'] + kernelSize = conv['kernelSize'] + if kernelSize[0] * kernelSize[1] == 1: + self.conv_1x1.append(layer) + else: + self.conv_other.append(layer) + break + def computeConvSize(layer): + conv = layer['conv'] + kernelSize = conv['kernelSize'] + inputChannel = conv['inputChannel'] + outputChannel = conv['outputChannel'] + return kernelSize * inputChannel * outputChannel + def sortconv(A, B): + return computeConvSize(A) < computeConvSize(B) + self.conv_1x1.sort(key=functools.cmp_to_key(sortconv)) + def update(self): + with open(self.dstjson, 'w') as f: + f.write(json.dumps(self.compress, indent=4)) + def setBlock(self, block): + for c in self.conv_1x1: + c['weight'][0]['blockSize'] = block + for c in self.conv_other: + c['weight'][0]['bits'] = 0 + def mutableSize(self): + return len(self.conv_1x1) + def setMultiBlock(self, seprate, block0, block1): + if seprate >= len(self.conv_1x1): + return + for i in range(0, seprate): + self.conv_1x1[i]['weight'][0]['blockSize'] = block0 + for i in range(seprate, len(self.conv_1x1)): + self.conv_1x1[i]['weight'][0]['blockSize'] = block1 + def setBits(self, pos, bits): + if pos >= len(self.conv_1x1): + return + self.conv_1x1[pos]['weight'][0]['bits'] = bits + + +def getRate(loginfo): + lines = loginfo.split('\n') + lines = list(filter(lambda x:x.find('TESTERROR')>=0, lines)) + if len(lines) == 0: + return 0.0 + rate = 0.0 + for line in lines: + content = line.split('absMaxV:')[1].replace(' ', '') + value = content.split('-DiffMax') + maxv = float(value[0]) + diffmax = float(value[1]) + if maxv > 0.01 and diffmax / maxv > rate: + rate = diffmax / maxv + return rate + +forwardJson = '.tmp.json' +def initDynamicQuantJson(memory): + v = {} + v['backend'] = 0 + v['mode'] = 4 + v['memory'] = memory + v['precision'] = 1 + v['hints'] = [5, 1] + print('Create test config: ', forwardJson, ", memory=", memory) + with open(forwardJson, 'w') as f: + f.write(json.dumps(v)) + +def testJson(model, dstjson, testdir, dstmodel): + cmd = mnnconvert + ' -f MNN --modelFile ' + model + ' --MNNModel ' + dstmodel + cmd += ' --weightQuantBits=8 --weightQuantAsymmetric=0 ' + cmd += ' --compressionParamsFile ' + dstjson + cmd += ' --testdir ' + testdir + cmd += ' --thredhold 0.001' + cmd += ' --testconfig ' + forwardJson + cmd += ' --alignDenormalizedValue 0 ' + info = os.popen(cmd).read() + return getRate(info) + +class TestSuit: + def __init__(self, model, dstjson, testdir, dstmodel): + self.model = model + self.dstjson = dstjson + self.testdir = testdir + self.dstmodel = dstmodel + def test(self): + return testJson(self.model, self.dstjson, self.testdir, self.dstmodel) + +def findBestBits(info, test, targetRate): + info.setBlock(64) + info.update() + rate = test.test() + if rate > targetRate: + return rate + length = info.mutableSize() + tested = False + for i in range(length): + info.setBits(i, 4) + info.update() + rate = test.test() + tested = True + if rate > targetRate: + # roll back to 8 + info.setBits(i, 8) + tested = False + else: + print('Set %d layer to 4 bits' %i, ', rate=%f' %rate) + if not tested: + info.update() + rate = test.test() + return rate + +def findBestBlock(info, test, targetRate): + validBlock = 0 + bestBlock = 256 + bestRate = 1.0 + rate = 1.0 + for block in (256, 128, 64, 32): + info.setBlock(block) + info.update() + rate = test.test() + print('block=%d,' %block + ' rate=%f' %rate) + if rate < bestRate: + bestRate = rate + bestBlock = block + if rate < targetRate: + validBlock = block + break + if validBlock < 256 and rate < targetRate: + largeBlock = validBlock * 2 + length = info.mutableSize() + # 2-div check + sta = 0 + pos = length // 2 + fin = length + while pos > sta and pos < fin: + info.setMultiBlock(pos, largeBlock, validBlock) + info.update() + rate = test.test() + print('len:%d' %pos, ', rate:%f' %rate) + if rate < targetRate: + sta = pos + else: + fin = pos + pos = (sta + fin + 1) // 2 + if sta != pos: + info.setMultiBlock(sta, largeBlock, validBlock) + info.update() + rate = test.test() + else: + info.setBlock(bestBlock) + info.update() + rate = test.test() + return rate + +def skipMoreOps(info, test, targetRate, current): + length = info.mutableSize() + for il in range(length): + i = length - il - 1 + info.setBits(i, 0) + info.update() + rate = test.test() + if rate < current: + print("Skip quant for ", i, ", rate=", rate) + current = rate + else: + info.setBits(i, 8) + if rate <= targetRate: + break + return rate + + +def mainFunction(): + parser = argparse.ArgumentParser(description='llm_exporter', formatter_class=argparse.RawTextHelpFormatter) + parser.add_argument('--model', type=str, required=True,help='src float mnn model') + parser.add_argument('--quant_model', type=str, required=True,help='dst quant mnn model') + parser.add_argument('--test_dir', type=str, required=True,help='test dir') + parser.add_argument('--rate', type=float, default=0.05,help='test rate') + parser.add_argument('--select_bits', type=int, default=1,help='Try set layer as 4 bits') + parser.add_argument('--select_block', type=int, default=1,help='Try select blocks') + args = parser.parse_args() + rate = args.rate + model = args.model + dstmodel = args.quant_model + testdir = args.test_dir + print("Target Rate is %f" %rate) + targetRate = rate + print('src: ', model, ", dst:", dstmodel) + dstjson = dstmodel + '.json' + initDynamicQuantJson(2) + if os.path.exists(dstjson): + print("Erase old file: ", dstjson) + os.remove(dstjson) + if not os.path.exists(model): + print(model, " not exist") + return + if not os.path.exists(testdir) or not os.path.isdir(testdir): + print(testdir, " not exist or is not dir") + return + if not os.path.exists(os.path.join(testdir, "input.json")): + print(testdir, " not has input.json") + return + test = TestSuit(model, dstjson, testdir, dstmodel) + rate = test.test() + print('Init rate: %f' %rate) + info = QuantInfo(dstjson) + if args.select_bits > 0: + rate = findBestBits(info, test, targetRate) + if args.select_block > 0: + rate = findBestBlock(info, test, targetRate) + if rate > targetRate: + initDynamicQuantJson(0) + rate = test.test() + if rate > targetRate: + rate = skipMoreOps(info, test, targetRate, rate) + print("rate=%f" %rate, ", save to " + dstmodel) + originSize = os.path.getsize(model) / 1024.0 / 1024.0 + dstSize = os.path.getsize(dstmodel) / 1024.0 / 1024.0 + + print("Compress From %f MB " %originSize, ' to %f MB' %dstSize) + + +if __name__ == '__main__': + mainFunction() diff --git a/tools/converter/tools/user_quant_modify_demo.py b/tools/converter/tools/user_quant_modify_demo.py new file mode 100644 index 000000000..e54b00f52 --- /dev/null +++ b/tools/converter/tools/user_quant_modify_demo.py @@ -0,0 +1,54 @@ +import json +import sys + +def treatQuant(quantalgo, limitsize): + if 'layer' not in quantalgo: + return + layers = quantalgo['layer'] + for layer in layers: + if 'opName' not in layer: + continue + name = layer['opName'] + if 'conv' not in layer: + continue + conv = layer['conv'] + inputChannel = conv['inputChannel'] + outputChannel = conv['outputChannel'] + kernelSize = conv['kernelSize'] + weight = layer['weight'][0] + size = inputChannel * outputChannel * kernelSize[0] * kernelSize[1] + if weight['blockSize'] <= 0: + print('Set ' + name + " block size to 128") + weight['blockSize'] = 128 + if size < limitsize: + print('Skip ' + name + " quant, becuase size < %d" %limitsize) + weight['bits'] = 0 + elif weight['blockSize'] != 0 and kernelSize[0] * kernelSize[1] != 1: + print('Skip ' + name + " quant, because not 1x1 conv with block") + weight['bits'] = 0 + + return + + +def mainFunciton(): + if len(sys.argv) < 3: + print("Usage: python user_quant_modify_demo.py input.json output.json") + return + compress = {} + with open(sys.argv[1]) as f: + compress = json.loads(f.read()) + if 'algo' not in compress: + return + algos = compress['algo'] + for algo in algos: + if 'type' not in algo: + continue + if algo['type'] == 'QUANTIZE': + if 'quantParams' in algo: + treatQuant(algo['quantParams'], 1024) + break + with open(sys.argv[2], 'w') as f: + f.write(json.dumps(compress, indent=4)) + +if __name__ == '__main__': + mainFunciton() diff --git a/tools/converter/user_provide_quant_params.json b/tools/converter/user_provide_quant_params.json index 9ee77dee2..b48c023c6 100644 --- a/tools/converter/user_provide_quant_params.json +++ b/tools/converter/user_provide_quant_params.json @@ -1,68 +1,66 @@ { - "pipeline": { - "version": "0.0.0", - "algo": [{ - "type": 0, - "quant_params": { - "round_mode": 0, - "layer": [{ - "weight": [ - { - "name": "weight_0", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - }, - { - "name": "weight_1", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - } - ], - "input": [ - { - "name": "input_0", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - }, - { - "name": "input_1", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - } - ], - "output": [ - { - "name": "output_0", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - }, - { - "name": "output_1", - "bits": 8, - "scales": [0.001, 0.001, 0.001], - "zero_point": 0, - "clamp_min": -128, - "clamp_max": 127 - } - ], - "method": 0 - }] - } - }] - } -} \ No newline at end of file + "version": "0.0.0", + "algo": [{ + "type": 0, + "quant_params": { + "round_mode": 0, + "layer": [{ + "weight": [ + { + "name": "weight_0", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + }, + { + "name": "weight_1", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + } + ], + "input": [ + { + "name": "input_0", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + }, + { + "name": "input_1", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + } + ], + "output": [ + { + "name": "output_0", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + }, + { + "name": "output_1", + "bits": 8, + "scales": [0.001, 0.001, 0.001], + "zero_point": 0, + "clamp_min": -128, + "clamp_max": 127 + } + ], + "method": 0 + }] + } + }] +} diff --git a/tools/cpp/ModuleBasic.cpp b/tools/cpp/ModuleBasic.cpp index adcea4937..5fb575257 100644 --- a/tools/cpp/ModuleBasic.cpp +++ b/tools/cpp/ModuleBasic.cpp @@ -83,6 +83,7 @@ static bool compareOutput(VARP output, const std::string& directName, const std: auto diffAbsMax = _ReduceMax(diff); auto absMaxV = absMax->readMap()[0]; auto diffAbsMaxV = diffAbsMax->readMap()[0]; + MNN_PRINT("For %s, max = %f, diffmax = %f, diff rate = %f\n", name.c_str(), absMaxV, diffAbsMaxV, diffAbsMaxV / fmaxf(absMaxV, 1e-6)); if (absMaxV * 0.01f < diffAbsMaxV || MNN_IS_NAN(absMaxV)) { MNN_ERROR("TESTERROR %s value error : absMaxV:%f - DiffMax %f\n", name.c_str(), absMaxV, diffAbsMaxV); return false; @@ -279,9 +280,6 @@ int main(int argc, char *argv[]) { if (runMask & 512) { rtmgr->setHint(Interpreter::WINOGRAD_MEMORY_LEVEL, 0); } - if (runMask & 1024) { - rtmgr->setHint(Interpreter::DYNAMIC_QUANT_OPTIONS, 1); - } if (runMask & 2048) { rtmgr->setExternalPath("tmp", Interpreter::EXTERNAL_FEATUREMAP_DIR); } diff --git a/tools/cv/CMakeLists.txt b/tools/cv/CMakeLists.txt index 4877ca016..85f978e9c 100644 --- a/tools/cv/CMakeLists.txt +++ b/tools/cv/CMakeLists.txt @@ -1,103 +1,99 @@ -IF(MNN_BUILD_OPENCV) - # imgproc submodules start - option(MNN_IMGPROC_COLOR "Enable imgproc color" ON) - option(MNN_IMGPROC_GEOMETRIC "Enable imgproc geometric" ON) - option(MNN_IMGPROC_DRAW "Enable imgproc draw" ON) - option(MNN_IMGPROC_FILTER "Enable imgproc filter" ON) - option(MNN_IMGPROC_MISCELLANEOUS "Enable imgproc miscellaneous" ON) - option(MNN_IMGPROC_STRUCTRAL "Enable imgproc structral" ON) - option(MNN_IMGPROC_HISTOGRAMS "Enable imgproc histograms" ON) - # imgproc submodules end - option(MNN_CVCORE "Enable cv core" ON) - option(MNN_CALIB3D "Enable calib3d" ON) - option(MNN_IMGCODECS "Enable imgcodecs" OFF) - option(MNN_OPENCV_TEST "Enable opencv test" OFF) - option(MNN_OPENCV_BENCH "Enable opencv benchmark" OFF) +# imgproc submodules start +option(MNN_IMGPROC_COLOR "Enable imgproc color" ON) +option(MNN_IMGPROC_GEOMETRIC "Enable imgproc geometric" ON) +option(MNN_IMGPROC_DRAW "Enable imgproc draw" ON) +option(MNN_IMGPROC_FILTER "Enable imgproc filter" ON) +option(MNN_IMGPROC_MISCELLANEOUS "Enable imgproc miscellaneous" ON) +option(MNN_IMGPROC_STRUCTRAL "Enable imgproc structral" ON) +option(MNN_IMGPROC_HISTOGRAMS "Enable imgproc histograms" ON) +# imgproc submodules end +option(MNN_CVCORE "Enable cv core" ON) +option(MNN_CALIB3D "Enable calib3d" ON) +option(MNN_IMGCODECS "Enable imgcodecs" OFF) +option(MNN_OPENCV_TEST "Enable opencv test" OFF) +option(MNN_OPENCV_BENCH "Enable opencv benchmark" OFF) - SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/../../) - include_directories(${CMAKE_CURRENT_LIST_DIR}/include) - include_directories(${CMAKE_CURRENT_LIST_DIR}/../../3rd_party/imageHelper/) +SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/../../) +include_directories(${CMAKE_CURRENT_LIST_DIR}/include) +include_directories(${CMAKE_CURRENT_LIST_DIR}/../../3rd_party/imageHelper/) - # include(${CMAKE_CURRENT_LIST_DIR}/test/CMakeLists.txt) - if(${MNN_OPENCV_TEST}) +# include(${CMAKE_CURRENT_LIST_DIR}/test/CMakeLists.txt) +if(${MNN_OPENCV_TEST}) add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/test) - endif() +endif() - if(${MNN_OPENCV_BENCH}) +if(${MNN_OPENCV_BENCH}) add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/benchmark) - endif() +endif() - if(${MNN_OPENCV_TEST} OR ${MNN_OPENCV_BENCH}) +if(${MNN_OPENCV_TEST} OR ${MNN_OPENCV_BENCH}) # copy test images FILE(COPY imgs DESTINATION ${CMAKE_BINARY_DIR}/) - endif() +endif() - # file(GLOB_RECURSE IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/*.hpp) - set(IMGPROC_SRC "") - # imgproc color functions - if(${MNN_IMGPROC_COLOR}) +# file(GLOB_RECURSE IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/*.hpp) +set(IMGPROC_SRC "") +# imgproc color functions +if(${MNN_IMGPROC_COLOR}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/color.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/color.hpp) - endif() - # imgproc geometric functions - if(${MNN_IMGPROC_GEOMETRIC}) +endif() +# imgproc geometric functions +if(${MNN_IMGPROC_GEOMETRIC}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/geometric.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/geometric.hpp) - endif() - # imgproc draw functions - if(${MNN_IMGPROC_DRAW}) +endif() +# imgproc draw functions +if(${MNN_IMGPROC_DRAW}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/draw.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/draw.hpp) - endif() - # imgproc filter functions - if(${MNN_IMGPROC_FILTER}) +endif() +# imgproc filter functions +if(${MNN_IMGPROC_FILTER}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/filter.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/filter.hpp) - endif() - # imgproc miscellaneous functions - if(${MNN_IMGPROC_MISCELLANEOUS}) +endif() +# imgproc miscellaneous functions +if(${MNN_IMGPROC_MISCELLANEOUS}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/miscellaneous.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/miscellaneous.hpp) - endif() - # imgproc structural functions - if(${MNN_IMGPROC_STRUCTRAL}) +endif() +# imgproc structural functions +if(${MNN_IMGPROC_STRUCTRAL}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/structural.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/structural.hpp) - endif() - # imgproc histograms functions - if(${MNN_IMGPROC_HISTOGRAMS}) +endif() +# imgproc histograms functions +if(${MNN_IMGPROC_HISTOGRAMS}) list(APPEND IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/histograms.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/histograms.hpp) - endif() - # calib3d functions - if(${MNN_CALIB3D}) +endif() +# calib3d functions +if(${MNN_CALIB3D}) add_definitions(-DMNN_CALIB3D) file(GLOB_RECURSE CALIB3D_SRC ${CMAKE_CURRENT_LIST_DIR}/source/calib3d/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/calib3d.hpp) - endif() - # imgcodecs functions - if(${MNN_IMGCODECS}) +endif() +# imgcodecs functions +if(${MNN_IMGCODECS}) add_definitions(-DMNN_IMGCODECS) file(GLOB_RECURSE IMGCODECS_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgcodecs/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgcodecs.hpp) - endif() - if(${MNN_CVCORE}) +endif() +if(${MNN_CVCORE}) add_definitions(-DMNN_CVCORE) file(GLOB_RECURSE CVCORE_SRC ${CMAKE_CURRENT_LIST_DIR}/source/core/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/core.hpp) - endif() +endif() - IF(MNN_SEP_BUILD) +IF(MNN_SEP_BUILD) IF(MNN_BUILD_SHARED_LIBS) - add_library(MNNOpenCV SHARED ${IMGPROC_SRC} ${CALIB3D_SRC} ${IMGCODECS_SRC} ${CVCORE_SRC}) - target_link_libraries(MNNOpenCV MNN MNN_Express) - ELSE() - add_library(MNNOpenCV STATIC ${IMGPROC_SRC} ${CALIB3D_SRC} ${IMGCODECS_SRC} ${CVCORE_SRC}) + add_library(MNNOpenCV SHARED ${IMGPROC_SRC} ${CALIB3D_SRC} ${IMGCODECS_SRC} ${CVCORE_SRC}) + target_link_libraries(MNNOpenCV MNN MNN_Express) ENDIF() - ELSE() +ELSE() add_library(MNNOpenCV OBJECT ${IMGPROC_SRC} ${CALIB3D_SRC} ${IMGCODECS_SRC} ${CVCORE_SRC}) - ENDIF() - IF(CMAKE_SYSTEM_NAME MATCHES "^Android" AND NOT MNN_BUILD_FOR_ANDROID_COMMAND) +ENDIF() +IF(CMAKE_SYSTEM_NAME MATCHES "^Android" AND NOT MNN_BUILD_FOR_ANDROID_COMMAND) IF(NOT NATIVE_INCLUDE_OUTPUT) - set(NATIVE_INCLUDE_OUTPUT ".") + set(NATIVE_INCLUDE_OUTPUT ".") ENDIF() add_custom_command( - TARGET MNNOpenCV - POST_BUILD - COMMAND ${CMAKE_COMMAND} - ARGS -E copy_directory ${CMAKE_CURRENT_LIST_DIR}/include ${NATIVE_INCLUDE_OUTPUT} + TARGET MNNOpenCV + POST_BUILD + COMMAND ${CMAKE_COMMAND} + ARGS -E copy_directory ${CMAKE_CURRENT_LIST_DIR}/include ${NATIVE_INCLUDE_OUTPUT} ) - ELSE() +ELSE() INSTALL(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/include/cv DESTINATION include FILES_MATCHING PATTERN *.hpp) - ENDIF() -ENDIF() \ No newline at end of file +ENDIF() diff --git a/tools/quantization/calibration.cpp b/tools/quantization/calibration.cpp index 66b6d39bb..d65af5493 100644 --- a/tools/quantization/calibration.cpp +++ b/tools/quantization/calibration.cpp @@ -462,7 +462,7 @@ Calibration::Calibration(MNN::NetT* model, const uint8_t* modelBuffer, const int mInputs = getModuleInputs(_calibrationFiles[0], _module->getInfo(), mInputNames); } else { mInputs.resize(1); - mInputs[0] = _Input(netInfo->inputs[0].dim, netInfo->inputs[0].order, netInfo->inputs[0].type); + mInputs[0] = _Input(mInputShape[mInputNames[0]], netInfo->inputs[0].order, netInfo->inputs[0].type); auto inputTensor = (MNN::Tensor*)mInputs[0]->getTensor(); Helper::preprocessInput(_process.get(), _preprocessConfig, _calibrationFiles[0], inputTensor, _inputType, mInputs[0]); } @@ -964,10 +964,8 @@ void Calibration::_computeQuantError() { inputs = getModuleInputs(file, netInfo, mInputNames); } else { inputs.resize(1); - inputs[0] = _Input(netInfo->inputs[0].dim, netInfo->inputs[0].order, netInfo->inputs[0].type); - inputs[0] = _Convert(inputs[0], netInfo->inputs[0].order); - auto inputTensor = (MNN::Tensor*)inputs[0]->getTensor(); - Helper::preprocessInput(_process.get(), _preprocessConfig, file, inputTensor, _inputType, mInputs[0]); + inputs[0] = _Input(mInputShape[mInputNames[0]], netInfo->inputs[0].order, netInfo->inputs[0].type); + Helper::preprocessInput(_process.get(), _preprocessConfig, file, (MNN::Tensor*)inputs[0]->getTensor(), _inputType, inputs[0]); } Express::Executor::getGlobalExecutor()->setCallBack(std::move(before), std::move(after)); _module->onForward(inputs); @@ -1292,6 +1290,8 @@ static unaryProc selectUnaryProc(int type) { return MNN::Express::_Hardswish; case UnaryOpOperation_GELU: return MNN::Express::_Gelu; + case UnaryOpOperation_SILU: + return MNN::Express::_Silu; default: MNN_ASSERT(false); break; diff --git a/tools/quantization/calibration.hpp b/tools/quantization/calibration.hpp index 6546ba57a..3fcdb0563 100644 --- a/tools/quantization/calibration.hpp +++ b/tools/quantization/calibration.hpp @@ -44,8 +44,8 @@ class Calibration { bool mValid = true; const int _binNums = 2048; int _calibrationFileNum = 0; - int _width; - int _height; + int _width = 1; + int _height = 1; int _channels; int _batch = 32; int _quant_bits = 8; diff --git a/tools/script/apply_gptq.py b/tools/script/apply_gptq.py deleted file mode 100644 index d3805a024..000000000 --- a/tools/script/apply_gptq.py +++ /dev/null @@ -1,201 +0,0 @@ -import json -import torch -import argparse - -class MNNWeight: - def __init__(self, name, external, weight_elements): - self.name = name - self.external = external - self.quant_bits = 4 - if round(weight_elements / external[1]) == 2: - self.quant_bits = 4 - self.a_min = -8 - else: - self.quant_bits = 8 - self.a_min = -128 - self.parse_name() - - def __repr__(self) -> str: - return f'{self.layer_id}.{self.op_id}.{self.block_id}, {self.external}' - - def parse_name(self): - parts = self.name.split('/') - if len(parts) > 4: - self.layer_id = parts[1].split('.')[1] - self.op_id = parts[2] + '.' + parts[3] - self.block_id = parts[-1].split('__')[-1] - else: - self.layer_id = -1 - self.op_id = parts[2] - self.block_id = parts[-1].split('__')[-1] - - def key(self): - if self.layer_id == -1: return self.op_id - return f'{self.layer_id}.{self.op_id}' - def idx(self): return int(self.block_id) - def offset(self): return self.external[0] - def weight_size(self): return self.external[1] - def scale_size(self): return self.external[2] - -def weight_reorder(qweight, bits=4, group_size=128): - oc = qweight.shape[-1] - wf = torch.tensor(list(range(0, 32, bits)), dtype=torch.int32).unsqueeze(0) - weight = torch.bitwise_right_shift(torch.unsqueeze(qweight, 1).expand(-1, 32 // bits, -1), wf.unsqueeze(-1)).to(torch.int16 if bits == 8 else torch.int8) - torch.bitwise_and(weight, (2 ** bits) - 1, out=weight) - weight = weight.reshape(-1, oc).transpose(1, 0) - if bits == 8: - weight = weight.to(torch.uint8) - return weight - weight = weight.reshape(-1, 2).to(torch.uint8) - weight = weight[:, 0] * 16 + weight[:, 1] - return weight - -class MNNModel: - def __init__(self, model, weight): - self.mnn_graph = json.load(open(model, 'rt')) - self.external_weight = weight - self.parse_conv() - - def parse_conv(self): - self.weights = [] - for op in self.mnn_graph['oplists']: - if op['type'] == 'Convolution': - name = op['name'] - external = op['main']['external'] - weight_elements = op['main']['common']['outputCount'] * op['main']['common']['inputCount'] - self.weights.append(MNNWeight(name, external, weight_elements)) - - def apply_weight_split(self, gptq_tensor): - bin_file = open(self.external_weight, 'r+b') - for mnn_weight in self.weights: - idx = mnn_weight.idx() - gptq_weight = gptq_tensor.get(mnn_weight.key()) - if gptq_weight is None: continue - print(f'write {mnn_weight.key()}.{idx} ... ', end='') - weight = gptq_weight.weight(idx) - scale = gptq_weight.scale(idx).float() - # write weight data - weight = weight_reorder(weight, mnn_weight.quant_bits) - weight_bytes = weight.numpy().tobytes() - weight_size = mnn_weight.weight_size() - header_len = weight_size - len(weight_bytes) - assert(header_len > 0) - bin_file.seek(mnn_weight.offset() + header_len) - bin_file.write(weight_bytes) - scale_size = mnn_weight.scale_size() - is_asy = scale.numel() * scale.element_size() < scale_size - # write scale data - if is_asy: - zeros = mnn_weight.a_min * scale - scale = torch.stack([zeros, scale], axis=-1) - scale_bytes = scale.numpy().tobytes() - assert(scale_size == len(scale_bytes)) - bin_file.write(scale_bytes) - print('Done!') - # break - bin_file.close() - - def apply_weight(self, gptq_tensor): - bin_file = open(self.external_weight, 'r+b') - for mnn_weight in self.weights: - gptq_weight = gptq_tensor.get(mnn_weight.key()) - if gptq_weight is None: continue - print(f'write {mnn_weight.key()} ... ', end='') - # print(f'mnn_weight.quant_bits = {mnn_weight.quant_bits}') - weight = gptq_weight.qweight - scale = gptq_weight.scales.float().transpose(1, 0) - # write weight data - weight = weight_reorder(weight, mnn_weight.quant_bits) - weight_bytes = weight.numpy().tobytes() - weight_size = mnn_weight.weight_size() - header_len = weight_size - len(weight_bytes) - assert(header_len > 0) - bin_file.seek(mnn_weight.offset() + header_len) - bin_file.write(weight_bytes) - scale_size = mnn_weight.scale_size() - is_asy = scale.numel() * scale.element_size() < scale_size - # write scale data - if is_asy: - zeros = mnn_weight.a_min * scale - scale = torch.stack([zeros, scale], axis=-1) - scale_bytes = scale.numpy().tobytes() - assert(scale_size == len(scale_bytes)) - bin_file.write(scale_bytes) - print('Done!') - bin_file.close() - - def apply(self, gptq_tensor): - if self.weights[0].block_id.isdigit(): - self.apply_weight_split(gptq_tensor) - else: - self.apply_weight(gptq_tensor) - -class GPTQWeight: - def __init__(self, name): - self.name = name - - def __repr__(self) -> str: - if hasattr(self, 'qweight'): - return f'{self.name}, {self.qweight.shape}, {self.scales.shape}' - return 'None' - - def add(self, name, tensor): - setattr(self, name, tensor) - - def weight(self, idx): - shape = self.qweight.shape - if len(shape) == 2: - ic, oc = shape - self.qweight = self.qweight.reshape(ic//16, 16, oc) - return self.qweight[idx] - - def scale(self, idx): - return self.scales[idx] - -class GPTQTensor: - def __init__(self, file): - self.file = file - self.load() - - def prefix(self, name): - splits = name.split('.') - if 'lm_head' in splits[0] and len(splits) == 2: - return splits[0], splits[1] - if len(splits) < 5: - return None, None - pre = f'{splits[2]}.{splits[3]}.{splits[4]}' - suf = splits[-1] - return pre, suf - - def __repr__(self) -> str: - return self.weight_dict.__repr__() - - def get(self, key : str): - if key in self.weight_dict: - return self.weight_dict[key] - return None - - def load(self): - self.weight_dict = dict() - from safetensors import safe_open - with safe_open(self.file, framework="pt") as f: - for k in f.keys(): - p, s = self.prefix(k) - if p is None: continue - if s not in ['qweight', 'scales']: continue - if p not in self.weight_dict: - self.weight_dict[p] = GPTQWeight(p) - self.weight_dict[p].add(s, f.get_tensor(k)) - -def main(args): - mnn_model = MNNModel(args.mnn_graph, args.mnn_weight) - gptq_weight = GPTQTensor(args.gptq_tensor) - mnn_model.apply(gptq_weight) - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='apply_gptq', formatter_class=argparse.RawTextHelpFormatter) - parser.add_argument('--mnn_graph', type=str, required=True, help='mnn graph json path.') - parser.add_argument('--mnn_weight', type=str, required=True, help='mnn weight file path.') - parser.add_argument('--gptq_tensor', type=str, required=True, help='gptq tensor path.') - args = parser.parse_args() - main(args) diff --git a/tools/script/convertTfTest.py b/tools/script/convertTfTest.py index e178409c9..14a726341 100755 --- a/tools/script/convertTfTest.py +++ b/tools/script/convertTfTest.py @@ -25,7 +25,10 @@ def run_cmd(args): if name == '.DS_Store': continue print(name) - message = run_cmd(['./TestConvertResult', 'Tf', root_dir + '/' + name]) + cmd = './MNNConvert -f TF --MNNModel convert_cache.mnn --useGeluApproximation=0 --saveExternalData --keepInputFormat=1 --bizCode Test' + cmd += ' --modelFile ' + os.path.join(root_dir, name, "test.pb") + cmd += ' --testdir ' + os.path.join(root_dir, name) + message = run_cmd([cmd]) if (message.find('TEST_SUCCESS') == -1): gWrong.append(name) print(message) diff --git a/tools/script/testMNNFromOnnx.py b/tools/script/testMNNFromOnnx.py index 8b032c86a..fbed8812d 100644 --- a/tools/script/testMNNFromOnnx.py +++ b/tools/script/testMNNFromOnnx.py @@ -151,6 +151,8 @@ def __run_onnx(self): else: # Float inputs[inputVar.name] = np.random.uniform(0.1, 1.2, shapes).astype(np.float32) + if inputVar.type.find("float16") >= 0: + inputs[inputVar.name] = inputs[inputVar.name].astype(np.float16) jsonDict['inputs'].append(inp) print([output.name for output in self.model.graph.output]) for output in self.model.graph.output: diff --git a/tools/train/source/nn/NN.cpp b/tools/train/source/nn/NN.cpp index 8d49f6ada..b52b130f0 100644 --- a/tools/train/source/nn/NN.cpp +++ b/tools/train/source/nn/NN.cpp @@ -9,6 +9,7 @@ #include #include "NN.hpp" #include "Distributions.hpp" +#include "module/ModuleInside.hpp" #include "module/PipelineModule.hpp" #include "module/WhileModule.hpp" #include "module/IfModule.hpp" diff --git a/transformers/diffusion/CMakeLists.txt b/transformers/diffusion/CMakeLists.txt deleted file mode 100644 index b8b7460cb..000000000 --- a/transformers/diffusion/CMakeLists.txt +++ /dev/null @@ -1,14 +0,0 @@ -cmake_minimum_required(VERSION 3.10) - -# source files -FILE(GLOB SRCS ${CMAKE_CURRENT_LIST_DIR}/*.cpp) -include_directories(${CMAKE_CURRENT_LIST_DIR}/../../tools/cv/include) -add_executable(diffusion_demo ${SRCS}) -IF(MNN_SEP_BUILD) -target_link_libraries(diffusion_demo MNN MNN_Express MNNOpenCV) -IF(MNN_OPENCL) - target_link_libraries(diffusion_demo MNN_CL) -ENDIF() -ELSE() -target_link_libraries(diffusion_demo MNN) -ENDIF() diff --git a/transformers/diffusion/engine/CMakeLists.txt b/transformers/diffusion/engine/CMakeLists.txt new file mode 100644 index 000000000..bb98163a2 --- /dev/null +++ b/transformers/diffusion/engine/CMakeLists.txt @@ -0,0 +1,24 @@ +cmake_minimum_required(VERSION 3.10) + +# source files +FILE(GLOB SRCS ${CMAKE_CURRENT_LIST_DIR}/src/*.cpp) +include_directories(${CMAKE_SOURCE_DIR}/tools/cv/include/) + +include_directories(${CMAKE_CURRENT_LIST_DIR}/include/) + +if (MNN_SEP_BUILD) + if (MNN_BUILD_SHARED_LIBS) + # compile dynamic so, support Linux/Mac + add_library(diffusion SHARED ${SRCS}) + set_target_properties(diffusion PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS TRUE) + target_link_libraries(diffusion ${MNN_DEPS}) + else() + add_library(diffusion STATIC ${SRCS}) + endif() + list(APPEND MNN_DEPS diffusion) +else() + add_library(diffusion OBJECT ${SRCS}) +endif() + +add_executable(diffusion_demo ${CMAKE_CURRENT_LIST_DIR}/diffusion_demo.cpp) +target_link_libraries(diffusion_demo ${MNN_DEPS}) \ No newline at end of file diff --git a/transformers/diffusion/engine/diffusion_demo.cpp b/transformers/diffusion/engine/diffusion_demo.cpp new file mode 100644 index 000000000..bc3635bcc --- /dev/null +++ b/transformers/diffusion/engine/diffusion_demo.cpp @@ -0,0 +1,73 @@ +#include +#include "diffusion/diffusion.hpp" +#define MNN_OPEN_TIME_TRACE +#include +#include +using namespace MNN::DIFFUSION; + +int main(int argc, const char* argv[]) { + if (argc < 8) { + MNN_PRINT("=====================================================================================================================\n"); + MNN_PRINT("Usage: ./diffusion_demo \n"); + MNN_PRINT("=====================================================================================================================\n"); + return 0; + } + + auto resource_path = argv[1]; + auto model_type = (DiffusionModelType)atoi(argv[2]); + auto memory_mode = atoi(argv[3]); + auto backend_type = (MNNForwardType)atoi(argv[4]); + auto iteration_num = atoi(argv[5]); + auto img_name = argv[6]; + + std::string input_text; + for (int i = 7; i < argc; ++i) { + input_text += argv[i]; + if (i < argc - 1) { + input_text += " "; + } + } + + MNN_PRINT("Model resource path: %s\n", resource_path); + if(model_type == STABLE_DIFFUSION_1_5) { + MNN_PRINT("Model type is stable diffusion 1.5\n"); + } else if (model_type == STABLE_DIFFUSION_TAIYI_CHINESE) { + MNN_PRINT("Model type is stable diffusion taiyi chinese version\n"); + } else { + MNN_PRINT("Error: Model type %d not supported, please check\n", (int)model_type); + } + + if(memory_mode == 0) { + MNN_PRINT("(Memory Lack) Each diffusion model will be initialized when using, freed after using. with slow initialization\n"); + } else { + MNN_PRINT("(Memory Enough) All Diffusion models will be initialized when application enter. with fast initialization\n"); + } + MNN_PRINT("Backend type: %d\n", (int)backend_type); + MNN_PRINT("Output image name: %s\n", img_name); + MNN_PRINT("Prompt text: %s\n", input_text.c_str()); + + + std::unique_ptr diffusion(Diffusion::createDiffusion(resource_path, model_type, backend_type, memory_mode, iteration_num)); + + diffusion->load(); + + // callback to show progress + auto progressDisplay = [](int progress) { + std::cout << "Progress: " << progress << "%" << std::endl; + }; + diffusion->run(input_text, img_name, progressDisplay); + + /* + when need multi text-generation-image: + if you choose memory lack mode, need diffusion load with each diffusion run. + if you choose memory enough mode, just start another diffusion run, only need diffusion load in first time. + */ + while(0) { + if(memory_mode == 0) { + diffusion->load(); + } + + diffusion->run("a big horse", "demo_2.jpg", progressDisplay); + } + return 0; +} diff --git a/transformers/diffusion/pipeline.hpp b/transformers/diffusion/engine/include/diffusion/diffusion.hpp similarity index 55% rename from transformers/diffusion/pipeline.hpp rename to transformers/diffusion/engine/include/diffusion/diffusion.hpp index 3ce1e95f0..19c81b61d 100644 --- a/transformers/diffusion/pipeline.hpp +++ b/transformers/diffusion/engine/include/diffusion/diffusion.hpp @@ -1,3 +1,12 @@ +// +// diffusion.hpp +// +// Created by MNN on 2025/01/12. +// MNN +// +#ifndef MNN_DIFFUSION_HPP +#define MNN_DIFFUSION_HPP + #include #include #include @@ -9,24 +18,28 @@ using namespace MNN; using namespace MNN::Express; -namespace diffusion { +namespace MNN { +namespace DIFFUSION { +class Tokenizer; typedef enum { STABLE_DIFFUSION_1_5 = 0, STABLE_DIFFUSION_TAIYI_CHINESE = 1, DIFFUSION_MODEL_USER } DiffusionModelType; - -class Pipeline { + +class MNN_PUBLIC Diffusion { public: - Pipeline(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode); - ~Pipeline() = default; - bool run(const std::string& prompt, const std::string& imagePath); + Diffusion(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode, int iterationNum); + virtual ~Diffusion(); + static Diffusion* createDiffusion(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode, int iterationNum); + + bool run(const std::string prompt, const std::string imagePath, std::function progressCallback); + bool load(); private: - bool load_modules(); VARP step_plms(VARP sample, VARP model_output, int index); VARP text_encoder(const std::vector& ids); - VARP unet(VARP text_embeddings); + VARP unet(VARP text_embeddings, std::function progressCallback); VARP vae_decoder(VARP latent); private: std::shared_ptr runtime_manager_; @@ -37,13 +50,18 @@ class Pipeline { std::vector mEts; VARP mSample; VARP mLatentVar, mPromptVar, mTimestepVar, mSampleVar; - + std::vector mInitNoise; + private: std::string mModelPath; DiffusionModelType mModelType; int mMaxTextLen = 77; int mMemoryMode; + int mIterationNum; MNNForwardType mBackendType; + std::unique_ptr mTokenizer; }; } +} +#endif diff --git a/transformers/diffusion/scheduler/alphas.txt b/transformers/diffusion/engine/scheduler/alphas.txt similarity index 100% rename from transformers/diffusion/scheduler/alphas.txt rename to transformers/diffusion/engine/scheduler/alphas.txt diff --git a/transformers/diffusion/pipeline.cpp b/transformers/diffusion/engine/src/diffusion.cpp similarity index 64% rename from transformers/diffusion/pipeline.cpp rename to transformers/diffusion/engine/src/diffusion.cpp index 7ac441f04..64a0adbcb 100644 --- a/transformers/diffusion/pipeline.cpp +++ b/transformers/diffusion/engine/src/diffusion.cpp @@ -1,7 +1,7 @@ #include #include #include -#include "pipeline.hpp" +#include "diffusion/diffusion.hpp" #include "tokenizer.hpp" #define MNN_OPEN_TIME_TRACE #include @@ -22,8 +22,9 @@ using namespace CV; -namespace diffusion { - +namespace MNN { +namespace DIFFUSION { + void display_progress(int cur, int total){ putchar('\r'); MNN_PRINT("["); @@ -35,11 +36,16 @@ void display_progress(int cur, int total){ fflush(stdout); } -Pipeline::Pipeline(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode) : - mModelPath(modelPath), mModelType(modelType), mBackendType(backendType), mMemoryMode(memoryMode) { +Diffusion* Diffusion::createDiffusion(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode, int iterationNum) { + Diffusion* diffusion = new Diffusion(modelPath, modelType, backendType, memoryMode, iterationNum); + return diffusion; +} + +Diffusion::Diffusion(std::string modelPath, DiffusionModelType modelType, MNNForwardType backendType, int memoryMode, int iterationNum) : +mModelPath(modelPath), mModelType(modelType), mBackendType(backendType), mMemoryMode(memoryMode), mIterationNum(iterationNum) { if(modelType == STABLE_DIFFUSION_1_5) { mMaxTextLen = 77; - } else if(modelType == diffusion::STABLE_DIFFUSION_TAIYI_CHINESE) { + } else if(modelType == STABLE_DIFFUSION_TAIYI_CHINESE) { mMaxTextLen = 512; } std::ifstream alphaFile(modelPath + "/alphas.txt"); @@ -48,29 +54,27 @@ Pipeline::Pipeline(std::string modelPath, DiffusionModelType modelType, MNNForwa while (alphaFile >> alpha) { mAlphas.push_back(alpha); } -#if 0 // Different steps setting - mTimeSteps = { // 50 steps - 981, 961, 961, 941, 921, 901, 881, 861, 841, 821, 801, 781, 761, 741, - 721, 701, 681, 661, 641, 621, 601, 581, 561, 541, 521, 501, 481, 461, - 441, 421, 401, 381, 361, 341, 321, 301, 281, 261, 241, 221, 201, 181, - 161, 141, 121, 101, 81, 61, 41, 21, 1 - }; - mTimeSteps = { // 20 steps - 951, 901, 901, 851, 801, 751, 701, 651, 601, 551, 501, 451, - 401, 351, 301, 251, 201, 151, 101, 51, 1 - }; - mTimeSteps = { // 5 steps - 801, 601, 601, - 401, 201, 1 - }; -#endif - mTimeSteps = { - 951, 901, 901, 851, 801, 751, 701, 651, 601, 551, 501, 451, - 401, 351, 301, 251, 201, 151, 101, 51, 1 - }; + if(iterationNum > 50) { + iterationNum = 50; + MNN_PRINT("too much number of iterations, iterations will be set to 50.\n"); + } + if(iterationNum < 1) { + iterationNum = 10; + MNN_PRINT("illegal number of iterations, iterations will be set to 10.\n"); + } + mTimeSteps.resize(iterationNum + 1); + int step = 1000 / (iterationNum + 1); + for(int i = iterationNum; i >= 0; i--) { + mTimeSteps[i] = 1 + (iterationNum - i) * step; + } } - -bool Pipeline::load_modules() { + +Diffusion::~Diffusion() { + mModules.clear(); + runtime_manager_.reset(); +} + +bool Diffusion::load() { AUTOTIME; ScheduleConfig config; BackendConfig backendConfig; @@ -83,19 +87,19 @@ bool Pipeline::load_modules() { } else { config.numThread = 1; } - + backendConfig.precision = BackendConfig::Precision_Low; config.backendConfig = &backendConfig; - + auto exe = ExecutorScope::Current(); exe->lazyEval = false; exe->setGlobalExecutorConfig(config.type, backendConfig, config.numThread); - + Module::Config module_config; module_config.shapeMutable = false; // module_config.rearrange = true; runtime_manager_.reset(Executor::RuntimeManager::createRuntimeManager(config)); - + if (config.type == MNN_FORWARD_OPENCL) { const char* cacheFileName = ".tempcache"; runtime_manager_->setCache(cacheFileName); @@ -106,8 +110,10 @@ bool Pipeline::load_modules() { mTimestepVar = _Input({1}, NCHW, halide_type_of()); mSampleVar = _Concat({mLatentVar, mLatentVar}, 0); - MNN_PRINT("First time initilizing may cost a few seconds to create cachefile, please wait ...\n"); - + if(mMemoryMode > 0) { + MNN_PRINT("First time initilizing may cost a few seconds to create cachefile, please wait ...\n"); + } + VARP text_embeddings; mModules.resize(3); // load text_encoder model @@ -115,12 +121,12 @@ bool Pipeline::load_modules() { std::string model_path = mModelPath + "/text_encoder.mnn"; MNN_PRINT("Load %s\n", model_path.c_str()); mModules[0].reset(Module::load( - {"input_ids"}, {"last_hidden_state", "pooler_output"}, model_path.c_str(), runtime_manager_, &module_config)); + {"input_ids"}, {"last_hidden_state", "pooler_output"}, model_path.c_str(), runtime_manager_, &module_config)); - if(mMemoryMode > 0) { + if(mMemoryMode > 0) { auto outputs = mModules[0]->onForward({mPromptVar}); text_embeddings = _Convert(outputs[0], NCHW); - } + } display_progress(1, 3); } // load unet model @@ -128,12 +134,12 @@ bool Pipeline::load_modules() { std::string model_path = mModelPath + "/unet.mnn"; MNN_PRINT("Load %s\n", model_path.c_str()); mModules[1].reset(Module::load( - {"sample", "timestep", "encoder_hidden_states"}, {"out_sample"}, model_path.c_str(), runtime_manager_, &module_config)); + {"sample", "timestep", "encoder_hidden_states"}, {"out_sample"}, model_path.c_str(), runtime_manager_, &module_config)); - if(mMemoryMode > 0) { + if(mMemoryMode > 0) { auto outputs = mModules[1]->onForward({mSampleVar, mTimestepVar, text_embeddings}); auto output = _Convert(outputs[0], NCHW); - } + } display_progress(2, 3); } // load vae_decoder model @@ -141,19 +147,29 @@ bool Pipeline::load_modules() { std::string model_path = mModelPath + "/vae_decoder.mnn"; MNN_PRINT("Load %s\n", model_path.c_str()); mModules[2].reset(Module::load( - {"latent_sample"}, {"sample"}, model_path.c_str(), runtime_manager_, &module_config)); + {"latent_sample"}, {"sample"}, model_path.c_str(), runtime_manager_, &module_config)); - if(mMemoryMode > 0) { - auto outputs = mModules[2]->onForward({mLatentVar}); - auto output = _Convert(outputs[0], NCHW); - } - display_progress(3, 3); + if(mMemoryMode > 0) { + auto outputs = mModules[2]->onForward({mLatentVar}); + auto output = _Convert(outputs[0], NCHW); + // sync + output->readMap(); + } + display_progress(3, 3); } - + + // tokenizer loading + if(mModelType == STABLE_DIFFUSION_1_5) { + mTokenizer.reset(new CLIPTokenizer); + } else if(mModelType == STABLE_DIFFUSION_TAIYI_CHINESE) { + mTokenizer.reset(new BertTokenizer); + } + mTokenizer->load(mModelPath); + return true; } -VARP Pipeline::text_encoder(const std::vector& ids) { +VARP Diffusion::text_encoder(const std::vector& ids) { AUTOTIME; memcpy((void *)mPromptVar->writeMap(), ids.data(), 2*mMaxTextLen*sizeof(int)); @@ -172,7 +188,7 @@ VARP Pipeline::text_encoder(const std::vector& ids) { return output; } -VARP Pipeline::step_plms(VARP sample, VARP model_output, int index) { +VARP Diffusion::step_plms(VARP sample, VARP model_output, int index) { int timestep = mTimeSteps[index]; int prev_timestep = 0; if (index + 1 < mTimeSteps.size()) { @@ -210,95 +226,105 @@ VARP Pipeline::step_plms(VARP sample, VARP model_output, int index) { return prev_sample; } -VARP Pipeline::unet(VARP text_embeddings) { - mModules[0].reset(); - std::mt19937 rng; - rng.seed(std::random_device()()); - std::normal_distribution normal(0, 1); - std::vector initVal(16384); - +VARP Diffusion::unet(VARP text_embeddings, std::function progressCallback) { + if(mMemoryMode == 0) { + mModules[0].reset(); + } + if(mInitNoise.size() != 16384) { + mInitNoise.resize(16384); + } #ifdef MNN_DUMP_DATA std::ostringstream fileName; fileName << "random.txt"; std::ifstream input(fileName.str().c_str()); for (int i = 0; i < 16384; ++i) { - input >> initVal[i]; + input >> mInitNoise[i]; } #else + std::mt19937 rng; + rng.seed(std::random_device()()); + std::normal_distribution normal(0, 1); for (int i = 0; i < 16384; i++) { - initVal[i] = normal(rng); + mInitNoise[i] = normal(rng); } #endif - memcpy((void *)mLatentVar->writeMap(), initVal.data(), 16384*sizeof(float)); - + memcpy((void *)mLatentVar->writeMap(), mInitNoise.data(), 16384*sizeof(float)); + VARP scalevar = _Input({1}, NCHW, halide_type_of()); auto scaleptr = scalevar->writeMap(); scaleptr[0] = 7.5; - + auto floatVar = _Input({1}, NCHW, halide_type_of()); auto ptr = floatVar->writeMap(); - + auto plms = mLatentVar; + for (int i = 0; i < mTimeSteps.size(); i++) { AUTOTIME; - display_progress(i, mTimeSteps.size()); - + //display_progress(i, mTimeSteps.size()); + int timestep = mTimeSteps[i]; - ptr[0] = timestep; auto temp = _Cast(floatVar, halide_type_of()); mTimestepVar->input(temp); - mSampleVar = _Concat({mLatentVar, mLatentVar}, 0); + mSampleVar = _Concat({plms, plms}, 0); auto outputs = mModules[1]->onForward({mSampleVar, mTimestepVar, text_embeddings}); auto output = _Convert(outputs[0], NCHW); - + auto noise_pred = output; - auto splitvar = _Split(noise_pred, {2}, 0); - auto noise_pred_uncond = splitvar[0]; - auto noise_pred_text = splitvar[1]; - + auto splitvar = _Split(noise_pred, {2}, 0); + auto noise_pred_uncond = splitvar[0]; + auto noise_pred_text = splitvar[1]; + noise_pred = scalevar * (noise_pred_text - noise_pred_uncond) + noise_pred_uncond; - - mLatentVar = step_plms(mLatentVar, noise_pred, i); - - // mLatentVar.fix(VARP::CONSTANT); + + plms = step_plms(plms, noise_pred, i); + #ifdef MNN_DUMP_DATA auto xx = output->readMap(); auto yy = mSampleVar->readMap(); auto zz = text_embeddings->readMap(); + auto mm = mTimestepVar->readMap(); for(int i=0; i<6; i+=2) { - MNN_PRINT("%f %f %f ", xx[i], yy[i], zz[i]); + MNN_PRINT("(0)%f (1)%f (2)%f (3)%d ", xx[i], yy[i], zz[i] ,mm[0]); } + MNN_PRINT("\n"); for(int i=0; i<6; i+=2) { - MNN_PRINT("%f %f %f ", xx[16384+i], yy[16384+i], zz[mMaxTextLen*768+i]); + MNN_PRINT("(0)%f (1)%f (2)%f ", xx[16384+i], yy[16384+i], zz[mMaxTextLen*768+i]); } MNN_PRINT("\n\n"); #endif + if (progressCallback) { + progressCallback((2 + i) * 100 / (mIterationNum + 3)); // percent + } + } - mLatentVar.fix(VARP::CONSTANT); + plms.fix(VARP::CONSTANT); #ifdef MNN_DUMP_DATA - auto xx = mLatentVar->readMap(); + auto xx = plms->readMap(); for(int i=0; i<10; i+=2) { MNN_PRINT("%f ", xx[i]); } MNN_PRINT("\n\n"); #endif - return mLatentVar; + return plms; } -VARP Pipeline::vae_decoder(VARP latent) { - mModules[1].reset(); +VARP Diffusion::vae_decoder(VARP latent) { + if(mMemoryMode == 0) { + mModules[1].reset(); + } latent = latent * _Const(1 / 0.18215); AUTOTIME; auto outputs = mModules[2]->onForward({latent}); auto output = _Convert(outputs[0], NCHW); - + #ifdef MNN_DUMP_DATA auto xx = output->readMap(); for(int i=0; i<320; i+=32) { @@ -316,27 +342,32 @@ VARP Pipeline::vae_decoder(VARP latent) { return image; } -bool Pipeline::run(const std::string& prompt, const std::string& imagePath) { - std::unique_ptr tok; - if(mModelType == STABLE_DIFFUSION_1_5) { - tok.reset(new diffusion::CLIPTokenizer); - } else if(mModelType == STABLE_DIFFUSION_TAIYI_CHINESE) { - tok.reset(new diffusion::BertTokenizer); - } - tok->load(mModelPath); - load_modules(); - +bool Diffusion::run(const std::string prompt, const std::string imagePath, std::function progressCallback) { AUTOTIME; - auto ids = tok->encode(prompt, mMaxTextLen); + mEts.clear(); + + auto ids = mTokenizer->encode(prompt, mMaxTextLen); auto text_embeddings = text_encoder(ids); - auto latent = unet(text_embeddings); - + if (progressCallback) { + progressCallback(1 * 100 / (mIterationNum + 3)); // percent + } + auto latent = unet(text_embeddings, progressCallback); + auto image = vae_decoder(latent); bool res = imwrite(imagePath, image); if (res) { MNN_PRINT("SUCCESS! write generated image to %s\n", imagePath.c_str()); } + + if(mMemoryMode == 0) { + mModules[2].reset(); + } + + if (progressCallback) { + progressCallback(100); // percent + } return true; } } +} diff --git a/transformers/diffusion/tokenizer.cpp b/transformers/diffusion/engine/src/tokenizer.cpp similarity index 91% rename from transformers/diffusion/tokenizer.cpp rename to transformers/diffusion/engine/src/tokenizer.cpp index f64e982ef..ffeeeac6c 100644 --- a/transformers/diffusion/tokenizer.cpp +++ b/transformers/diffusion/engine/src/tokenizer.cpp @@ -12,8 +12,9 @@ #include "rapidjson/document.h" #include "rapidjson/filereadstream.h" #include "rapidjson/error/en.h" -namespace diffusion { - +namespace MNN { +namespace DIFFUSION { + bool BertTokenizer::load(const std::string& dictPath) { std::ifstream dictFile(dictPath + "/vocab.txt"); if(!dictFile.is_open()) { @@ -64,7 +65,7 @@ std::vector BertTokenizer::word_piece(const std::string& token) { return ids; } - + std::vector BertTokenizer::encode(const std::string& str, int maxlen) { std::vector ids(maxlen * 2, 0); // uncond @@ -73,7 +74,7 @@ std::vector BertTokenizer::encode(const std::string& str, int maxlen) { // ids int idx = maxlen; ids[idx++] = mStartIdx; - + std::vector tokens; std::string current_token; size_t i = 0; @@ -117,23 +118,23 @@ std::vector BertTokenizer::encode(const std::string& str, int maxlen) { tokens.push_back(current_token); } } - + for (auto token : tokens) { for (auto id : word_piece(token)) { ids[idx++] = id; } } - + ids[idx++] = mEndIdx; return ids; } - + bool CLIPTokenizer::load(const std::string& filePath) { bool res_0 = loadVocab(filePath + "/vocab.json"); bool res_1 = loadMerges(filePath + "/merges.txt"); return res_0 && res_1; } - + std::wstring utf8_to_wstring(const std::string& str) { std::wstring_convert> myconv; return myconv.from_bytes(str); @@ -150,47 +151,47 @@ bool CLIPTokenizer::loadVocab(const std::string& vocabFilePath) { MNN_ERROR("File %s open failed.\n", vocabFilePath.c_str()); return false; } - + char readBuffer[65536]; rapidjson::FileReadStream is(fp, readBuffer, sizeof(readBuffer)); - + rapidjson::Document doc; doc.ParseStream(is); fclose(fp); - + if (doc.HasParseError()) { MNN_ERROR("JSON parse error: %s\n", rapidjson::GetParseError_En(doc.GetParseError())); return false; } - + if (!doc.IsObject()) { MNN_ERROR("JSON is not an object.\n"); return false; } - + for (rapidjson::Value::ConstMemberIterator itr = doc.MemberBegin(); itr != doc.MemberEnd(); ++itr) { const char* key = itr->name.GetString(); if (itr->value.IsInt()) { int intValue = itr->value.GetInt(); mVocabs[std::string(key)] = intValue; -// std::cout << key << ": " << intValue << std::endl; + // std::cout << key << ": " << intValue << std::endl; } else { MNN_ERROR("Value for key: %s is not an integer.\n", key); } } auto _insert_range = [=](int start, int end) { - for (int c = start; c <= end; c++) { - b2u_.insert({uint8_t(c), wchar_t(c)}); - } - }; + for (int c = start; c <= end; c++) { + b2u_.insert({uint8_t(c), wchar_t(c)}); + } + }; b2u_.clear(); _insert_range(L'!', L'~'); _insert_range(L'¡', L'¬'); _insert_range(L'®', L'ÿ'); - + int n = 0; for (int b = 0; b < 256; b++) { if (b2u_.find(uint8_t(b)) == b2u_.end()) { @@ -208,7 +209,7 @@ bool CLIPTokenizer::loadVocab(const std::string& vocabFilePath) { bool CLIPTokenizer::loadMerges(const std::string& mergesFilePath) { std::ifstream file(mergesFilePath); std::string line; - + if (!file.is_open()) { MNN_ERROR("Failed to open merges file: %s\n", mergesFilePath.c_str()); return false; @@ -218,7 +219,7 @@ bool CLIPTokenizer::loadMerges(const std::string& mergesFilePath) { if (line.empty() || line[0] == '#') { continue; } - + size_t colonPos = line.find(' '); if (colonPos != std::string::npos) { std::string token_0 = line.substr(0, colonPos); @@ -228,12 +229,12 @@ bool CLIPTokenizer::loadMerges(const std::string& mergesFilePath) { } return true; } - + void get_pairs(const std::wstring& word, std::vector>* pairs) { pairs->clear(); - + if (word.size() < 2) return; - + wchar_t previous = word[0]; for (int i = 1; i < word.size(); i++) { pairs->push_back({std::wstring(1, previous), std::wstring(1, word[i])}); @@ -245,49 +246,49 @@ void CLIPTokenizer::bpe(const std::wstring& token, const BPERanks& bpe_ranks, st std::set merged; // records indices in pairs that were merged. auto _left = [](int i, std::set& merged) { for (int j = i - 1; j >= -1; j--) { - if (merged.find(j) == merged.end()) return j; + if (merged.find(j) == merged.end()) return j; } return -1; }; auto _right = [](int i, int cap, std::set& merged) { for (int j = i + 1; j < cap; j++) { - if (merged.find(j) == merged.end()) return j; + if (merged.find(j) == merged.end()) return j; } return cap; }; - + std::vector> pairs; get_pairs(token, &pairs); - + while (true) { int min_score = INT_MAX; int to_merge = -1; // indices into pairs. - + for (int i = 0; i < pairs.size(); ++i) { - if (merged.find(i) == merged.end()) { // pair i is not merged. - auto iter = bpe_ranks.find(pairs[i]); - int score = iter != bpe_ranks.end() ? iter->second : INT_MAX; - if (score < min_score) { - min_score = score; - to_merge = i; + if (merged.find(i) == merged.end()) { // pair i is not merged. + auto iter = bpe_ranks.find(pairs[i]); + int score = iter != bpe_ranks.end() ? iter->second : INT_MAX; + if (score < min_score) { + min_score = score; + to_merge = i; + } } } - } - + if (to_merge == -1) break; - + merged.insert(to_merge); std::wstring merge_into = pairs[to_merge].first + pairs[to_merge].second; - + int l = _left(to_merge, merged); if (l >= 0) pairs[l].second = merge_into; int r = _right(to_merge, pairs.size(), merged); if (r < pairs.size()) pairs[r].first = merge_into; } // end while (true) - + if (merged.size() == pairs.size()) { result->push_back(token); - + } else { for (int i = 0; i < pairs.size(); ++i) { if (merged.find(i) == merged.end()) { @@ -315,12 +316,12 @@ std::vector CLIPTokenizer::encode(const std::string& text, int maxlen) { } std::vector bpe_tokens; bpe(wtoken, bpe_ranks_, &bpe_tokens); - + for (auto ws : bpe_tokens) { result.push_back(wstring_to_utf8(ws)); } } - + std::vector ids(maxlen * 2, 0); // uncond ids[0] = mStartIdx; @@ -332,8 +333,8 @@ std::vector CLIPTokenizer::encode(const std::string& text, int maxlen) { ids[idx++] = mVocabs.at(s); } ids[idx++] = mEndIdx; - + return ids; } - +} } // diffusion diff --git a/transformers/diffusion/tokenizer.hpp b/transformers/diffusion/engine/src/tokenizer.hpp similarity index 70% rename from transformers/diffusion/tokenizer.hpp rename to transformers/diffusion/engine/src/tokenizer.hpp index 5c7d76e40..8349e50d0 100644 --- a/transformers/diffusion/tokenizer.hpp +++ b/transformers/diffusion/engine/src/tokenizer.hpp @@ -2,8 +2,12 @@ #include #include -namespace diffusion { +#ifndef MNN_DIFFUSION_TOKENIZER_HPP +#define MNN_DIFFUSION_TOKENIZER_HPP +namespace MNN { +namespace DIFFUSION { + class Tokenizer { public: Tokenizer() = default; @@ -11,7 +15,7 @@ class Tokenizer { virtual bool load(const std::string& filePath) = 0; virtual std::vector encode(const std::string& sentence, int maxlen = 0) = 0; }; - + class BertTokenizer : public Tokenizer{ public: BertTokenizer() = default; @@ -25,16 +29,16 @@ class BertTokenizer : public Tokenizer{ }; class CLIPTokenizer : public Tokenizer{ -struct hash_pair_wstring { - size_t operator()(const std::pair& p) const { - auto hash1 = std::hash{}(p.first); - auto hash2 = std::hash{}(p.second); - // If hash1 == hash2, their XOR is zero. - return (hash1 != hash2) ? hash1 ^ hash2 : hash1; - } -}; -using BPERanks = std::unordered_map, int, hash_pair_wstring>; - + struct hash_pair_wstring { + size_t operator()(const std::pair& p) const { + auto hash1 = std::hash{}(p.first); + auto hash2 = std::hash{}(p.second); + // If hash1 == hash2, their XOR is zero. + return (hash1 != hash2) ? hash1 ^ hash2 : hash1; + } + }; + using BPERanks = std::unordered_map, int, hash_pair_wstring>; + public: CLIPTokenizer() = default; virtual bool load(const std::string& filePath) override; @@ -43,7 +47,7 @@ using BPERanks = std::unordered_map, int, private: bool loadVocab(const std::string& vocabFilePath); bool loadMerges(const std::string& mergesFilePath); - + private: void bpe(const std::wstring& token, const BPERanks& bpe_ranks, std::vector* result); BPERanks bpe_ranks_; @@ -55,5 +59,6 @@ using BPERanks = std::unordered_map, int, int mStartIdx = 49406; int mEndIdx = 49407; }; - +} } // diffusion +#endif diff --git a/transformers/diffusion/main.cpp b/transformers/diffusion/main.cpp deleted file mode 100644 index 75abd8924..000000000 --- a/transformers/diffusion/main.cpp +++ /dev/null @@ -1,47 +0,0 @@ -#include -#include "pipeline.hpp" - -int main(int argc, const char* argv[]) { - if (argc < 7) { - MNN_PRINT("=====================================================================================================================\n"); - MNN_PRINT("Usage: ./diffusion_demo \n"); - MNN_PRINT("=====================================================================================================================\n"); - return 0; - } - - auto resource_path = argv[1]; - auto model_type = (diffusion::DiffusionModelType)atoi(argv[2]); - auto img_name = argv[3]; - auto memory_mode = atoi(argv[4]); - auto backend_type = (MNNForwardType)atoi(argv[5]); - std::string input_text; - for (int i = 6; i < argc; ++i) { - input_text += argv[i]; - if (i < argc - 1) { - input_text += " "; - } - } - - MNN_PRINT("Model resource path: %s\n", resource_path); - if(model_type == diffusion::STABLE_DIFFUSION_1_5) { - MNN_PRINT("Model type is stable diffusion 1.5\n"); - } else if (model_type == diffusion::STABLE_DIFFUSION_TAIYI_CHINESE) { - MNN_PRINT("Model type is stable diffusion taiyi chinese version\n"); - } else { - MNN_PRINT("Error: Model type %d not supported, please check\n", (int)model_type); - } - - if(memory_mode == 0) { - MNN_PRINT("(Memory Lack) Each diffusion model will be initilized when using, freed after using.\n"); - } else { - MNN_PRINT("(Memory Enough) All Diffusion models will be initilized when application enter.\n"); - } - MNN_PRINT("Backend type: %d\n", (int)backend_type); - MNN_PRINT("Output image name: %s\n", img_name); - MNN_PRINT("Prompt text: %s\n", input_text.c_str()); - - - diffusion::Pipeline pipeline(resource_path, model_type, backend_type, memory_mode); - pipeline.run(input_text, img_name); - return 0; -} diff --git a/transformers/llm/engine/CMakeLists.txt b/transformers/llm/engine/CMakeLists.txt index a2557c79b..bb3eb14ff 100644 --- a/transformers/llm/engine/CMakeLists.txt +++ b/transformers/llm/engine/CMakeLists.txt @@ -3,7 +3,6 @@ option(LLM_SUPPORT_AUDIO "Llm model support audio input." OFF) if (LLM_SUPPORT_VISION AND MNN_BUILD_OPENCV) - list(APPEND MNN_DEPS MNNOpenCV) include_directories(${CMAKE_SOURCE_DIR}/tools/cv/include/) endif() diff --git a/transformers/llm/engine/audio_demo.cpp b/transformers/llm/engine/audio_demo.cpp index 50a0e1661..032cf2650 100644 --- a/transformers/llm/engine/audio_demo.cpp +++ b/transformers/llm/engine/audio_demo.cpp @@ -120,6 +120,7 @@ int main(int argc, const char* argv[]) { for (int i=0; iwriteMap()[i] = i; } + llm->setKVCacheInfo(inputIds[0].size(), 0); auto logits = llm->forwardRaw(embeddings, llm->gen_attention_mask(T+3), inputPos); { auto info = logits->getInfo(); @@ -136,6 +137,7 @@ int main(int argc, const char* argv[]) { llm->switchMode(MNN::Transformer::Llm::Decode); std::vector embeddingsVec(inputIds.size()); for (int index=0; index<1; ++index) { + llm->setKVCacheInfo(1, 0); MNN::Express::VARP embeddings; for (int i=0; iembedding(inputIds[i]); @@ -147,7 +149,7 @@ int main(int argc, const char* argv[]) { } embeddings = embeddings * MNN::Express::_Scalar(1.0f/8.0f); auto inputPos = MNN::Express::_Input({1, 1}, MNN::Express::NCHW, halide_type_of()); - inputPos->writeMap()[0] = T+3; + inputPos->writeMap()[0] = T+3 + index; auto logits = llm->forwardRaw(embeddings, llm->gen_attention_mask(1), inputPos); saveVar(logits, "logit_decode.txt"); auto size = logits->getInfo()->size; diff --git a/transformers/llm/engine/embedding_demo.cpp b/transformers/llm/engine/embedding_demo.cpp index 3261ca163..5a91e40ba 100644 --- a/transformers/llm/engine/embedding_demo.cpp +++ b/transformers/llm/engine/embedding_demo.cpp @@ -12,8 +12,30 @@ using namespace MNN::Express; using namespace MNN::Transformer; +#define DUMP_NUM_DATA(type) \ + auto data = var->readMap(); \ + for (int z = 0; z < outside; ++z) { \ + for (int x = 0; x < width; ++x) { \ + outputOs << data[x + z * width] << "\n"; \ + } \ + } + +static void dumpVar2File(VARP var, const char* file) { + std::ofstream outputOs(file); + + auto dimension = var->getInfo()->dim.size(); + int width = 1; + if (dimension > 1) { + width = var->getInfo()->dim[dimension - 1]; + } + + auto outside = var->getInfo()->size / width; + DUMP_NUM_DATA(float); + +} + static void dumpVARP(VARP var) { - auto size = var->getInfo()->size; + auto size = static_cast(var->getInfo()->size); auto ptr = var->readMap(); printf("[ "); for (int i = 0; i < 5; i++) { @@ -26,6 +48,33 @@ static void dumpVARP(VARP var) { printf(" ]\n"); } +static void unittest(std::unique_ptr &embedding, std::string prompt) { + auto vec_0 = embedding->txt_embedding(prompt); + float sum = 0; + auto ptr = vec_0->readMap(); + for (int i = 0;i < vec_0->getInfo()->size; ++i) { + sum += ptr[i]; + } + MNN_PRINT("%s\n", prompt.c_str()); + MNN_PRINT("sum = %f\n", sum); + MNN_PRINT("\n"); +} +static void benchmark(std::unique_ptr &embedding, std::string prompt_file) { + std::ifstream prompt_fs(prompt_file); + std::vector prompts; + std::string prompt; + while (std::getline(prompt_fs, prompt)) { + if (prompt.back() == '\r') { + prompt.pop_back(); + } + prompts.push_back(prompt); + } + prompt_fs.close(); + for (auto& p: prompts) { + unittest(embedding, p); + } +} + int main(int argc, const char* argv[]) { if (argc < 2) { std::cout << "Usage: " << argv[0] << " config.json" << std::endl; @@ -34,14 +83,12 @@ int main(int argc, const char* argv[]) { std::string config_path = argv[1]; std::cout << "config path is " << config_path << std::endl; std::unique_ptr embedding(Embedding::createEmbedding(config_path, true)); - auto vec_0 = embedding->txt_embedding("在春暖花开的季节，走在樱花缤纷的道路上，人们纷纷拿出手机拍照留念。樱花树下，情侣手牵手享受着这绝美的春光。孩子们在树下追逐嬉戏，脸上洋溢着纯真的笑容。春天的气息在空气中弥漫，一切都显得那么生机勃勃，充满希望。"); - auto vec_1 = embedding->txt_embedding("春天到了，樱花树悄然绽放，吸引了众多游客前来观赏。小朋友们在花瓣飘落的树下玩耍，而恋人们则在这浪漫的景色中尽情享受二人世界。每个人的脸上都挂着幸福的笑容，仿佛整个世界都被春天温暖的阳光和满树的樱花渲染得更加美好。"); - auto vec_2 = embedding->txt_embedding("在炎热的夏日里，沙滩上的游客们穿着泳装享受着海水的清凉。孩子们在海边堆沙堡，大人们则在太阳伞下品尝冷饮，享受悠闲的时光。远处，冲浪者们挑战着波涛，体验着与海浪争斗的刺激。夏天的海滩，总是充满了活力和热情。"); - dumpVARP(vec_0); - dumpVARP(vec_1); - dumpVARP(vec_2); - printf("dist_0_1: %f\n", Embedding::dist(vec_0, vec_1)); - printf("dist_0_2: %f\n", Embedding::dist(vec_0, vec_2)); - printf("dist_1_2: %f\n", Embedding::dist(vec_1, vec_2)); + if (argc > 2) { + benchmark(embedding, argv[2]); + return 0; + } + unittest(embedding, "这个东西，这。"); +// dumpVar2File(vec_0, filename.c_str()); +// dumpVARP(vec_0); return 0; } diff --git a/transformers/llm/engine/include/llm/llm.hpp b/transformers/llm/engine/include/llm/llm.hpp index 0cab96469..a55891665 100644 --- a/transformers/llm/engine/include/llm/llm.hpp +++ b/transformers/llm/engine/include/llm/llm.hpp @@ -67,6 +67,7 @@ class MNN_PUBLIC Llm { void tuning(TuneType type, std::vector candidates); virtual void load(); void switchMode(Stage stage); + void setKVCacheInfo(size_t add, size_t remove, int* reserve = nullptr, int n_reserve = 0); MNN::Express::VARP forwardRaw(MNN::Express::VARP hiddenState, MNN::Express::VARP mask, MNN::Express::VARP inputPos); virtual MNN::Express::VARP gen_attention_mask(int seq_len); virtual MNN::Express::VARP embedding(const std::vector& input_ids); diff --git a/transformers/llm/engine/src/llm.cpp b/transformers/llm/engine/src/llm.cpp index 264304650..8dcc14987 100644 --- a/transformers/llm/engine/src/llm.cpp +++ b/transformers/llm/engine/src/llm.cpp @@ -20,6 +20,7 @@ #include "tokenizer.hpp" // 0: no debug, 1: test op time, 2: print tensor info, 3: print tensor in output #define DEBUG_MODE 0 +//#define DEBUG_IMAGE #include "httplib.h" #ifdef LLM_SUPPORT_VISION @@ -257,11 +258,11 @@ void Llm::init_runtime() { runtime_manager_.reset(Executor::RuntimeManager::createRuntimeManager(config)); runtime_manager_->setHint(MNN::Interpreter::MEM_ALLOCATOR_TYPE, 0); - runtime_manager_->setHint(MNN::Interpreter::DYNAMIC_QUANT_OPTIONS, 1); // 1: per batch quant, 2: per tensor quant runtime_manager_->setHint(MNN::Interpreter::QKV_QUANT_OPTIONS, config_->quant_qkv()); runtime_manager_->setHint(MNN::Interpreter::KVCACHE_SIZE_LIMIT, config_->kvcache_limit()); - runtime_manager_->setHint(MNN::Interpreter::MMAP_FILE_SIZE, file_size_m(config_->llm_weight()) + 128); - runtime_manager_->setHint(MNN::Interpreter::USE_CACHED_MMAP, 1); + if (config_->use_cached_mmap()) { + runtime_manager_->setHint(MNN::Interpreter::USE_CACHED_MMAP, 1); + } std::string tmpPath = config_->tmp_path(); if (config_->kvcache_mmap()) { runtime_manager_->setExternalPath(tmpPath, MNN::Interpreter::EXTERNAL_PATH_KVCACHE_DIR); @@ -335,15 +336,16 @@ size_t Llm::apply_lora(const std::string& lora_path) { module_config.rearrange = true; module_config.base = modules_.begin()->get(); size_t lora_index = modules_.size(); + runtime_manager_->setHint(MNN::Interpreter::USE_CACHED_MMAP, 0); modules_.emplace_back(Module::load({"input_ids", "attention_mask", "position_ids"}, {"logits"}, - model_path.c_str(), runtime_manager_, &module_config)); + model_path.c_str(), runtime_manager_, &module_config)); select_module(lora_index); return lora_index; } Llm* Llm::create_lora(const std::string& lora_path) { - auto llm = new Llm(config_); - llm->set_config("{\"llm_model\": \"" + lora_path + "\"}"); + auto llm = new Llm(std::make_shared(*config_)); + llm->set_config("{\"llm_model\": \"" + lora_path + "\", \"use_mmap\": false, \"use_cached_mmap\": false}"); llm->base_module_ = modules_.begin()->get(); llm->load(); return llm; @@ -405,7 +407,7 @@ void Llm::tuning(TuneType type, std::vector candidates) { int prefer_candidate = 10; for (auto& candidate : candidates) { runtime_manager_->setHint(MNN::Interpreter::OP_ENCODER_NUMBER_FOR_COMMIT, candidate); - + mMeta->add = 1; auto st = std::chrono::system_clock::now(); auto logits = forward({0}); if (nullptr == logits.get()) { @@ -424,6 +426,9 @@ void Llm::tuning(TuneType type, std::vector candidates) { } } runtime_manager_->setHint(MNN::Interpreter::OP_ENCODER_NUMBER_FOR_COMMIT, prefer_candidate); + // clear dirty tuning kv history + setKVCacheInfo(0, getCurrentHistory()); + reset(); } void Llm::switchMode(Llm::Stage stage) { switch (stage) { @@ -438,6 +443,16 @@ void Llm::switchMode(Llm::Stage stage) { } } +void Llm::setKVCacheInfo(size_t add, size_t remove, int* reserve, int n_reserve) { + if (remove > mMeta->previous) { + remove = mMeta->previous; + } + mMeta->remove = remove; + mMeta->reserve = reserve; + mMeta->n_reserve = n_reserve; + mMeta->add = add; +} + MNN::Express::VARP Llm::forwardRaw(MNN::Express::VARP hiddenState, MNN::Express::VARP mask, MNN::Express::VARP inputPos) { VARP logits; std::vector outputs; @@ -446,6 +461,7 @@ MNN::Express::VARP Llm::forwardRaw(MNN::Express::VARP hiddenState, MNN::Express: return nullptr; } logits = outputs[0]; + mMeta->sync(); return logits; } @@ -482,6 +498,7 @@ int Llm::sample(VARP logits, const std::vector& pre_ids, int offset, int si token_id = i; } } + mState.output_ids_.push_back(token_id); return token_id; } @@ -624,7 +641,6 @@ void Llm::generate(int max_token) { mMeta->add = 1; mMeta->remove = 0; auto logits = forward({mState.current_token_}); - mMeta->sync(); len++; if (nullptr == logits.get()) { break; @@ -660,7 +676,6 @@ std::vector Llm::generate(const std::vector& input_ids, int max_tokens auto et = std::chrono::system_clock::now(); current_modules_ = decode_modules_; mState.prefill_us_ = std::chrono::duration_cast(et - st).count(); - mMeta->sync(); generate(max_tokens); #ifdef DUMP_PROFILE_INFO @@ -670,10 +685,10 @@ std::vector Llm::generate(const std::vector& input_ids, int max_tokens } std::vector Llm::tokenizer_encode(const std::string& user_content, bool use_template) { - if (!use_template) { - return tokenizer_->encode(user_content); + auto prompt = user_content; + if (config_->use_template() && use_template) { + prompt = apply_prompt_template(user_content); } - auto prompt = apply_prompt_template(user_content); auto input_ids = tokenizer_->encode(prompt); return input_ids; } @@ -904,7 +919,6 @@ void Mllm::load() { config.backendConfig = &cpuBackendConfig; mllm_runtime_manager_.reset(Executor::RuntimeManager::createRuntimeManager(config)); mllm_runtime_manager_->setHint(MNN::Interpreter::MEM_ALLOCATOR_TYPE, 0); - mllm_runtime_manager_->setHint(MNN::Interpreter::DYNAMIC_QUANT_OPTIONS, 1); // 1: per batch quant, 2: per tensor quant mllm_runtime_manager_->setHint(MNN::Interpreter::QKV_QUANT_OPTIONS, config_->quant_qkv()); mllm_runtime_manager_->setHint(MNN::Interpreter::KVCACHE_SIZE_LIMIT, config_->kvcache_limit()); std::string tmpPath = config_->tmp_path(); @@ -1020,6 +1034,10 @@ printf("]\n}\n"); std::vector Mllm::vision_process(const std::string& file) { #ifdef LLM_SUPPORT_VISION VARP image = MNN::CV::imread(file); + if (image == nullptr) { + MNN_PRINT("Mllm Can't open image: %s\n", file.c_str()); + return std::vector(0); + } auto st = std::chrono::system_clock::now(); VARP image_embedding; @@ -1075,7 +1093,20 @@ std::vector Mllm::vision_process(const std::string& file) { // build attention_mask VARP attention_mask = MNN::Express::_Input({1, seq_len, seq_len}, NCHW); ::memset(attention_mask->writeMap(), 0, seq_len * seq_len * sizeof(float)); +#ifdef DEBUG_IMAGE + patches.fix(MNN::Express::VARP::CONSTANT); + patches->setName("patches"); + position_ids.fix(MNN::Express::VARP::CONSTANT); + position_ids->setName("position_ids"); + attention_mask.fix(MNN::Express::VARP::CONSTANT); + attention_mask->setName("attention_mask"); + MNN::Express::Variable::save({patches, position_ids, attention_mask}, "input.mnn"); +#endif image_embedding = mul_module_->onForward({patches, position_ids, attention_mask})[0]; +#ifdef DEBUG_IMAGE + image_embedding->setName("image_embeds"); + MNN::Express::Variable::save({image_embedding}, "output.mnn"); +#endif } else { image = MNN::CV::resize(image, {image_height_, image_width_}, 0, 0, MNN::CV::INTER_LINEAR, MNN::CV::COLOR_BGR2RGB, @@ -1107,6 +1138,10 @@ std::vector Mllm::audio_process(const std::string& file) { constexpr int sample_rate = 16000; auto load_res = MNN::AUDIO::load(file, sample_rate); VARP waveform = load_res.first; + if (waveform == nullptr) { + MNN_PRINT("Mllm Can't open audio: %s\n", file.c_str()); + return std::vector(0); + } // int sample_rate = load_res.second; int wav_len = waveform->getInfo()->dim[0]; int hop_length = 160; diff --git a/transformers/llm/engine/src/llmconfig.hpp b/transformers/llm/engine/src/llmconfig.hpp index a3ea87bd4..fa43e469a 100644 --- a/transformers/llm/engine/src/llmconfig.hpp +++ b/transformers/llm/engine/src/llmconfig.hpp @@ -67,6 +67,25 @@ class rapid_json_wrapper { rapidjson::Document document; rapid_json_wrapper() {} rapid_json_wrapper(rapidjson::Document doc) : document(std::move(doc)) {} + rapid_json_wrapper(const rapid_json_wrapper &other) { + document.CopyFrom(other.document, document.GetAllocator()); + } + rapid_json_wrapper& operator=(const rapid_json_wrapper& other) { + if (this != &other) { + document.SetObject(); + document.CopyFrom(other.document, document.GetAllocator()); + } + return *this; + } + rapid_json_wrapper(rapid_json_wrapper&& other) noexcept : document(std::move(other.document)) {} + rapid_json_wrapper& operator=(rapid_json_wrapper&& other) noexcept { + if (this != &other) { + document.SetObject(); + document.GetAllocator().Clear(); + document = std::move(other.document); + } + return *this; + } static rapid_json_wrapper parse(const std::ifstream& ifile) { std::ostringstream ostr; ostr << ifile.rdbuf(); @@ -181,6 +200,12 @@ class LlmConfig { std::string base_dir_; rapid_json_wrapper config_, llm_config_, mllm_config_, cur_config_; LlmConfig() {} + LlmConfig(const LlmConfig& other) + : base_dir_(other.base_dir_), + config_(other.config_), + llm_config_(other.llm_config_), + mllm_config_(other.mllm_config_), + cur_config_(other.cur_config_) {} LlmConfig(const std::string& path) { // load config if (has_suffix(path, ".json")) { @@ -318,9 +343,16 @@ class LlmConfig { return llm_config_.value("is_audio", false); } + bool use_template() const { + return config_.value("use_template", true); + } + bool use_mmap() const { return config_.value("use_mmap", false); } + bool use_cached_mmap() const { + return config_.value("use_cached_mmap", true); + } bool kvcache_mmap() const { return config_.value("kvcache_mmap", false); } diff --git a/transformers/llm/export/llmexport.py b/transformers/llm/export/llmexport.py index a25c69c13..cfe081b1b 100644 --- a/transformers/llm/export/llmexport.py +++ b/transformers/llm/export/llmexport.py @@ -1,2540 +1,31 @@ import os -import gc -import sys -import math -import copy import json -import time +import glob import base64 -import logging -import inspect import warnings import argparse -import functools -import traceback -from collections import defaultdict -from typing import Optional, Tuple, List, Union, Dict -from tqdm import tqdm -from yaspin import yaspin +warnings.filterwarnings("ignore") +os.environ['TOKENIZERS_PARALLELISM'] = 'false' +os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' import onnx import torch -import numpy as np +from typing import Optional from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoTokenizer -RESET = "\033[0m" -GREEN = "\033[32;1m" -YELLOW = "\033[33;4m" -EXPORT_LOG = '.export.log' - -# ignore warnning info -warnings.filterwarnings("ignore") -logging.basicConfig(level=logging.ERROR) -os.environ['TOKENIZERS_PARALLELISM'] = 'false' - -def spinner_run(text='Processing...', hide=False): - def decorator(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - with yaspin(text=text, color="cyan") as spinner: - start = time.time() - try: - if hide: spinner.hide() - result = func(*args, **kwargs) - if hide: spinner.show() - except Exception as e: - spinner.fail("💥 Failed") - traceback.print_exc() - exit(1) - end = time.time() - during = f'[{end-start:05.2f} s]'.replace('[0', '[ ') - padding = ' ' * (64 - len(spinner.text) - len(result)) - spinner.text = f'{spinner.text}{YELLOW}{result}{RESET}{padding}{GREEN}{during}{RESET}' - spinner.ok("✅ Done") - return result - return wrapper - return decorator - -class ModelMapper: - def __init__(self): - self.attrs = [] - self.mapper = dict() - self.regist_models() - - def get_map(self, config): - model_type = config.model_type - if model_type == 'chatglm': - if hasattr(config, 'vocab_size') and config.vocab_size == 130528: - model_type = 'chatglm' - else: - model_type = 'chatglm2' - if model_type in self.mapper: - return model_type, self.mapper[model_type] - return model_type, self.default_map - - def regist(self, model_type, model_map): - assert('config' in model_map and - 'decoder' in model_map and - 'attention' in model_map) - self.mapper[model_type] = model_map - - def regist_models(self): - self.defualt_map() - # regist models - self.regist_llama() - self.regist_mllama() - self.regist_qwen() - self.regist_glm() - self.regist_glm2() - self.regist_phi() - self.regist_gemma2() - self.register_openelm() - - def regist_llama(self): - llama_map = self.default_map - self.regist('llama', llama_map) - self.regist('qwen2', llama_map) - self.regist('internlm', llama_map) - self.regist('mobilellm', llama_map) - # baichuan - baichuan_map = copy.deepcopy(self.default_map) - baichuan_map[self.attention_key] = { - 'qkv_proj': 'W_pack', - 'o_proj': 'o_proj' - } - self.regist('baichuan', baichuan_map) - - def regist_mllama(self): - mllama_map = { - 'config': { - 'hidden_size': 'text_config.hidden_size', - 'num_attention_heads': 'text_config.num_attention_heads', - 'num_hidden_layers': 'text_config.num_hidden_layers', - 'num_key_value_heads': 'text_config.num_key_value_heads', - 'rope_theta': 'text_config.rope_theta' - }, - 'model': { - 'lm_': 'language_model.lm_head', - 'embed_': 'language_model.model.embed_tokens', - 'blocks_': 'language_model.model.layers', - 'final_layernorm_': 'language_model.model.norm', - 'visual': 'vision_model', - 'multi_modal_projector': 'multi_modal_projector' - }, - 'decoder': { - 'self_attn': 'self_attn', - 'cross_attn': 'cross_attn', - 'mlp': 'mlp', - 'input_layernorm': 'input_layernorm', - 'post_attention_layernorm': 'post_attention_layernorm' - }, - 'attention': { - 'q_proj': 'q_proj', - 'k_proj': 'k_proj', - 'v_proj': 'v_proj', - 'o_proj': 'o_proj', - 'q_norm': 'q_norm', - 'k_norm': 'k_norm', - 'cross_attn_attn_gate': 'cross_attn_attn_gate', - 'cross_attn_mlp_gate': 'cross_attn_mlp_gate' - } - } - self.regist('mllama', mllama_map) - - def regist_qwen(self): - qwen_map = { - 'config': { - 'hidden_size': 'hidden_size', - 'num_attention_heads': 'num_attention_heads', - 'num_hidden_layers': 'num_hidden_layers', - 'rope_theta': 'rotary_emb_base', - }, - 'model': { - 'lm_': 'lm_head', - 'embed_': 'transformer.wte', - 'blocks_': 'transformer.h', - 'final_layernorm_': 'transformer.ln_f', - 'visual': 'transformer.visual' - }, - 'decoder': { - 'self_attn': 'attn', - 'mlp': 'mlp', - 'input_layernorm': 'ln_1', - 'post_attention_layernorm': 'ln_2' - }, - 'attention': { - 'qkv_proj': 'c_attn', - 'o_proj': 'c_proj' - } - } - self.regist('qwen', qwen_map) - - def regist_glm(self): - glm_map = { - 'config': { - 'hidden_size': 'hidden_size', - 'num_attention_heads': 'num_attention_heads', - 'num_hidden_layers': 'num_layers' - }, - 'model': { - 'lm_': 'lm_head', - 'embed_': 'transformer.word_embeddings', - 'blocks_': 'transformer.layers', - 'final_layernorm_': 'transformer.final_layernorm', - }, - 'decoder': { - 'self_attn': 'attention', - 'mlp': 'mlp', - 'input_layernorm': 'input_layernorm', - 'post_attention_layernorm': 'post_attention_layernorm' - }, - 'attention': { - 'qkv_proj': 'query_key_value', - 'o_proj': 'dense' - } - } - self.regist('chatglm', glm_map) - - def regist_glm2(self): - glm2_map = { - 'config': { - 'hidden_size': 'hidden_size', - 'num_attention_heads': 'num_attention_heads', - 'num_key_value_heads': 'multi_query_group_num', - 'num_hidden_layers': 'num_layers', - }, - 'model': { - 'lm_': 'transformer.output_layer', - 'embed_': 'transformer.embedding.word_embeddings', - 'blocks_': 'transformer.encoder.layers', - 'final_layernorm_': 'transformer.encoder.final_layernorm', - }, - 'decoder': { - 'self_attn': 'self_attention', - 'mlp': 'mlp', - 'input_layernorm': 'input_layernorm', - 'post_attention_layernorm': 'post_attention_layernorm' - }, - 'attention': { - 'qkv_proj': 'query_key_value', - 'o_proj': 'dense' - } - } - self.regist('chatglm2', glm2_map) - - def regist_phi(self): - phi_map = { - 'config': { - 'hidden_size': 'n_embd', - 'num_attention_heads': 'n_head', - 'num_hidden_layers': 'n_layer', - 'rotary_dim': 'rotary_dim' - }, - 'model': { - 'lm_': 'lm_head.linear', - 'embed_': 'transformer.embd.wte', - 'blocks_': 'transformer.h', - 'final_layernorm_': 'lm_head.ln', - }, - 'decoder': { - 'self_attn': 'mixer', - 'mlp': 'mlp', - 'input_layernorm': 'ln', - }, - 'attention': { - 'qkv_proj': 'Wqkv', - 'o_proj': 'out_proj' - } - } - self.regist('phi-msft', phi_map) - - def regist_gemma2(self): - gemma2_config = copy.deepcopy(self.default_config) - gemma2_config['head_dim'] = 'head_dim' - gemma2_decoder = copy.deepcopy(self.default_decoder) - gemma2_decoder['pre_feedforward_layernorm'] = 'pre_feedforward_layernorm' - gemma2_decoder['post_feedforward_layernorm'] = 'post_feedforward_layernorm' - gemma2_map = { - 'config': gemma2_config, - 'model': self.defualt_model, - 'decoder': gemma2_decoder, - 'attention': self.default_attention - } - self.regist('gemma2', gemma2_map) - - def register_openelm(self): - openelm_config = { - 'hidden_size': 'model_dim', - 'head_dim': 'head_dim', - 'num_attention_heads': 'num_query_heads', - 'num_hidden_layers': 'num_transformer_layers', - 'num_key_value_heads': 'num_kv_heads', - 'rope_theta': 'rope_freq_constant' - } - openelm_model = { - 'lm_': 'lm_head', - 'embed_': 'transformer.token_embeddings', - 'blocks_': 'transformer.layers', - 'final_layernorm_': 'transformer.norm' - } - openelm_decoder = { - 'self_attn': 'attn', - 'mlp': 'ffn', - 'input_layernorm': 'attn_norm', - 'post_attention_layernorm': 'ffn_norm' - } - openelm_attention = { - 'qkv_proj': 'qkv_proj', - 'o_proj': 'out_proj', - 'q_norm': 'q_norm', - 'k_norm': 'k_norm' - } - openelm_map = { - 'config': openelm_config, - 'model': openelm_model, - 'decoder': openelm_decoder, - 'attention': openelm_attention - } - self.regist('openelm', openelm_map) - - def defualt_map(self): - # default map is `LlamaForCausalLM` - self.config_key = 'config' - self.model_key = 'model' - self.decoder_key = 'decoder' - self.attention_key = 'attention' - self.default_config = { - 'hidden_size': 'hidden_size', - 'num_attention_heads': 'num_attention_heads', - 'num_hidden_layers': 'num_hidden_layers', - 'num_key_value_heads': 'num_key_value_heads', - 'rope_theta': 'rope_theta' - } - self.defualt_model = { - 'lm_': 'lm_head', - 'embed_': 'model.embed_tokens', - 'blocks_': 'model.layers', - 'final_layernorm_': 'model.norm', - 'visual': 'visual' - } - self.default_decoder = { - 'self_attn': 'self_attn', - 'mlp': 'mlp', - 'input_layernorm': 'input_layernorm', - 'post_attention_layernorm': 'post_attention_layernorm' - } - self.default_attention = { - 'q_proj': 'q_proj', - 'k_proj': 'k_proj', - 'v_proj': 'v_proj', - 'o_proj': 'o_proj' - } - self.default_map = { - 'config': self.default_config, - 'model': self.defualt_model, - 'decoder': self.default_decoder, - 'attention': self.default_attention - } - - @staticmethod - def do_map(dst, src, map): - for dst_attr, src_attr in map.items(): - attributes = src_attr.split('.') - obj = src - for attr in attributes: - if hasattr(obj, attr): - obj = getattr(obj, attr) - else: - obj = None - break - setattr(dst, dst_attr, obj) - -# Quant class - -# awq quantizer start -class AwqQuantizer: - def __init__( - self, - model, - modules_to_not_convert=None, - apply_clip=True, - n_parallel_calib_samples=None, - max_calib_samples=128, - max_calib_seq_len=512, - max_chunk_memory=1024 * 1024 * 1024, - ) -> None: - self.awq_model = model - self.model = model - self.tokenizer = model.tokenizer - self.w_bit = model.quant_bit - self.group_size = model.quant_block - self.zeropoint = not model.symmetric - self.calib_data = 'ag_news' - self.split = 'test' - self.duo_scaling = True - self.apply_clip = apply_clip - self.n_parallel_calib_samples = n_parallel_calib_samples - self.max_calib_samples = max_calib_samples - self.max_calib_seq_len = max_calib_seq_len - self.max_chunk_memory = max_chunk_memory - self.modules_to_not_convert = ( - modules_to_not_convert if modules_to_not_convert is not None else [] - ) - self.modules, self.module_kwargs, self.inps = self.init_quant( - n_samples=self.max_calib_samples, max_seq_len=self.max_calib_seq_len - ) - - def pseudo_quantize_tensor(self, w: torch.Tensor): - org_w_shape = w.shape - if self.group_size > 0: - assert org_w_shape[-1] % self.group_size == 0 - w = w.reshape(-1, self.group_size) - assert w.dim() == 2 - assert torch.isnan(w).sum() == 0 - # zero point quantization - if self.zeropoint: - max_val = w.amax(dim=1, keepdim=True) - min_val = w.amin(dim=1, keepdim=True) - offset = 1 << (self.w_bit - 1) - clip_max = offset - 1 - clip_min = -offset - scales = (max_val - min_val) / (clip_max - clip_min) - zeros = - torch.round(min_val / scales) + clip_min - qw = torch.round(w / scales) + zeros - qw = torch.clamp(qw, clip_min, clip_max) - w = (qw - zeros) * scales - zeros = min_val.view(org_w_shape[0], -1) - else: - abs_max = w.abs().amax(dim=1, keepdim=True) - offset = 1 << (self.w_bit - 1) - clip_max = offset - 1 - clip_min = -clip_max - scales = abs_max / clip_max - w = torch.clamp(torch.round(w / scales), clip_min, clip_max) * scales - zeros = None - - assert torch.isnan(scales).sum() == 0 - assert torch.isnan(w).sum() == 0 - - scales = scales.view(org_w_shape[0], -1) - w = w.reshape(org_w_shape) - - return w, scales, zeros - - def quantize(self): - for i in tqdm(range(len(self.modules)), desc="AWQ"): - # Move module and inputs to correct device - common_device = next(self.modules[i].parameters()).device - if common_device is None or str(common_device) == "cpu": - best_device = AwqQuantizer.get_best_device() - - self.modules[i] = self.modules[i].to(best_device) - common_device = next(self.modules[i].parameters()).device - - if self.module_kwargs.get("position_ids") is not None: - self.module_kwargs["position_ids"] = self.module_kwargs[ - "position_ids" - ].to(common_device) - - if self.module_kwargs.get("attention_mask") is not None: - self.module_kwargs["attention_mask"] = self.module_kwargs[ - "attention_mask" - ].to(common_device) - - self.inps = self.inps.to(common_device) - # print(f'# {i} inps shape: {self.inps.shape}, inps.max: {self.inps.max()}') - - # [STEP 1]: Get layer, extract linear modules, extract input features - named_linears = AwqQuantizer.get_named_linears(self.modules[i]) - - # Filter out the linear layers we don't want to exclude - named_linears = AwqQuantizer.exclude_layers_to_not_quantize( - named_linears, self.modules_to_not_convert - ) - input_feat = self._get_input_feat(self.modules[i], named_linears) - AwqQuantizer.clear_memory() - - # [STEP 2]: Compute and apply scale list - module_config = [] - # q, k, v proj - module_config.append( - dict( - prev_op=self.modules[i].input_layernorm, - layers=[ - self.modules[i].self_attn.q_proj, - self.modules[i].self_attn.k_proj, - self.modules[i].self_attn.v_proj, - ], - inp=input_feat["self_attn.q_proj"], - module2inspect=self.modules[i].self_attn, - kwargs=self.module_kwargs, - ) - ) - # o_proj - if self.modules[i].self_attn.v_proj.weight.shape == self.modules[i].self_attn.o_proj.weight.shape: - module_config.append( - dict( - prev_op=self.modules[i].self_attn.v_proj, - layers=[self.modules[i].self_attn.o_proj], - inp=input_feat["self_attn.o_proj"], - ) - ) - # mlp gate - module_config.append( - dict( - prev_op=self.modules[i].post_attention_layernorm, - layers=[self.modules[i].mlp.gate_proj, self.modules[i].mlp.up_proj], - inp=input_feat["mlp.gate_proj"], - module2inspect=self.modules[i].mlp, - ) - ) - # mlp down - module_config.append( - dict( - prev_op=self.modules[i].mlp.up_proj, - layers=[self.modules[i].mlp.down_proj], - inp=input_feat["mlp.down_proj"], - ) - ) - scales_list = [ - self._search_best_scale(self.modules[i], **layer) - for layer in module_config - ] - # print(scales_list); exit(0) - AwqQuantizer.apply_scale(self.modules[i], scales_list, input_feat_dict=input_feat) - # [STEP 3]: Compute and apply clipping list - if self.apply_clip: - clip_list = self._search_best_clip( - self.modules[i], named_linears, input_feat - ) - AwqQuantizer.apply_clip(self.modules[i], clip_list) - - AwqQuantizer.clear_memory() - - @torch.no_grad() - def _module_forward( - self, x: torch.Tensor, module: torch.nn.Module, module_kwargs: Dict - ) -> torch.Tensor: - if self.n_parallel_calib_samples is None: - # runs through all samples at once - # print(module, x, module_kwargs); exit(0) - module_output = module(x, **module_kwargs) - if isinstance(module_output, tuple): - module_output = module_output[0] - else: - # memory efficiently runs through all calibration samples - # but only n_parallel_calib_samples at a time - module_output = [] - partitioned_inputs = torch.split(x, self.n_parallel_calib_samples) - for x_partial in partitioned_inputs: - partial_output = module(x_partial, **module_kwargs) - - if isinstance(partial_output, tuple): - partial_output = partial_output[0] - - module_output.append(partial_output.cpu()) - - module_output = torch.cat(module_output, dim=0) - - return module_output - - @torch.no_grad() - def _search_best_scale( - self, - module, - prev_op, - layers: List[torch.nn.Linear], - inp: torch.Tensor, - module2inspect=None, - kwargs={}, - ): - if module2inspect is None: - assert len(layers) == 1 - module2inspect = layers[0] - - if "use_cache" in kwargs: - kwargs.pop("use_cache") - - # Put x on the right device - inp = inp.to(next(module2inspect.parameters()).device) - - # [STEP 1]: Compute per-channel mean of normalised weights - # All layer weights are concatted together - weight = torch.cat([_m.weight for _m in layers], dim=0) - org_shape = weight.shape - # The weights are reshaped to be organised by quantization group - weight = weight.view(-1, self.group_size) - # Calculates the relative magnitude of the weights within each of the quantization groups, - # and rescales each group individually so that each group has weights on a 0-1 scale. - w_scale = weight.abs() / (weight.abs().amax(dim=1, keepdim=True) + 1e-6) - # Resizes the rescaled weight matrix back up to its original dimensions - w_scale = w_scale.view(org_shape) - # Gets the average rescaled magnitude for each output channel - w_mean = w_scale.mean(0) - AwqQuantizer.clear_memory(weight) - - # [STEP 2]: Compute per-channel mean of the input activation with chunking - # move inp to cpu to avoid memory leak - inp_flat = inp.cpu().abs().view(-1, inp.shape[-1]) - num_elements = inp_flat.size(0) - num_channels = inp_flat.size(1) - element_size_bytes = inp_flat.element_size() * 2 # multiplied by 2 for FP32 - - # Calculate chunk size dynamically based on max_chunk_memory - chunk_size = int(self.max_chunk_memory // (element_size_bytes * num_channels)) - chunk_size = min(chunk_size, num_elements) - - # Use float32 for sum calculation - x_sum = torch.zeros(num_channels, dtype=torch.float32, device=inp.device) - - for i in range(0, num_elements, chunk_size): - end = min(i + chunk_size, num_elements) - chunk_sum = inp_flat[i:end].to(torch.float32).sum(dim=0) - x_sum += chunk_sum.to(inp.device) - - x_mean = (x_sum / num_elements).to(inp.dtype) - AwqQuantizer.clear_memory(x_sum) - - # [STEP 3]: Compute output of module - with torch.no_grad(): - module_kwargs = self._sanitize_kwargs(kwargs, module2inspect) - fp16_output = self._module_forward(inp, module2inspect, module_kwargs) - - # [STEP 4]: Compute loss - best_scales = self._compute_best_scale( - inp, w_mean, x_mean, module2inspect, layers, fp16_output, module_kwargs - ) - - return ( - AwqQuantizer.get_op_name(module, prev_op), - tuple([AwqQuantizer.get_op_name(module, m) for m in layers]), - best_scales, - ) - - def _compute_best_scale( - self, - x: torch.Tensor, - w_mean: torch.Tensor, - x_mean: torch.Tensor, - module2inspect: torch.nn.Module, - linears2scale: List[torch.nn.Linear], - fp16_output: torch.Tensor, - kwargs: Dict={}, - ): - """ - Compute loss and select best scales - - L(s) = || Q(W * s) (s^-1 * X) - W * X || - Q: weight quantization function | pseudo_quantize_tensor(W * s) - X: inputs from calib dataset | X - W: original weights in FP16 | layer - s: per channel scaling factor | s^-1 * X - """ - n_grid = 20 - history = [] - best_ratio = -1 - best_scales = None - best_error = float("inf") - - device = x.device - x_mean = x_mean.view(-1).to(device) - w_mean = w_mean.view(-1).to(device) - - ord_weights = [] - for fc in linears2scale: - ord_weights.append(fc.weight.data.clone()) - - for ratio in range(n_grid): - # create new scales - ratio = ratio / n_grid - - # NOTE: s^-1 * x is fused here, according to paper - if self.duo_scaling: - scales = (x_mean.pow(ratio) / (w_mean.pow(1 - ratio) + 1e-4)).clamp(min=1e-4) - else: - scales = x_mean.pow(ratio).clamp(min=1e-4).view(-1) - scales = scales / (scales.max() * scales.min()).sqrt() - scales_view = scales.view(1, -1).to(device) - - # avoid scaling values that overflow - scales[torch.isinf(scales)] = 1 - scales[torch.isnan(scales)] = 1 - - # Q(W * s) - for fc in linears2scale: - fc.weight.mul_(scales_view) - fc.weight.data = ( - self.pseudo_quantize_tensor(fc.weight.data)[0] / scales_view - ) - - # W * X - int_w_output = self._module_forward(x, module2inspect, kwargs) - - # compute mean squared error (L2 norm) - loss = self._compute_loss(fp16_output, int_w_output, device) - - history.append(loss) - if loss < best_error: - best_error = loss - best_ratio = ratio - best_scales = scales.clone() - - for fc, ord_weight in zip(linears2scale, ord_weights): - fc.weight.data = ord_weight.clone() - - del ord_weights - - if best_ratio == -1: - logging.debug(history) - raise Exception - - assert torch.isnan(best_scales).sum() == 0, best_scales - - return best_scales.detach().cpu() - - @torch.no_grad() - def _compute_loss( - self, - fp16_output: torch.Tensor, - int_w_output: torch.Tensor, - device: torch.device, - ): - loss = 0.0 - fp16_output_flat = fp16_output.view(-1) - int_w_output_flat = int_w_output.view(-1) - num_elements = fp16_output_flat.size(0) - element_size_bytes = fp16_output.element_size() - - # Calculate chunk size dynamically based on max_chunk_memory - # Divide the max_chunk_memory by twice the element size - chunk_size = self.max_chunk_memory // (element_size_bytes * 2) - chunk_size = min(chunk_size, num_elements) - - # Split the computation into chunks - fp16_chunks = torch.split(fp16_output_flat, chunk_size) - int_w_chunks = torch.split(int_w_output_flat, chunk_size) - - # Compute the loss for each chunk - for fp16_chunk, int_w_chunk in zip(fp16_chunks, int_w_chunks): - chunk_loss = (fp16_chunk.to(device) - int_w_chunk.to(device)).float().pow(2).sum().item() - loss += chunk_loss - - # Normalize the loss by the total number of elements - loss /= num_elements - - return loss - - @torch.no_grad() - def _search_best_clip(self, layer, named_linears, input_feat): - clip_list = [] - avoid_clipping = ["q_", "k_", "query", "key", "Wqkv"] - - for name in named_linears: - # due to qk bmm, it is hard to clip precisely - if any([_ in name for _ in avoid_clipping]): - continue - - named_linears[name].to(AwqQuantizer.get_best_device()) - max_val = self._compute_best_clip( - named_linears[name].weight, input_feat[name] - ) - clip_list.append((name, max_val)) - named_linears[name].cpu() - - return clip_list - - @torch.no_grad() - def _compute_best_clip( - self, - w: torch.Tensor, - input_feat: torch.Tensor, - n_grid=20, - max_shrink=0.5, - n_sample_token=512, - ): - assert w.dim() == 2 - org_w_shape = w.shape - # w [co, ci] -> [co, 1, n_group, group size] - # input_feat [n_token, ci] -> [1, n_token, n_group, group size] - group_size = self.group_size if self.group_size > 0 else org_w_shape[1] - input_feat = input_feat.view(-1, input_feat.shape[-1]) - input_feat = input_feat.reshape(1, input_feat.shape[0], -1, group_size) - - # Compute input feature step size (minimum 1) - step_size = max(1, input_feat.shape[1] // n_sample_token) - input_feat = input_feat[:, ::step_size] - - w = w.reshape(org_w_shape[0], 1, -1, group_size) - - oc_batch_size = 256 if org_w_shape[0] % 256 == 0 else 64 # prevent OOM - assert org_w_shape[0] % oc_batch_size == 0 - w_all = w - best_max_val_all = [] - - for i_b in range(org_w_shape[0] // oc_batch_size): - w = w_all[i_b * oc_batch_size : (i_b + 1) * oc_batch_size] - - org_max_val = w.abs().amax(dim=-1, keepdim=True) # co, 1, n_group, 1 - - best_max_val = org_max_val.clone() - min_errs = torch.ones_like(org_max_val) * 1e9 - input_feat = input_feat.to(w.device) - org_out = (input_feat * w).sum(dim=-1) # co, n_token, n_group - - for i_s in range(int(max_shrink * n_grid)): - max_val = org_max_val * (1 - i_s / n_grid) - min_val = -max_val - cur_w = torch.clamp(w, min_val, max_val) - q_w = self.pseudo_quantize_tensor(cur_w)[0] - cur_out = (input_feat * q_w).sum(dim=-1) - - # co, 1, n_group, 1 - err = (cur_out - org_out).pow(2).mean(dim=1).view(min_errs.shape) - del cur_w - del cur_out - cur_best_idx = err < min_errs - min_errs[cur_best_idx] = err[cur_best_idx] - best_max_val[cur_best_idx] = max_val[cur_best_idx] - best_max_val_all.append(best_max_val) - - best_max_val = torch.cat(best_max_val_all, dim=0) - - AwqQuantizer.clear_memory(input_feat) - AwqQuantizer.clear_memory(org_out) - - return best_max_val.squeeze(1) - - @staticmethod - @torch.no_grad() - def apply_clip(module, clip_list: Tuple[str, torch.Tensor]): - for name, max_val in clip_list: - layer: torch.nn.Linear = AwqQuantizer.get_op_by_name(module, name) - layer.to(AwqQuantizer.get_best_device()) - max_val = max_val.to(layer.weight.device) - org_shape = layer.weight.shape - layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1) - layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val) - layer.weight.data = layer.weight.data.reshape(org_shape) - layer.cpu() - - @staticmethod - @torch.no_grad() - def scale_fc_fcs(fc1: torch.nn.Linear, fcs: List[torch.nn.Linear], scales: torch.Tensor): - if not isinstance(fcs, list): - fcs = [fcs] - - scales = scales.to(fc1.weight.device) - - fc1.weight[-scales.size(0) :].div_(scales.view(-1, 1)) - if fc1.bias is not None: - fc1.bias.div_(scales.view(-1)) - - for fc in fcs: - fc.weight.mul_(scales.view(1, -1)) - - for p in fc1.parameters(): - assert torch.isnan(p).sum() == 0 - for fc in fcs: - for p in fc.parameters(): - assert torch.isnan(p).sum() == 0 - - @staticmethod - def is_allowed_act_fns(op): - from transformers.activations import NewGELUActivation, PytorchGELUTanh, GELUActivation - allowed_act_fns = [ - torch.nn.GELU, - NewGELUActivation, - PytorchGELUTanh, - GELUActivation, - ] - return (op in allowed_act_fns) - - @staticmethod - def is_allowed_norms(op): - if isinstance(op, torch.nn.LayerNorm): - return True - if any(t in str(type(op)) for t in ['LlamaRMSNorm', 'GemmaRMSNorm', 'CohereLayerNorm']): - return True - return False - - @staticmethod - @torch.no_grad() - def scale_fc_fc(fc1: torch.nn.Linear, fc2: torch.nn.Linear, scales: torch.Tensor): - assert isinstance(fc1, torch.nn.Linear) - assert isinstance(fc2, torch.nn.Linear) - - scales = scales.to(fc1.weight.device) - fc1.weight[-scales.size(0) :].div_(scales.view(-1, 1)) - if fc1.bias is not None: - fc1.bias.div_(scales.view(-1)) - - fc2.weight.mul_(scales.view(1, -1)) - - for p in fc1.parameters(): - assert torch.isnan(p).sum() == 0 - for p in fc2.parameters(): - assert torch.isnan(p).sum() == 0 - - @staticmethod - @torch.no_grad() - def scale_ln_fcs(ln: torch.nn.Linear, fcs: List[torch.nn.Linear], scales: torch.Tensor): - if not isinstance(fcs, list): - fcs = [fcs] - - scales = scales.to(ln.weight.device) - - # GemmaRMSNorm is different from Llama's in that it multiplies - # (1 + weight) to the output, instead of just weight. - if 'GemmaRMSNorm' in str(type(ln)): - ln.weight += 1 - ln.weight.div_(scales) - ln.weight -= 1 - else: - ln.weight.div_(scales) - - if hasattr(ln, "bias") and ln.bias is not None: - ln.bias.div_(scales) - - for fc in fcs: - fc.weight.mul_(scales.view(1, -1)) - - for p in ln.parameters(): - assert torch.isnan(p).sum() == 0 - for fc in fcs: - for p in fc.parameters(): - assert torch.isnan(p).sum() == 0 - - @staticmethod - @torch.no_grad() - def scale_gelu_fc(gelu, fc: torch.nn.Linear, scales: torch.Tensor): - assert AwqQuantizer.is_allowed_act_fns(gelu) - assert isinstance(fc, torch.nn.Linear) - - fc.weight.mul_(scales.view(1, -1).to(fc.weight.device)) - - for p in fc.parameters(): - assert torch.isnan(p).sum() == 0 - - @staticmethod - def apply_scale(module, scales_list, input_feat_dict=None): - for prev_op_name, layer_names, scales in scales_list: - prev_op = AwqQuantizer.get_op_by_name(module, prev_op_name) - layers = [AwqQuantizer.get_op_by_name(module, name) for name in layer_names] - - best_device = AwqQuantizer.get_best_device() - prev_op.to(best_device) - for layer in layers: - layer.to(best_device) - scales.to(best_device) - if ( - isinstance(prev_op, torch.nn.Linear) - and type(layers) == list - and isinstance(layers[0], torch.nn.Linear) - ): - if len(layers) == 1: - AwqQuantizer.scale_fc_fc(prev_op, layers[0], scales) - else: - AwqQuantizer.scale_fc_fcs(prev_op, layers, scales) - elif ( - AwqQuantizer.is_allowed_norms(prev_op) - or "rmsnorm" in str(prev_op.__class__).lower() - ): - AwqQuantizer.scale_ln_fcs(prev_op, layers, scales) - - elif AwqQuantizer.is_allowed_act_fns(prev_op): - #new_module = ScaledActivation(prev_op, scales) - #set_op_by_name(module, prev_op_name, new_module) - AwqQuantizer.scale_gelu_fc(prev_op, layers[0], scales) - else: - raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!") - - # apply the scaling to input feat if given; prepare it for clipping - if input_feat_dict is not None: - for layer_name in layer_names: - # Skip the modules that are not quantized - if layer_name in input_feat_dict: - inp = input_feat_dict[layer_name] - inp.div_(scales.view(1, -1).to(inp.device)) - - prev_op.cpu() - for layer in layers: - layer.cpu() - scales.cpu() - - @staticmethod - def exclude_layers_to_not_quantize(linear_layers, modules_to_not_convert): - if modules_to_not_convert is None: - return linear_layers - - filtered_layers = {} - for name, linear_layer in linear_layers.items(): - if not any(key in name for key in modules_to_not_convert): - filtered_layers[name] = linear_layer - return filtered_layers - - @staticmethod - def get_named_linears(module): - return {name: m for name, m in module.named_modules() if isinstance(m, torch.nn.Linear)} - - @staticmethod - def get_op_by_name(module, op_name): - # get the op by its name relative to the module - for name, m in module.named_modules(): - if name == op_name: - return m - raise ValueError(f"Cannot find op {op_name} in module {module}") - - @staticmethod - def get_calib_dataset( - data: Union[str, List[str], List[List[int]]] = "pileval", - tokenizer=None, - n_samples=128, - max_seq_len=512, - split="train", - text_column="text", - ): - if isinstance(data, str): - from datasets import load_dataset - if data == "pileval": - dataset = load_dataset("mit-han-lab/pile-val-backup", split="validation") - else: - dataset = load_dataset(data, split=split) - # dataset = dataset.shuffle(seed=42) - elif isinstance(data, list): - if isinstance(data[0], str): - dataset = [{text_column: text} for text in data] - elif isinstance(data[0][0], int): - dataset = data - else: - raise NotImplementedError( - "Either pass a string to a huggingface dataset or a list" - "that is preprocessed with one sample of text per element" - " or a list of list of int for tokenized words." - ) - else: - raise NotImplementedError( - "Either pass a string to a huggingface dataset or a list" - "that is preprocessed with one sample of text per element" - " or a list of list of int for tokenized words." - ) - - samples = [] - n_run = 0 - for data in dataset: - if isinstance(data, list): - line_encoded = data - else: - line = data[text_column] - line = line.strip() - line_encoded = tokenizer.encode(line) - if len(line_encoded) > max_seq_len: - continue - sample = torch.tensor([line_encoded]) - if sample.numel() == 0: - continue - samples.append(sample) - n_run += 1 - if n_run == n_samples: - break - # now concatenate all samples and split according to max sequence length - cat_samples = torch.cat(samples, dim=1) - n_split = cat_samples.shape[1] // max_seq_len - logging.debug(f" * Split into {n_split} blocks") - return [ - cat_samples[:, i * max_seq_len : (i + 1) * max_seq_len] for i in range(n_split) - ] - - @staticmethod - def get_best_device(): - if torch.backends.mps.is_available(): - return "mps" - elif torch.cuda.is_available(): - return "cuda:0" - else: - return "cpu" - - @staticmethod - def clear_memory(weight=None): - if weight is not None: - del weight - gc.collect() - torch.cuda.empty_cache() - - @staticmethod - def get_op_name(module, op): - # get the name of the op relative to the module - for name, m in module.named_modules(): - if m is op: - return name - raise ValueError(f"Cannot find op {op} in module {module}") - - @staticmethod - def append_str_prefix(x, prefix): - if isinstance(x, str): - return prefix + x - elif isinstance(x, tuple): - return tuple([AwqQuantizer.append_str_prefix(y, prefix) for y in x]) - elif isinstance(x, list): - return [AwqQuantizer.append_str_prefix(y, prefix) for y in x] - else: - return x - - def init_quant(self, n_samples=128, max_seq_len=512): - modules = self.awq_model.blocks - samples = AwqQuantizer.get_calib_dataset( - data=self.calib_data, - tokenizer=self.tokenizer, - n_samples=n_samples, - max_seq_len=max_seq_len, - split=self.split - ) - # samples = torch.cat(samples, dim=0) - samples = torch.cat(samples[:1], dim=0) # just using 1 batch - inps = [] - layer_kwargs = {} - # build inps - self.model.seq_len = samples.numel() - self.model.context_len = samples.numel() - 2 - self.model.token_len = 0 - best_device = AwqQuantizer.get_best_device() - inps = self.model.embedding(samples).to(best_device) - position_ids = self.model.get_position_ids() - rotary_pos_emb = self.model.rotary(position_ids) - attention_mask = self.model.get_attention_mask() - layer_kwargs["rotary_pos_emb"] = rotary_pos_emb.to(best_device) - layer_kwargs["attention_mask"] = attention_mask.to(best_device) - del samples - AwqQuantizer.clear_memory() - return modules, layer_kwargs, inps - - def _get_input_feat(self, layer, named_linears): - # firstly, get input features of all linear layers - def cache_input_hook(m, x, y, name, feat_dict): - x = x[0] - x = x.detach().cpu() - feat_dict[name].append(x) - input_feat = defaultdict(list) - handles = [] - for name in named_linears: - handles.append( - named_linears[name].register_forward_hook( - functools.partial(cache_input_hook, name=name, feat_dict=input_feat) - ) - ) - self.inps = self.inps.to(next(layer.parameters()).device) # in case multi-gpu - # get output as next layer's input - - # Sanitize the kwargs in case we use transformers version that contains - # kwargs that are not handled by the module. - # Useful for trust_remote_code models. - module_kwargs = self._sanitize_kwargs(self.module_kwargs, layer) - - self.inps = self._module_forward(self.inps, layer, module_kwargs) - for h in handles: - h.remove() - # now solve for scaling and clipping - input_feat = {k: torch.cat(v, dim=0) for k, v in input_feat.items()} - - return input_feat - - def _sanitize_kwargs(self, inputs_kwargs, module): - """ - Remove the arguments that are not supported in the module's - forward pass to avoid breaking behaviour between different versions - of transformers. - - Args: - inputs_kwargs (`dict`): - The input dictionary to pass to the model layer - module (`torch.nn.Module`): - Target module to quantize. - """ - module_signature = inspect.signature(module.forward).parameters - sanitized_kwargs = {} - for k, v in inputs_kwargs.items(): - if k in module_signature: - sanitized_kwargs[k] = v - return sanitized_kwargs -# awq quantizer end - -# Export class - -# custom op start -class FakeLinearOp(torch.autograd.Function): - @staticmethod - def symbolic(g, input, in_features, out_features, has_bias, name): - # These become the operator attributes. - kwargs = { - "in_features_i": in_features, - "out_features_i": out_features, - "has_bias_i": has_bias, - "name_s": name - } - from torch.onnx.symbolic_helper import _get_tensor_sizes - out_sizes = _get_tensor_sizes(input)[:-1] + [out_features] - output_type = input.type().with_sizes(out_sizes) - return g.op("LlmExporter::FakeLinear", input, **kwargs).setType(output_type) - - @staticmethod - def forward(ctx, input, in_features, out_features, has_bias, name): - out_shape = list(input.shape)[:-1] + [out_features] - return input.new_zeros(out_shape) - -class FakeLinear(torch.nn.Module): - def __init__(self, in_features, out_features, has_bias, name): - super(FakeLinear, self).__init__() - self.in_features = in_features - self.out_features = out_features - self.has_bias = has_bias - self.name = name - - def forward(self, x): - return FakeLinearOp.apply(x, self.in_features, self.out_features, self.has_bias, self.name) - -class FusedAttentionOp(torch.autograd.Function): - @staticmethod - def symbolic(g, query, key, value, attention_mask, hidden_size, name): - # These become the operator attributes. - kwargs = { - "hidden_size_i": hidden_size, - "name_s": name - } - from torch.onnx.symbolic_helper import _get_tensor_sizes - out_sizes = _get_tensor_sizes(query) - output_type = query.type().with_sizes(out_sizes) - return g.op("LlmExporter::FusedAttention", query, key, value, attention_mask, **kwargs).setType(output_type) - - @staticmethod - def forward(ctx, query, key, value, attention_mask, hidden_size, name): - out_shape = list(query.shape)[:2] + [hidden_size] - return query.new_zeros(out_shape) - -class FusedAttention(torch.nn.Module): - def __init__(self, hidden_size, name): - super(FusedAttention, self).__init__() - self.hidden_size = hidden_size - self.name = name - - def forward(self, query, key, value, attention_mask): - return FusedAttentionOp.apply(query, key, value, attention_mask, self.hidden_size, self.name) - -# custom op end - -class OnnxRebuilder: - def __init__(self, onnx_path, weight_ops): - self.weight_ops = weight_ops - self.onnx_model = onnx.load(onnx_path) - self.dst_path = onnx_path - self.onnx_weight_path = f'{onnx_path}.data' - self.onnx_weight_offset = 0 - - def make_external(self, name, data, shape): - # write to external weight - length = self.onnx_weight.write(data.tobytes()) - location = os.path.basename(self.onnx_weight_path) - offset = self.onnx_weight_offset - self.onnx_weight_offset += length - tensor = onnx.TensorProto() - tensor.name = name - tensor.data_type = onnx.TensorProto.FLOAT - tensor.dims.extend(shape) - # external info - tensor.data_location = onnx.TensorProto.EXTERNAL - for k, v in { "location": location, "offset": offset, "length": length }.items(): - entry = tensor.external_data.add() - entry.key = k - entry.value = str(v) - self.onnx_model.graph.initializer.append(tensor) - - def build_weight(self, name, has_bias, ic, oc): - assert(name in self.weight_ops) - linear = self.weight_ops[name] - assert(linear.in_features == ic and - linear.out_features == oc and - (linear.bias is not None) == has_bias) - weight_name, bias_name = f'{name}_weight', f'{name}_bias' - weight = linear.weight.data.transpose(1, 0).flatten().float().numpy() - self.make_external(weight_name, weight, [ic, oc]) - if has_bias: - bias = linear.bias.data.flatten().float().numpy() - self.make_external(bias_name, bias, [oc]) - return weight_name, bias_name - - def rebuild(self): - from onnx import helper - new_nodes = [] - self.onnx_weight = open(self.onnx_weight_path, 'wb') - for node in self.onnx_model.graph.node: - if node.op_type == 'FakeLinear': - attributes = {a.name: a for a in node.attribute} - name = attributes.get('name').s.decode('utf-8') - has_bias = attributes.get('has_bias').i - ic = attributes.get('in_features').i - oc = attributes.get('out_features').i - weight, bias = self.build_weight(name, has_bias, ic, oc) - if has_bias: - # fakelinear -> matmul + add - middle_tensor = f'{name}_matmul' - new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], [middle_tensor], name)) - new_nodes.append(helper.make_node('Add', [middle_tensor, bias], node.output, f'{name}/Add')) - else: - # fakelinear -> matmul - new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], node.output, name)) - else: - new_nodes.append(node) - self.onnx_weight.close() - del self.onnx_model.graph.node[:] - self.onnx_model.graph.node.extend(new_nodes) - onnx.save(self.onnx_model, self.dst_path) - return self.onnx_weight_path - -class MNNConveter: - def __init__(self, onnx_path, weight_ops, config): - self.weight_ops = weight_ops - self.config = config - self.quant_block = config.args.quant_block - self.quant_bit = config.args.quant_bit - self.lm_quant_bit = config.args.lm_quant_bit - self.symmetric = config.args.sym - self.mnn_weight_offset = 0 - self.onnx_model_path = onnx_path - self.mnn_name = os.path.basename(onnx_path).replace('.onnx', '.mnn') - self.mnn_model_path = os.path.join(config.args.dst_path, self.mnn_name) - self.mnn_weight_path = f'{self.mnn_model_path}.weight' - if os.path.exists(config.args.mnnconvert): - self.mnnconvert = config.args.mnnconvert - else: - self.mnnconvert = None - - def convert(self, convert_args): - sfd = os.dup(1) - log_fp = open(EXPORT_LOG, "a") - log_fd = log_fp.fileno() - # mnnconvert ... > .export.log - os.dup2(log_fd, 1) - try: - sys.argv = convert_args - sys.argc = len(convert_args) - if self.mnnconvert is None: - from MNN.tools import mnnconvert - mnnconvert.main() - else: - convert_args[0] = self.mnnconvert - cmd = ' '.join(convert_args) - message = os.popen(cmd).read() - print(message) - sys.argv = [] - finally: - os.dup2(sfd, 1) - os.close(log_fd) - - @spinner_run(f'convert onnx model to ') - def onnx2mnn(self, onnx_path, mnn_path, args = []): - convert_args = [ - '', - '-f', - 'ONNX', - '--modelFile', - str(onnx_path), - '--MNNModel', - str(mnn_path), - '--transformerFuse', - '--allowCustomOp' - ] - convert_args += args - self.convert(convert_args) - return mnn_path - - def mnn2json(self, mnn_path, json_path): - convert_args = [ - '', - '-f', - 'MNN', - '--modelFile', - str(mnn_path), - '--JsonFile', - str(json_path) - ] - self.convert(convert_args) - return json_path - - def json2mnn(self, json_path, mnn_path): - convert_args = [ - '', - '-f', - 'JSON', - '--modelFile', - str(json_path), - '--MNNModel', - str(mnn_path) - ] - self.convert(convert_args) - return mnn_path - - def removeDupOps(self, mnn_path): - convert_args = [ - '', - '-f', - 'MNN', - '--modelFile', - str(mnn_path), - '--MNNModel', - str(mnn_path), - '--optimizeLevel=1' - ] - self.convert(convert_args) - return mnn_path - - def export(self, quant_bit = None, quant_block = None): - if self.weight_ops is None: - if quant_bit is None: - quant_bit = self.quant_bit - if quant_block is None: - quant_block = self.quant_block - if quant_bit == 16: - quant_args = ['--fp16'] - else: - quant_args = [ - '--weightQuantBits', - str(quant_bit), - '--weightQuantBlock', - str(quant_block) - ] - self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, quant_args) - else: - mnn_json = f'{self.mnn_model_path}.json' - self.onnx2mnn(self.onnx_model_path, self.mnn_model_path) - self.mnn2json(self.mnn_model_path, mnn_json) - self.rebuild(mnn_json) - self.json2mnn(mnn_json, self.mnn_model_path) - self.removeDupOps(self.mnn_model_path) - - @spinner_run(f'quant model weight to ', True) - def rebuild(self, json_path): - mnn_graph = json.load(open(json_path, 'rt')) - new_ops = [] - with open(self.mnn_weight_path, 'wb') as self.mnn_weight: - for op in tqdm(mnn_graph['oplists'], 'Quant weights'): - if op['type'] == 'Extra': - new_ops += self.rebuild_op(op, mnn_graph) - else: - new_ops.append(op) - mnn_graph['oplists'] = new_ops - with open(json_path, 'w', encoding='utf-8') as file: - json.dump(mnn_graph, file, ensure_ascii=False, indent=4) - return self.mnn_weight_path - - def quant(self, weight, quant_bit, quant_block, symmetric): - if torch.cuda.is_available(): - weight = weight.cuda() - if torch.backends.mps.is_available(): - weight = weight.to('mps') - oc, ic = weight.shape - if quant_block == 0: - block_size = ic - else: - block_size = quant_block - block_num = ic // block_size - weight = weight.reshape(oc, block_num, block_size) - offset = 1 << (quant_bit - 1) - clip_max = offset - 1 - if symmetric: - clip_min = -clip_max - abs_max, _ = torch.max(torch.abs(weight), axis=-1, keepdims=True) - scale = abs_max / clip_max - q_weight = torch.round(weight / scale) - q_weight = (torch.clamp(q_weight.flatten(), clip_min, clip_max) + offset).to(torch.uint8) - alpha = scale.flatten() - else: - clip_min = -offset - max_val, _ = torch.max(weight, axis=-1, keepdims=True) - min_val, _ = torch.min(weight, axis=-1, keepdims=True) - scale = (max_val - min_val) / (clip_max - clip_min) - - if False: - q_weight = np.round((weight - min_val) / scale) + clip_min - zeros = min_val - scale * clip_min - else: - q_weight = torch.round(weight / scale) - torch.round(min_val / scale) + clip_min - zeros = (torch.round(min_val / scale) - clip_min) * scale - q_weight = (torch.clamp(q_weight.flatten(), clip_min, clip_max) + offset).to(torch.uint8) - alpha = torch.stack([zeros.flatten(), scale.flatten()], axis=-1).flatten() - - q_weight = q_weight.reshape(-1, 2) - if quant_bit == 4: - q_weight = q_weight[:, 0] * 16 + q_weight[:, 1] - - clip_min = 1 - - if q_weight.device is not torch.device('cpu'): - return q_weight.cpu(), alpha.float().cpu(), clip_min - return q_weight, alpha.float(), clip_min - - def write_weight(self, data): - if isinstance(data, torch.Tensor): - data = data.numpy() - return self.mnn_weight.write(data.tobytes()) - - def write_header(self, ic, oc, quant_bit): - dim_num = self.mnn_weight.write(b'\x02') - shape_dtype = np.int16 - if oc > 65535 or ic > 65535: - shape_dtype = np.int32 - dim_length = self.write_weight(np.array([oc, ic]).astype(shape_dtype)) - offset = 1 << (quant_bit - 1) - weight_map = [i for i in range(-offset, offset)] - if len(weight_map) == 256: - weight_map.insert(0, 0) - else: - weight_map.insert(0, len(weight_map)) - map_length = self.write_weight(np.array(weight_map, dtype=np.int8)) - header_length = dim_num + dim_length + map_length - return header_length, shape_dtype == np.int32 - - def build_weight(self, linear, quant_bit, quant_block, symmetric): - ic, oc = linear.in_features, linear.out_features - if quant_bit == 16: - half_weight = linear.weight.data.flatten().half() - weight_len = self.write_weight(half_weight) - alpha_len, q_min, shape_int32 = 0, 0, False - else: - assert(quant_bit in (4, 8)) - q_weight, alpha, q_min = self.quant(linear.weight.data, quant_bit, quant_block, symmetric) - header_len, shape_int32 = self.write_header(ic, oc, quant_bit) - weight_len = self.write_weight(q_weight) + header_len - alpha_len = self.write_weight(alpha) - if linear.bias is not None: - bias = linear.bias.data.flatten().float() - bias_length = self.write_weight(bias) - else: - bias_length = 0 - # bias = np.zeros([oc], dtype=np.float32) - # bias_length = self.write_weight(bias) - external = [self.mnn_weight_offset, weight_len, alpha_len, bias_length, 0] - self.mnn_weight_offset += (weight_len + alpha_len + bias_length) - return external, q_min, shape_int32, header_len - - def build_tensor(self, graph, tensor_name): - tensor_idx = [len(graph['tensorName'])] - graph['tensorName'].append(tensor_name) - return tensor_idx - - def rebuild_op(self, op, graph): - op_type = op['main']['type'] - if op_type == 'FakeLinear': - return self.rebuild_linear(op, graph) - if op_type == 'FusedAttention': - return self.rebuild_attnention(op, graph) - - def rebuild_attnention(self, op, graph): - attrs = op['main']['attr'] - for attr in attrs: - if attr['key'] == 'name': - name = attr['s'] - origin_input = op['inputIndexes'] - origin_output = op['outputIndexes'] - fused_attention = { - "inputIndexes": origin_input, - "main_type": "AttentionParam", - "main": { "kv_cache": True }, - "name": name, - "outputIndexes": origin_output, - "type": "Attention", - "defaultDimentionFormat": "NHWC" - } - return [fused_attention] - - def rebuild_linear(self, op, graph): - attrs = op['main']['attr'] - for attr in attrs: - if attr['key'] == 'name': - name = attr['s'] - elif attr['key'] == "in_features": - ic = attr["i"] - elif attr['key'] == "out_features": - oc = attr["i"] - elif attr['key'] == "has_bias": - has_bias = attr["i"] - linear = self.weight_ops[name] - assert(linear.in_features == ic and - linear.out_features == oc and - (linear.bias is not None) == has_bias) - - is_lm = 'lm_head' in name - quant_bit = self.lm_quant_bit if is_lm else self.quant_bit - block_size = ic if self.quant_block == 0 else self.quant_block - external, q_min, shape_int32, header_len = self.build_weight(linear, quant_bit, self.quant_block, self.symmetric) - if is_lm and self.config.tie_word_embeddings: - weight_offset = external[0] + header_len - alpha_offset = external[0] + external[1] - alpha_size = external[2] - self.config.llm_config['tie_embeddings'] = [weight_offset, alpha_offset, alpha_size, quant_bit, self.quant_block] - - origin_input = op['inputIndexes'] - origin_output = op['outputIndexes'] - # build new tensor - pre_reshape_name = f'{name}/pre_reshape' - pre_convert_name = f'{name}/pre_convert' - conv_name = name - post_convert_name = f'{name}/post_convert' - post_reshape_name = f'{name}/post_reshape' - pre_reshape_output = self.build_tensor(graph, pre_reshape_name) - pre_convert_output = self.build_tensor(graph, pre_convert_name) - conv_output = self.build_tensor(graph, conv_name) - post_convert_output = self.build_tensor(graph, post_convert_name) - # [batch, seq, hidden_size_i] -[Linear] -> [batch, seq, hidden_size_o] - # [1, seq, hidden_size_i] ->[Reshape]-> [seq, hidden_size_i, 1, 1] - # -[Convert]-[Convolution]-[Convert]-> [Reshape] -> [1, seq, hidden_size_o] - pre_reshape = { - "name": pre_reshape_name, - "type": "Reshape", - "inputIndexes": origin_input, - "outputIndexes": pre_reshape_output, - "main_type": "Reshape", - "main": { - "dims": [-1, ic, 1, 1], - "dimType": "NCHW" - }, - "defaultDimentionFormat": "NHWC" - } - pre_convert = { - "name": pre_convert_name, - "inputIndexes": pre_reshape_output, - "outputIndexes": pre_convert_output, - "type": "ConvertTensor", - "main_type": "TensorConvertInfo", - "main": { - "source": "NCHW", - "dest": "NC4HW4" - }, - "defaultDimentionFormat": "NHWC" - } - - if quant_bit == 16: - quanParameter = { "type": 3 } - else: - if self.symmetric: - aMin = 0 - readType = 0 - else: - aMin = q_min - readType = oc * (ic // block_size) - - quanParameter = { - "quantScale": 1.0, "scaleIn": 0.0, "scaleOut": 0.0, - "useInt32": False, "has_scaleInt": False, "shapeInt32": shape_int32, - "type": 1, "aMax": 0, "aMin": aMin, "readType": readType, "weightSize": 0 - } - conv_op = { - "name": conv_name, - "inputIndexes": pre_convert_output, - "outputIndexes": conv_output, - "type": "Convolution", - "main_type": "Convolution2D", - "main": { - 'common': { - 'dilateX': 1, 'dilateY': 1, 'strideX': 1, 'strideY': 1, - 'kernelX': 1, 'kernelY': 1, 'padX': 0, 'padY': 0, 'group': 1, - 'outputCount': oc, 'relu': False, 'padMode': 'CAFFE', - 'relu6': False, 'inputCount': ic, 'hasOutputShape': False - }, - "quanParameter": quanParameter, - "external": external - }, - "defaultDimentionFormat": "NHWC" - } - post_convert = { - "name": post_convert_name, - "inputIndexes": conv_output, - "outputIndexes": post_convert_output, - "type": "ConvertTensor", - "main_type": "TensorConvertInfo", - "main": { - "source": "NC4HW4", - "dest": "NCHW" - }, - "defaultDimentionFormat": "NHWC" - } - post_reshape = { - "name": post_reshape_name, - "type": "Reshape", - "inputIndexes": post_convert_output, - "outputIndexes": origin_output, - "main_type": "Reshape", - "main": { - "dims": [1, -1, oc], - "dimType": "NCHW" - }, - "defaultDimentionFormat": "NHWC" - } - return [pre_reshape, pre_convert, conv_op, post_convert, post_reshape] - -# some wrapper class for export -class Embedding(torch.nn.Module): - def __init__(self, embed, config): - super().__init__() - self.hidden_size = config.hidden_size - self.embed = embed - if config.model_type == 'gemma2': - normalizer = torch.tensor(self.hidden_size**0.5) - self.embed.weight.data *= normalizer - - def forward(self, input_ids): - inputs_embeds = self.embed(input_ids).view(-1, 1, self.hidden_size) - return inputs_embeds - -def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: - batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) - return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) - -class Attention(torch.nn.Module): - def __init__(self, attn, layer_id, config): - super().__init__() - self.export_fused_attn = False - self.fused_attn = FusedAttention(config.hidden_size, f'/layers.{layer_id}/self_attn/FusedAttention') - self.layer_id = layer_id - self.hidden_size = config.hidden_size - self.head_dim = config.head_dim - if isinstance(config.num_attention_heads, list): - self.num_heads = config.num_attention_heads[layer_id] - self.num_key_value_heads = config.num_key_value_heads[layer_id] - else: - self.head_dim = config.head_dim - self.num_heads = config.num_attention_heads - self.num_key_value_heads = config.num_key_value_heads - self.num_key_value_groups = self.num_heads // self.num_key_value_heads - self.rotary = config.rotary - - ModelMapper.do_map(self, attn, config.model_map['attention']) - - if hasattr(self, 'qkv_proj') and self.qkv_proj is not None: - # split qkv linear to q, k, v - split_sizes = [self.hidden_size] * 3 - if self.qkv_proj.weight.shape[0] != self.hidden_size * 3: - # M/GQA - split_sizes = [ - self.num_heads * self.head_dim, # q_size - self.num_key_value_heads * self.head_dim, # k_size - self.num_key_value_heads * self.head_dim # v_size - ] - - self.q_proj = torch.nn.Linear(self.hidden_size, split_sizes[0]) - self.k_proj = torch.nn.Linear(self.hidden_size, split_sizes[1]) - self.v_proj = torch.nn.Linear(self.hidden_size, split_sizes[2]) - if config.model_type == 'chatglm': - # chatglm-6b - qkv_weight = self.qkv_proj.weight.data.view(self.num_heads, 3, self.head_dim, self.hidden_size) - self.q_proj.weight.data = qkv_weight[:, 0, :, :].reshape(self.hidden_size, self.hidden_size) - self.k_proj.weight.data = qkv_weight[:, 1, :, :].reshape(self.hidden_size, self.hidden_size) - self.v_proj.weight.data = qkv_weight[:, 2, :, :].reshape(self.hidden_size, self.hidden_size) - qkv_bias = self.qkv_proj.bias.data.view(self.num_heads, 3, self.head_dim) - self.q_proj.bias.data = qkv_bias[:, 0, :].reshape(self.hidden_size) - self.k_proj.bias.data = qkv_bias[:, 1, :].reshape(self.hidden_size) - self.v_proj.bias.data = qkv_bias[:, 2, :].reshape(self.hidden_size) - else: - # other - qw, kw, vw = torch.split(self.qkv_proj.weight, split_sizes) - self.q_proj.weight.data = qw - self.k_proj.weight.data = kw - self.v_proj.weight.data = vw - if self.qkv_proj.bias is not None: - qb, kb, vb = torch.split(self.qkv_proj.bias, split_sizes) - self.q_proj.bias.data = qb - self.k_proj.bias.data = kb - self.v_proj.bias.data = vb - else: - self.q_proj.bias.data = torch.zeros(split_sizes[0]) - self.k_proj.bias.data = torch.zeros(split_sizes[1]) - self.v_proj.bias.data = torch.zeros(split_sizes[2]) - self.q_proj.weight.requires_grad = False - self.k_proj.weight.requires_grad = False - self.v_proj.weight.requires_grad = False - self.q_proj.bias.requires_grad = False - self.k_proj.bias.requires_grad = False - self.v_proj.bias.requires_grad = False - - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - rotary_pos_emb: Optional[torch.Tensor] = None, - cross_attention_states: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - bsz, q_len, _ = hidden_states.size() - query_states = self.q_proj(hidden_states) - if cross_attention_states is not None: - hidden_states = cross_attention_states - key_states = self.k_proj(hidden_states) - value_states = self.v_proj(hidden_states) - query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim) - key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) - value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) - # openelm model has qk_norm - if hasattr(self, 'q_norm') and self.q_norm is not None and \ - hasattr(self, 'k_norm') and self.k_norm is not None : - query_states = self.q_norm(query_states) - key_states = self.k_norm(key_states) - - kv_seq_len = key_states.shape[1] - if past_key_value is not None: - kv_seq_len += past_key_value[0].shape[1] - - # rope - cos, sin = rotary_pos_emb[0], rotary_pos_emb[1] - query_states = self.rotary.apply_rotary_pos(query_states, cos, sin) - key_states = self.rotary.apply_rotary_pos(key_states, cos, sin) - - if self.export_fused_attn: - attn_output = self.fused_attn(query_states, key_states, value_states, attention_mask) - attn_output = self.o_proj(attn_output) - return attn_output, past_key_value - - # kv cache - if past_key_value is not None: - past_key, past_value = past_key_value[0], past_key_value[1] - key_states = torch.cat((past_key, key_states), dim=1) - value_states = torch.cat((past_value, value_states), dim=1) - - past_key_value = torch.stack((key_states, value_states)) - query_states = query_states.transpose(1, 2) - key_states = key_states.permute([0, 2, 3, 1]) - value_states = value_states.transpose(1, 2) - # repeat k/v heads if n_kv_heads < n_heads - key_states = repeat_kv(key_states, self.num_key_value_groups) - value_states = repeat_kv(value_states, self.num_key_value_groups) - #------- attention ---------- - # query_states @ key_states - attn_weights = torch.matmul(query_states, key_states) / math.sqrt(self.head_dim) - # attention_mask - if attention_mask.dtype in (torch.bool, torch.int32): - # chatglm - attn_weights.masked_fill_(attention_mask, -10000.0) - else: - attn_weights = attn_weights + attention_mask - # upcast softmax to fp32 - attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) - # attn_weights @ value_states - attn_output = torch.matmul(attn_weights, value_states) - - attn_output = attn_output.transpose(1, 2).contiguous() - attn_output = attn_output.reshape(bsz, q_len, -1) - attn_output = self.o_proj(attn_output) - return attn_output, past_key_value - -def rotate_half(x): - x1 = x[..., : x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2 :] - return torch.cat((-x2, x1), dim=-1) - -class Rotary(torch.nn.Module): - def __init__(self, config): - super().__init__() - self.rope_theta = config.rope_theta - self.rotary_dim = config.head_dim - self.model_type = config.model_type - if hasattr(config, 'rotary_dim'): - self.rotary_dim = config.rotary_dim - if self.model_type == 'chatglm': - self.rotary_dim = config.head_dim // 2 - self.theta = 1.0 / (self.rope_theta ** (torch.arange(0, self.rotary_dim, 2, dtype=torch.float32) / self.rotary_dim)) - - def forward(self, position_ids): - position_ids = position_ids.float().reshape(-1, 1) - idx_theta = position_ids * self.theta - rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)]) - if self.model_type != 'chatglm2': - rotary_pos_emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - rotary_pos_emb = rotary_pos_emb.unsqueeze(2).unsqueeze(1) - return rotary_pos_emb - - def apply_rotary_pos(self, x, cos, sin): - if self.model_type == 'chatglm': - return self.chatglm_rotary_pos(x, cos, sin) - if self.model_type == 'chatglm2': - return self.chatglm2_rotary_pos(x, cos, sin) - if self.model_type == 'phi-msft': - return self.phi_rotary_pos(x, cos, sin) - return self.llama_rotary_pos(x, cos, sin) - - def llama_rotary_pos(self, x, cos, sin): - x = (x * cos) + (rotate_half(x) * sin) - return x - - def phi_rotary_pos(self, x, cos, sin): - x, x_pass = x[..., :self.rotary_dim], x[..., self.rotary_dim:] - x = (x * cos) + (rotate_half(x) * sin) - return torch.cat((x, x_pass), dim=-1) - - def chatglm2_rotary_pos(self, x, cos, sin): - x, x_pass = x[..., :self.rotary_dim], x[..., self.rotary_dim:] - b, s, n, h = x.shape - xshaped = x.view(b, s, n, h//2, 2) - x = torch.concat( - [ - xshaped[..., 0] * cos - xshaped[..., 1] * sin, - xshaped[..., 1] * cos + xshaped[..., 0] * sin, - ], - -1, - ) - return torch.cat((x, x_pass), dim=-1) - - def chatglm_rotary_pos(self, x, cos, sin): - seq = x.shape[1] - x1, x2 = x[..., :self.rotary_dim], x[..., self.rotary_dim:] - cos1, sin1 = cos[:, :seq, ...], sin[:, :seq, ...] - cos2, sin2 = cos[:, seq:, ...], sin[:, seq:, ...] - x1 = (x1 * cos1) + (rotate_half(x1) * sin1) - x2 = (x2 * cos2) + (rotate_half(x2) * sin2) - return torch.cat((x1, x2), dim=-1) - -class VisionRotary(Rotary): - def __init__(self, config): - super().__init__(config) - - # support [h_pos, w_pos] - def forward(self, position_ids): - # [2, patch_len, 1] - position_ids = position_ids.float().unsqueeze(-1) - idx_theta = position_ids * self.theta - # [patch_len, rotary_dim] - idx_theta = idx_theta.permute(1, 0, 2).reshape(-1, self.rotary_dim) - rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)]) - rotary_pos_emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - rotary_pos_emb = rotary_pos_emb.unsqueeze(2).unsqueeze(1) - return rotary_pos_emb - -class Decoder(torch.nn.Module): - def __init__(self, decoder, layer_id, config): - super().__init__() - self.cross_decoder = False - ModelMapper.do_map(self, decoder, config.model_map['decoder']) - # mllama has cross_attn - if hasattr(self, 'cross_attn') and self.cross_attn is not None: - self.cross_decoder = True - self.self_attn = Attention(self.cross_attn, layer_id, config) - else: - self.self_attn = Attention(self.self_attn, layer_id, config) - self.hidden_size = config.hidden_size - # chatglm - self.alpha = (2 * config.num_hidden_layers) ** 0.5 if config.model_type == 'chatglm' else 1.0 - - def forward( - self, - hidden_states: torch.Tensor, - rotary_pos_emb: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - cross_attention_states: Optional[torch.Tensor] = None, - cross_attention_mask: Optional[torch.Tensor] = None, - ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: - hidden_states = hidden_states.view(1, -1, self.hidden_size) - residual = hidden_states - hidden_states = self.input_layernorm(hidden_states) - norm_hidden_states = hidden_states - # Self Attention - hidden_states, present_key_value = self.self_attn( - hidden_states=hidden_states, - rotary_pos_emb=rotary_pos_emb, - attention_mask=attention_mask, - past_key_value=past_key_value, - cross_attention_states=cross_attention_states, - ) - # Fully Connected - if not hasattr(self, 'post_attention_layernorm'): - # phi - feed_forward_hidden_states = self.mlp(norm_hidden_states) - hidden_states = hidden_states + feed_forward_hidden_states + residual - elif self.alpha != 1.0: - # chatglm-6b - hidden_states = norm_hidden_states * self.alpha + hidden_states - mlp_input = self.post_attention_layernorm(hidden_states) - mlp_output = self.mlp(mlp_input) - hidden_states = mlp_input * self.alpha + mlp_output - elif hasattr(self, 'pre_feedforward_layernorm'): - # gemma2 - hidden_states = self.post_attention_layernorm(hidden_states) - hidden_states = residual + hidden_states - residual = hidden_states - hidden_states = self.pre_feedforward_layernorm(hidden_states) - hidden_states = self.mlp(hidden_states) - hidden_states = self.post_feedforward_layernorm(hidden_states) - hidden_states = residual + hidden_states - elif cross_attention_mask is not None and self.cross_decoder: - hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states - residual = hidden_states - hidden_states = self.post_attention_layernorm(hidden_states) - hidden_states = self.mlp(hidden_states) - hidden_states = cross_attention_mask * hidden_states - hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states - else: - # general - hidden_states = residual + hidden_states - residual = hidden_states - hidden_states = self.post_attention_layernorm(hidden_states) - hidden_states = self.mlp(hidden_states) - hidden_states = residual + hidden_states - - return hidden_states, present_key_value - -class Lm(torch.nn.Module): - def __init__(self, lm_, final_layernorm_, config): - super().__init__() - self.final_layernorm = final_layernorm_ - self.lm = lm_ - self.hidden_size = config.hidden_size - self.ppl = config.args.ppl - - def forward(self, hidden_states): - if not self.ppl: - # just need last logit for predict next token - hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) - hidden_states = self.final_layernorm(hidden_states) - m_logits = self.lm(hidden_states) - return m_logits - -# visual start -class Visual(torch.nn.Module): - def __init__(self, visual, base): - super().__init__() - self.model_type = base.model_type - self.visual = visual.eval() - self.embed_ = base.embed - self.tokenizer = base.tokenizer - self.config = base.config - self.hidden_size = base.hidden_size - self.llm_config = base.llm_config - # mllama - self.cross_attention_states = None - self.cross_attention_mask = None - self.init_config() - self.load() - - @staticmethod - def get_visual(model_type): - visual_models = { - 'qwen': QwenVisual, - 'qwen2_vl': Qwen2Visual, - 'mllama': MllamaVision - } - if model_type in visual_models: - return visual_models[model_type] - return None - - def init_config(self): - from transformers.image_utils import (OPENAI_CLIP_MEAN, OPENAI_CLIP_STD) - self.llm_config['is_visual'] = True - image_mean = np.array(OPENAI_CLIP_MEAN) * 255.0 - image_norm = 1 / (np.array(OPENAI_CLIP_STD) * 255.0) - self.llm_config['image_mean'] = image_mean.tolist() - self.llm_config['image_norm'] = image_norm.tolist() - - def export(self, onnx_path): - raise NotImplementedError - - def load(self): - raise NotImplementedError - - def str_to_ids(self, prompt): - input_ids = self.tokenizer(prompt, return_tensors="pt")['input_ids'] - return input_ids - - def forward(self, images): - raise NotImplementedError - - def embed(self, input_ids, images = None, videos = None): - raise NotImplementedError - -class QwenVisual(Visual): - def __init__(self, visual, base): - self.quant_bit = 16 - super().__init__(visual, base) - - def load(self): - self.image_start_id = self.config.visual['image_start_id'] - self.image_size = self.config.visual['image_size'] - self.llm_config['is_visual'] = True - self.llm_config['image_size'] = self.image_size - self.llm_config['vision_start'] = self.tokenizer.img_start_id - self.llm_config['vision_end'] = self.tokenizer.img_end_id - self.llm_config['image_pad'] = self.tokenizer.img_pad_id - - def export(self, onnx_path): - input_images = torch.randn((1, 3, self.image_size, self.image_size)) - onnx_model = f'{onnx_path}/visual.onnx' - torch.onnx.export(self, (input_images), - onnx_model, - input_names=['input_images'], - output_names=['image_embeds'], - dynamic_axes={ - "input_images": { 0: "size" }, - }, - do_constant_folding=True, - verbose=False, - opset_version=15) - return onnx_model - - def forward(self, images): - return self.visual(images).transpose(1, 0) - - def embed(self, input_ids, images = None, videos = None): - if not torch.any(input_ids == self.image_start_id): - return self.embed_(input_ids) - bos_pos = torch.where(input_ids == self.image_start_id) - eos_pos = torch.where(input_ids == self.image_start_id + 1) - img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) - images = [] - for i, a, b in img_pos: - image = input_ids[i][a + 1 : b - 1].tolist() - image = image[ : image.index(self.image_start_id + 2)] - images.append(bytes(image).decode('utf-8')) - images = self.visual.encode(images).transpose(1, 0) - hidden_states = self.embed_(input_ids) - for idx, (i, a, b) in enumerate(img_pos): - hidden_states[a + 1 : b, i] = images[:, idx] - return hidden_states - -class Qwen2Visual(Visual): - def __init__(self, visual, base): - self.quant_bit = 4 - self.temporal_patch_size = 2 - self.patch_size = 14 - self.merge_size = 2 - self.image_height = 420 - self.image_width = 420 - self.image_embeds = None - super().__init__(visual, base) - - def load(self): - self.vision_start_id = self.config.vision_start_token_id - self.vision_end_id = self.config.vision_end_token_id - self.image_pad_id = self.config.image_token_id - self.llm_config['image_size'] = self.image_height - self.llm_config['vision_start'] = self.vision_start_id - self.llm_config['vision_end'] = self.vision_end_id - self.llm_config['image_pad'] = self.image_pad_id - # load model - config = self.visual.config - self.hidden_size = config.embed_dim - self.num_attention_heads = config.num_heads - self.num_key_value_heads = config.num_heads - self.head_dim = self.hidden_size // self.num_attention_heads - self.rope_theta = 10000.0 - self.rotary_dim = self.head_dim // 2 - self.rotary = VisionRotary(self) - self.model_map = { - 'decoder': { - 'self_attn': 'attn', - 'mlp': 'mlp', - 'input_layernorm': 'norm1', - 'post_attention_layernorm': 'norm2' - }, - 'attention': { - 'qkv_proj': 'qkv', - 'o_proj': 'proj' - } - } - self.patch_embed = self.visual.patch_embed - self.blocks = [] - for block in self.visual.blocks.children(): - layer_id = len(self.blocks) - self.blocks.append(Decoder(block, layer_id, self)) - self.merger = self.visual.merger - - def str_to_ids(self, prompt): - if '' in prompt and '' in prompt: - import re - import requests - from PIL import Image - pattern = r'(.*?)' - parts = re.split(pattern, prompt) - txt_prompt = '' - for part in parts: - if re.match(pattern, part): - img_content = re.search(r'(.*?)', part).group(1) - # find in image_content - match = re.search(r'(.*?)', img_content) - img_content = img_content[:match.start()] + img_content[match.end():] - hw = match.group(1).split(',') - self.image_height, self.image_width = int(hw[0]), int(hw[1]) - if img_content.startswith('http://') or img_content.startswith('https://'): - image_obj = Image.open(requests.get(img_content, stream=True).raw) - img_pad_len = self.img_process(image_obj) - img_pad_str = '<|image_pad|>' * img_pad_len - img_str = f'<|vision_start|>{img_pad_str}<|vision_end|>' - txt_prompt += img_str - else: - txt_prompt += part - else: - txt_prompt = prompt - input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] - return input_ids - - def forward(self, flatten_patches, position_ids, attention_mask): - rotary_pos_emb = self.rotary(position_ids) - hidden_states = self.patch_embed(flatten_patches) - if rotary_pos_emb.dtype != hidden_states.dtype: - rotary_pos_emb = rotary_pos_emb.to(hidden_states.dtype) - for blk in self.blocks: - hidden_states, _ = blk(hidden_states, rotary_pos_emb=rotary_pos_emb, attention_mask=attention_mask) - image_embeds = self.merger(hidden_states) - image_embeds = image_embeds.unsqueeze(1) - return image_embeds - - def images_forward(self, images): - images = [images] * self.temporal_patch_size - patches = torch.concat(images, axis=0) - _, channel, height, width = patches.shape - grid_t = patches.shape[0] // self.temporal_patch_size - grid_h, grid_w = height // self.patch_size, width // self.patch_size - patches = patches.reshape( - grid_t, - self.temporal_patch_size, - channel, - grid_h // self.merge_size, - self.merge_size, - self.patch_size, - grid_w // self.merge_size, - self.merge_size, - self.patch_size, - ) - patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8) - flatten_patches = patches.reshape( - grid_t * grid_h * grid_w, channel * self.temporal_patch_size * self.patch_size * self.patch_size - ) - image_grid_thw = torch.tensor([[grid_t, grid_h, grid_w]]) - pos_ids = [] - for t, h, w in image_grid_thw: - hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) - hpos_ids = hpos_ids.reshape( - h // self.merge_size, - self.merge_size, - w // self.merge_size, - self.merge_size, - ) - hpos_ids = hpos_ids.permute(0, 2, 1, 3) - hpos_ids = hpos_ids.flatten() - - wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) - wpos_ids = wpos_ids.reshape( - h // self.merge_size, - self.merge_size, - w // self.merge_size, - self.merge_size, - ) - wpos_ids = wpos_ids.permute(0, 2, 1, 3) - wpos_ids = wpos_ids.flatten() - pos_ids.append(torch.stack([hpos_ids, wpos_ids])) - position_ids = torch.cat(pos_ids, dim=0) - seq_len = grid_t * grid_h * grid_w - attention_mask = torch.zeros([1, seq_len, seq_len], dtype=torch.float) - return self.forward(flatten_patches, position_ids, attention_mask) - - def smart_resize(self, height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280): - if height < factor or width < factor: - raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") - elif max(height, width) / min(height, width) > 200: - raise ValueError( - f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" - ) - h_bar = round(height / factor) * factor - w_bar = round(width / factor) * factor - if h_bar * w_bar > max_pixels: - beta = math.sqrt((height * width) / max_pixels) - h_bar = math.floor(height / beta / factor) * factor - w_bar = math.floor(width / beta / factor) * factor - elif h_bar * w_bar < min_pixels: - beta = math.sqrt(min_pixels / (height * width)) - h_bar = math.ceil(height * beta / factor) * factor - w_bar = math.ceil(width * beta / factor) * factor - return h_bar, w_bar - - def img_process(self, image): - from transformers.image_transforms import ( - convert_to_rgb, - resize, - rescale, - normalize - ) - from transformers.image_utils import ( - OPENAI_CLIP_MEAN, - OPENAI_CLIP_STD, - PILImageResampling, - infer_channel_dimension_format, - to_numpy_array - ) - image = convert_to_rgb(image) - image = to_numpy_array(image) - resized_height, resized_width = self.smart_resize(self.image_height, self.image_width) - format = infer_channel_dimension_format(image) - resample = PILImageResampling.BICUBIC - image = resize(image, size=(resized_height, resized_width), resample=resample, input_data_format=format) - image = rescale(image, scale=1 / 255.0, input_data_format=format) - image = normalize(image=image, mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_data_format=format) - image = np.expand_dims(image, [0]) - image = image.transpose(0, 3, 1, 2) - image = torch.from_numpy(image) - self.image_embeds = self.images_forward(image) - return self.image_embeds.shape[0] - - def embed(self, input_ids, images = None, videos = None): - input_embeds = self.embed_(input_ids) - if self.image_embeds is not None: - image_mask = (input_ids == self.image_pad_id).squeeze() - input_embeds[image_mask] = self.image_embeds - return input_embeds - - def export(self, onnx_path): - patch = torch.randn([900, 1176]) - posision_ids = torch.zeros([2, 900], dtype=torch.int32) - attention_mask = torch.zeros([1, 900, 900], dtype=torch.float) - onnx_model = f'{onnx_path}/visual.onnx' - torch.onnx.export(self, (patch, posision_ids, attention_mask), - onnx_model, - input_names=['patches', 'position_ids', 'attention_mask'], - output_names=['image_embeds'], - dynamic_axes={ - "patches": { 0: "size" }, - "position_ids": { 1: "size" }, - "attention_mask": { 1: "size", 2: "size" } - }, - do_constant_folding=True, - verbose=False, - opset_version=15) - return onnx_model - -class MllamaVision(Visual): - def __init__(self, visual, base): - super().__init__(visual, base) - self.multi_modal_projector = base.multi_modal_projector - self.image_objs = [] - - def load(self): - self.llm_config['is_visual'] = True - self.llm_config['image_size'] = self.config.vision_config.image_size - self.image_size = self.config.vision_config.image_size - - def str_to_ids(self, prompt): - if '' in prompt and '' in prompt: - import re - import requests - from PIL import Image - pattern = r'(.*?)' - parts = re.split(pattern, prompt) - txt_prompt = '' - for part in parts: - if re.match(pattern, part): - img_content = re.search(r'(.*?)', part).group(1) - if img_content.startswith('http://') or img_content.startswith('https://'): - self.image_objs.append(Image.open(requests.get(img_content, stream=True).raw)) - txt_prompt += '<|image|>' - else: - txt_prompt += part - else: - txt_prompt = prompt - input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] - # image process - for img in self.image_objs: - self.img_process(img) - return input_ids - - def img_process(self, image): - self.image_size = 560 - resized_height = self.image_size - resized_width = self.image_size - from transformers.image_transforms import ( - convert_to_rgb, - resize, - rescale, - normalize - ) - from transformers.image_utils import ( - OPENAI_CLIP_MEAN, - OPENAI_CLIP_STD, - PILImageResampling, - infer_channel_dimension_format, - to_numpy_array - ) - image = convert_to_rgb(image) - image = to_numpy_array(image) - format = infer_channel_dimension_format(image) - resample = PILImageResampling.BICUBIC - image = resize(image, size=(resized_height, resized_width), resample=resample, input_data_format=format) - image = rescale(image, scale=1 / 255.0, input_data_format=format) - image = normalize(image=image, mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_data_format=format) - image = image.transpose(2, 0, 1) - image = np.expand_dims(image, [0, 1, 2]) - pad_val = np.zeros_like(image) - image = np.concatenate([image, pad_val, pad_val, pad_val], axis=2) - image = torch.from_numpy(image) - self.cross_attention_states = self.forward(image) - - def forward(self, images): - aspect_ratio_ids = torch.tensor([[1]]) - aspect_ratio_mask = torch.tensor([[[1, 0, 0, 0]]]) - vision_outputs = self.visual(images, aspect_ratio_ids, aspect_ratio_mask) - cross_attention_states = vision_outputs[0] - cross_attention_states = cross_attention_states.type(self.multi_modal_projector.weight.dtype) - cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape( - -1, cross_attention_states.shape[-2], self.hidden_size) - return cross_attention_states - - def embed(self, input_ids, images = None, videos = None): - txt_embeds = self.embed_(input_ids) - return txt_embeds -# visual end - -# audio start -class Audio(torch.nn.Module): - def __init__(self, audio, base): - super().__init__() - self.audio = audio - self.embed_ = base.embed - self.tokenizer = base.tokenizer - self.config = base.config - self.hidden_size = base.hidden_size - self.llm_config = base.llm_config - self.quant_bit = 16 - self.init_config() - self.load() - - @staticmethod - def get_audio(model_type): - audio_models = { - 'qwen2_audio': Qwen2Audio, - } - if model_type in audio_models: - return audio_models[model_type] - return None - - def init_config(self): - self.llm_config['is_audio'] = True - - def load(self): - raise NotImplementedError - - def str_to_ids(self, prompt): - input_ids = self.tokenizer(prompt, return_tensors="pt")['input_ids'] - return input_ids - - def forward(self, images): - raise NotImplementedError - - def embed(self, input_ids, images = None, videos = None): - raise NotImplementedError - -class Qwen2Audio(Audio): - def __init__(self, audio, base): - super().__init__(audio, base) - self.audio_embeds = None - self.audio_pad_id = 151646 - self.n_fft = 400 - self.sampling_rate = 16000 - self.hop_length = 160 - self.chunk_length = 30 - self.feature_size = 128 - self.n_samples = self.chunk_length * self.sampling_rate - self.max_length = self.n_samples // self.hop_length - from transformers.audio_utils import mel_filter_bank - self.mel_filters = mel_filter_bank( - num_frequency_bins=1 + self.n_fft // 2, - num_mel_filters=self.feature_size, - min_frequency=0.0, - max_frequency=8000.0, - sampling_rate=self.sampling_rate, - norm="slaney", - mel_scale="slaney", - ) - - def load(self): - # model - self.audio_tower = self.audio.audio_tower - self.multi_modal_projector = self.audio.multi_modal_projector - # config - self.llm_config['is_audio'] = True - - def str_to_ids(self, prompt): - if '' in prompt: - import re - from io import BytesIO - from urllib.request import urlopen - import librosa - pattern = r'()' - parts = re.split(pattern, prompt) - txt_prompt = '' - for part in parts: - if re.match(pattern, part): - audio_content = re.search(r'', part).group(1) - if audio_content.startswith('http://') or audio_content.startswith('https://'): - audio_obj = librosa.load(BytesIO(urlopen(audio_content).read()), sr=self.sampling_rate)[0] - audio_embed_len = self.audio_process(audio_obj) - audio_pad_str = '<|AUDIO|>' * audio_embed_len - txt_prompt += audio_pad_str - else: - txt_prompt += part - else: - txt_prompt = prompt - input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] - return input_ids - - def forward(self, input_features): - input_features = input_features.to(dtype=self.audio_tower.conv1.weight.dtype, device=self.audio_tower.conv1.weight.device) - inputs_embeds = torch.nn.functional.gelu(self.audio_tower.conv1(input_features)) - inputs_embeds = torch.nn.functional.gelu(self.audio_tower.conv2(inputs_embeds)) - inputs_embeds = inputs_embeds.permute(0, 2, 1) - _, seq_len, _ = inputs_embeds.shape - embed_pos = self.audio_tower.embed_positions.weight[:seq_len, :] - hidden_states = inputs_embeds + embed_pos - for encoder_layer in self.audio_tower.layers: - hidden_states = encoder_layer(hidden_states, None, None)[0] - hidden_states = hidden_states.permute(0, 2, 1) - hidden_states = self.audio_tower.avg_pooler(hidden_states) - hidden_states = hidden_states.permute(0, 2, 1) - hidden_states = self.audio_tower.layer_norm(hidden_states) - audio_features = self.multi_modal_projector(hidden_states) - return audio_features - - def _torch_extract_fbank_features(self, waveform): - window = torch.hann_window(self.n_fft) - stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True) - magnitudes = stft[..., :-1].abs() ** 2 - mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32) - mel_spec = mel_filters.T @ magnitudes - log_spec = torch.clamp(mel_spec, min=1e-10).log10() - if waveform.dim() == 2: - max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0] - log_spec = torch.maximum(log_spec, max_val - 8.0) - else: - log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) - log_spec = (log_spec + 4.0) / 4.0 - return log_spec - - def audio_process(self, audio_obj): - # audio_obj = np.pad(audio_obj, (0, self.n_samples - audio_obj.shape[0])) - waveform = torch.from_numpy(audio_obj).type(torch.float32) - input_features = self._torch_extract_fbank_features(waveform).unsqueeze(0) - audio_embeds = self.forward(input_features) - self.audio_embeds = audio_embeds.permute([1, 0, 2]) - return self.audio_embeds.shape[0] - - def embed(self, input_ids, images = None, videos = None): - input_embeds = self.embed_(input_ids) - if self.audio_embeds is not None: - audio_mask = (input_ids == self.audio_pad_id).squeeze() - input_embeds[audio_mask] = self.audio_embeds.type(input_embeds.dtype) - return input_embeds -# audio end +from utils.spinner import spinner_run +from utils.custom_op import FakeLinear +from utils.onnx_rebuilder import OnnxRebuilder +from utils.mnn_converter import MNNConveter +from utils.awq_quantizer import AwqQuantizer +from utils.model_mapper import ModelMapper +from utils.transformers import Embedding, Rotary, Decoder, Lm class LlmExporter(torch.nn.Module): ''' Base class for all llm model export. Inherits from [`torch.nn.Module`]. ''' - def __init__(self, args): super().__init__() self.init_from_args(args) @@ -2569,13 +60,16 @@ def load_pretrained(self, model_path: str): elif 'Llama-3.2' in model_path and 'Vision' in model_path: from transformers import MllamaForConditionalGeneration self.model = MllamaForConditionalGeneration.from_pretrained(model_path, torch_dtype='auto').eval() + elif 'Llama' in model_path or 'Yi' in model_path: + from transformers import LlamaForCausalLM + self.model = LlamaForCausalLM.from_pretrained(model_path, torch_dtype='auto', trust_remote_code=True).eval() else: try: self.model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype='auto', trust_remote_code=True).eval() except: self.model = AutoModel.from_pretrained(model_path, torch_dtype='auto', trust_remote_code=True).eval() self.config = self.model.config - if self.args.lora_path is not None: + if self.args.lora_path is not None and not self.args.lora_split: from peft import PeftModel adapter = PeftModel.from_pretrained(self.model, model_id=self.args.lora_path) self.model = adapter.merge_and_unload(progressbar=True) @@ -2592,9 +86,12 @@ def load_model(self, model_path): self.stop_ids.append(self.tokenizer.eos_token_id) if hasattr(self.tokenizer, 'im_end_id'): self.stop_ids.append(self.tokenizer.im_end_id) - eot_id = self.tokenizer.encode('<|eot_id|>') - if len(eot_id) == 1: - self.stop_ids.append(eot_id[0]) + try: + eot_id = self.tokenizer.encode('<|eot_id|>') + if len(eot_id) == 1: + self.stop_ids.append(eot_id[0]) + except: + pass if hasattr(self.model, 'generation_config') and self.model.generation_config is not None: eos_token_id = self.model.generation_config.eos_token_id from collections.abc import Iterable @@ -2608,7 +105,7 @@ def load_model(self, model_path): self.visual = None model_mapper = ModelMapper() - self.tie_word_embeddings = (hasattr(self.config, 'tie_word_embeddings') and self.config.tie_word_embeddings) + self.tie_word_embeddings = self.args.tie_embed and (hasattr(self.config, 'tie_word_embeddings') and self.config.tie_word_embeddings) self.model_type, self.model_map = model_mapper.get_map(self.config) if self.args.awq: @@ -2622,7 +119,6 @@ def visit_module(module): visit_module(child) visit_module(self.model) # print(self.config, self.model_type, self.model_map, self.model) - # exit(0) # load config info ModelMapper.do_map(self, self.config, self.model_map['config']) if not hasattr(self, 'num_key_value_heads') or self.num_key_value_heads is None: @@ -2666,6 +162,12 @@ def visit_module(module): out_features, in_features = self.embed_.weight.shape self.lm_ = torch.nn.Linear(in_features, out_features) self.lm_.weight = self.embed_.weight + elif not isinstance(self.lm_, torch.nn.Linear): + # for Baichuan2 + weight = self.lm_.weight + out_features, in_features = weight.shape + self.lm_ = torch.nn.Linear(in_features, out_features) + self.lm_.weight = weight if self.embed_.weight is self.lm_.weight: import copy @@ -2684,8 +186,10 @@ def visit_module(module): if self.visual is not None: if self.args.export is not None: self.visual.float() - self.visual = Visual.get_visual(self.model_type)(self.visual, self) + from utils.vision import Vision + self.visual = Vision.get_vision(self.model_type)(self.visual, self) if hasattr(self, 'audio') and self.audio is not None: + from utils.audio import Audio self.audio = Audio.get_audio(self.audio.config.model_type)(self.audio, self) else: self.audio = None @@ -3058,7 +562,6 @@ def export(self, export_type): MNNConveter(onnx_model, self.unloaded_ops, self).export() # delete onnx file if os.path.exists(onnx_model): - import glob try: for file in glob.glob(f'{self.onnx_path}/*'): os.remove(file) @@ -3118,11 +621,14 @@ def write_header(fp, type, speicals, prefix = []): if hasattr(self.tokenizer, 'get_prefix_tokens'): prefix_list = self.tokenizer.get_prefix_tokens() if len(prefix_list) == 0: - test_txt = 'A' - ids = self.tokenizer.encode(test_txt) - get_txt = self.tokenizer.decode(ids[-1]) - if len(ids) > 1 and get_txt == test_txt: - prefix_list += ids[:-1] + try: + test_txt = 'A' + ids = self.tokenizer.encode(test_txt) + get_txt = self.tokenizer.decode(ids[-1]) + if len(ids) > 1 and get_txt == test_txt: + prefix_list += ids[:-1] + except: + pass if self.sp_model is not None: # senetencepiece @@ -3371,24 +877,40 @@ def get_attention_mask(self) -> torch.Tensor: def export(path, type = None, + tokenizer_path = None, lora_path = None, + gptq_path = None, dst_path = './model', export = 'onnx', onnx_slim = False, quant_bit = 4, quant_block = 128, - lm_quant_bit = None): + lm_quant_bit = None, + mnnconvert = None, + ppl = False, + awq = False, + sym = False, + tie_embed = False, + lora_split = False): args = argparse.Namespace() for k, v in { 'path': path, 'type': type, + 'tokenizer_path': tokenizer_path, 'lora_path': lora_path, + 'gptq_path': gptq_path, 'dst_path': dst_path, 'export': export, 'onnx_slim': onnx_slim, 'quant_bit': quant_bit, 'quant_block': quant_block, - 'lm_quant_bit': lm_quant_bit + 'lm_quant_bit': lm_quant_bit, + 'mnnconvert': mnnconvert, + 'ppl': ppl, + 'awq': awq, + 'sym': sym, + 'tie_embed': tie_embed, + 'lora_split': lora_split }.items(): setattr(args, k, v) if 'bge' in path: @@ -3410,6 +932,7 @@ def main(): ) parser.add_argument('--tokenizer_path', type=str, default=None, help='tokenizer path, defaut is `None` mean using `--path` value.') parser.add_argument('--lora_path', type=str, default=None, help='lora path, defaut is `None` mean not apply lora.') + parser.add_argument('--gptq_path', type=str, default=None, help='gptq path, defaut is `None` mean not apply gptq.') parser.add_argument('--dst_path', type=str, default='./model', help='export onnx/mnn model to path, defaut is `./model`.') parser.add_argument('--verbose', action='store_true', help='Whether or not to print verbose.') parser.add_argument('--test', type=str, help='test model inference with query `TEST`.') @@ -3422,7 +945,8 @@ def main(): parser.add_argument('--ppl', action='store_true', help='Whether or not to get all logits of input tokens.') parser.add_argument('--awq', action='store_true', help='Whether or not to use awq quant.') parser.add_argument('--sym', action='store_true', help='Whether or not to using symmetric quant (without zeropoint), defualt is False.') - + parser.add_argument('--tie_embed', action='store_true', help='Whether or not to using tie_embedding, defualt is False.') + parser.add_argument('--lora_split', action='store_true', help='Whether or not export lora split, defualt is False.') args = parser.parse_args() model_path = args.path diff --git a/transformers/llm/export/utils/__init__.py b/transformers/llm/export/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/transformers/llm/export/utils/audio.py b/transformers/llm/export/utils/audio.py new file mode 100644 index 000000000..72d6efb33 --- /dev/null +++ b/transformers/llm/export/utils/audio.py @@ -0,0 +1,140 @@ +import torch + +class Audio(torch.nn.Module): + def __init__(self, audio, base): + super().__init__() + self.audio = audio + self.embed_ = base.embed + self.tokenizer = base.tokenizer + self.config = base.config + self.hidden_size = base.hidden_size + self.llm_config = base.llm_config + self.quant_bit = 16 + self.init_config() + self.load() + + @staticmethod + def get_audio(model_type): + audio_models = { + 'qwen2_audio': Qwen2Audio, + } + if model_type in audio_models: + return audio_models[model_type] + return None + + def init_config(self): + self.llm_config['is_audio'] = True + + def load(self): + raise NotImplementedError + + def str_to_ids(self, prompt): + input_ids = self.tokenizer(prompt, return_tensors="pt")['input_ids'] + return input_ids + + def forward(self, images): + raise NotImplementedError + + def embed(self, input_ids, images = None, videos = None): + raise NotImplementedError + +class Qwen2Audio(Audio): + def __init__(self, audio, base): + super().__init__(audio, base) + self.audio_embeds = None + self.audio_pad_id = 151646 + self.n_fft = 400 + self.sampling_rate = 16000 + self.hop_length = 160 + self.chunk_length = 30 + self.feature_size = 128 + self.n_samples = self.chunk_length * self.sampling_rate + self.max_length = self.n_samples // self.hop_length + from transformers.audio_utils import mel_filter_bank + self.mel_filters = mel_filter_bank( + num_frequency_bins=1 + self.n_fft // 2, + num_mel_filters=self.feature_size, + min_frequency=0.0, + max_frequency=8000.0, + sampling_rate=self.sampling_rate, + norm="slaney", + mel_scale="slaney", + ) + + def load(self): + # model + self.audio_tower = self.audio.audio_tower + self.multi_modal_projector = self.audio.multi_modal_projector + # config + self.llm_config['is_audio'] = True + + def str_to_ids(self, prompt): + if '' in prompt: + import re + from io import BytesIO + from urllib.request import urlopen + import librosa + pattern = r'()' + parts = re.split(pattern, prompt) + txt_prompt = '' + for part in parts: + if re.match(pattern, part): + audio_content = re.search(r'', part).group(1) + if audio_content.startswith('http://') or audio_content.startswith('https://'): + audio_obj = librosa.load(BytesIO(urlopen(audio_content).read()), sr=self.sampling_rate)[0] + audio_embed_len = self.audio_process(audio_obj) + audio_pad_str = '<|AUDIO|>' * audio_embed_len + txt_prompt += audio_pad_str + else: + txt_prompt += part + else: + txt_prompt = prompt + input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] + return input_ids + + def forward(self, input_features): + input_features = input_features.to(dtype=self.audio_tower.conv1.weight.dtype, device=self.audio_tower.conv1.weight.device) + inputs_embeds = torch.nn.functional.gelu(self.audio_tower.conv1(input_features)) + inputs_embeds = torch.nn.functional.gelu(self.audio_tower.conv2(inputs_embeds)) + inputs_embeds = inputs_embeds.permute(0, 2, 1) + _, seq_len, _ = inputs_embeds.shape + embed_pos = self.audio_tower.embed_positions.weight[:seq_len, :] + hidden_states = inputs_embeds + embed_pos + for encoder_layer in self.audio_tower.layers: + hidden_states = encoder_layer(hidden_states, None, None)[0] + hidden_states = hidden_states.permute(0, 2, 1) + hidden_states = self.audio_tower.avg_pooler(hidden_states) + hidden_states = hidden_states.permute(0, 2, 1) + hidden_states = self.audio_tower.layer_norm(hidden_states) + audio_features = self.multi_modal_projector(hidden_states) + return audio_features + + def _torch_extract_fbank_features(self, waveform): + window = torch.hann_window(self.n_fft) + stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32) + mel_spec = mel_filters.T @ magnitudes + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + if waveform.dim() == 2: + max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0] + log_spec = torch.maximum(log_spec, max_val - 8.0) + else: + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec + + def audio_process(self, audio_obj): + # audio_obj = np.pad(audio_obj, (0, self.n_samples - audio_obj.shape[0])) + waveform = torch.from_numpy(audio_obj).type(torch.float32) + input_features = self._torch_extract_fbank_features(waveform).unsqueeze(0) + audio_embeds = self.forward(input_features) + self.audio_embeds = audio_embeds.permute([1, 0, 2]) + return self.audio_embeds.shape[0] + + def embed(self, input_ids, images = None, videos = None): + input_embeds = self.embed_(input_ids) + if self.audio_embeds is not None: + audio_mask = (input_ids == self.audio_pad_id).squeeze() + input_embeds[audio_mask] = self.audio_embeds.type(input_embeds.dtype) + return input_embeds \ No newline at end of file diff --git a/transformers/llm/export/utils/awq_quantizer.py b/transformers/llm/export/utils/awq_quantizer.py new file mode 100644 index 000000000..1f69ecbb8 --- /dev/null +++ b/transformers/llm/export/utils/awq_quantizer.py @@ -0,0 +1,815 @@ +import gc +import torch +import logging +import inspect +import functools + +from tqdm import tqdm +from collections import defaultdict +from typing import Tuple, List, Union, Dict + +logging.basicConfig(level=logging.ERROR) + +class AwqQuantizer: + def __init__( + self, + model, + modules_to_not_convert=None, + apply_clip=True, + n_parallel_calib_samples=None, + max_calib_samples=128, + max_calib_seq_len=512, + max_chunk_memory=1024 * 1024 * 1024, + ) -> None: + self.awq_model = model + self.model = model + self.tokenizer = model.tokenizer + self.w_bit = model.args.quant_bit + self.group_size = model.args.quant_block + self.zeropoint = not model.args.sym + self.calib_data = 'ag_news' + self.split = 'test' + self.duo_scaling = True + self.apply_clip = apply_clip + self.n_parallel_calib_samples = n_parallel_calib_samples + self.max_calib_samples = max_calib_samples + self.max_calib_seq_len = max_calib_seq_len + self.max_chunk_memory = max_chunk_memory + self.modules_to_not_convert = ( + modules_to_not_convert if modules_to_not_convert is not None else [] + ) + self.modules, self.module_kwargs, self.inps = self.init_quant( + n_samples=self.max_calib_samples, max_seq_len=self.max_calib_seq_len + ) + + def pseudo_quantize_tensor(self, w: torch.Tensor): + org_w_shape = w.shape + if self.group_size > 0: + assert org_w_shape[-1] % self.group_size == 0 + w = w.reshape(-1, self.group_size) + assert w.dim() == 2 + assert torch.isnan(w).sum() == 0 + # zero point quantization + if self.zeropoint: + max_val = w.amax(dim=1, keepdim=True) + min_val = w.amin(dim=1, keepdim=True) + offset = 1 << (self.w_bit - 1) + clip_max = offset - 1 + clip_min = -offset + scales = (max_val - min_val) / (clip_max - clip_min) + zeros = - torch.round(min_val / scales) + clip_min + qw = torch.round(w / scales) + zeros + qw = torch.clamp(qw, clip_min, clip_max) + w = (qw - zeros) * scales + zeros = min_val.view(org_w_shape[0], -1) + else: + abs_max = w.abs().amax(dim=1, keepdim=True) + offset = 1 << (self.w_bit - 1) + clip_max = offset - 1 + clip_min = -clip_max + scales = abs_max / clip_max + w = torch.clamp(torch.round(w / scales), clip_min, clip_max) * scales + zeros = None + + assert torch.isnan(scales).sum() == 0 + assert torch.isnan(w).sum() == 0 + + scales = scales.view(org_w_shape[0], -1) + w = w.reshape(org_w_shape) + + return w, scales, zeros + + def quantize(self): + for i in tqdm(range(len(self.modules)), desc="AWQ"): + # Move module and inputs to correct device + common_device = next(self.modules[i].parameters()).device + if common_device is None or str(common_device) == "cpu": + best_device = AwqQuantizer.get_best_device() + + self.modules[i] = self.modules[i].to(best_device) + common_device = next(self.modules[i].parameters()).device + + if self.module_kwargs.get("position_ids") is not None: + self.module_kwargs["position_ids"] = self.module_kwargs[ + "position_ids" + ].to(common_device) + + if self.module_kwargs.get("attention_mask") is not None: + self.module_kwargs["attention_mask"] = self.module_kwargs[ + "attention_mask" + ].to(common_device) + + self.inps = self.inps.to(common_device) + # print(f'# {i} inps shape: {self.inps.shape}, inps.max: {self.inps.max()}') + + # [STEP 1]: Get layer, extract linear modules, extract input features + named_linears = AwqQuantizer.get_named_linears(self.modules[i]) + + # Filter out the linear layers we don't want to exclude + named_linears = AwqQuantizer.exclude_layers_to_not_quantize( + named_linears, self.modules_to_not_convert + ) + input_feat = self._get_input_feat(self.modules[i], named_linears) + AwqQuantizer.clear_memory() + + # [STEP 2]: Compute and apply scale list + module_config = [] + # q, k, v proj + module_config.append( + dict( + prev_op=self.modules[i].input_layernorm, + layers=[ + self.modules[i].self_attn.q_proj, + self.modules[i].self_attn.k_proj, + self.modules[i].self_attn.v_proj, + ], + inp=input_feat["self_attn.q_proj"], + module2inspect=self.modules[i].self_attn, + kwargs=self.module_kwargs, + ) + ) + # o_proj + if self.modules[i].self_attn.v_proj.weight.shape == self.modules[i].self_attn.o_proj.weight.shape: + module_config.append( + dict( + prev_op=self.modules[i].self_attn.v_proj, + layers=[self.modules[i].self_attn.o_proj], + inp=input_feat["self_attn.o_proj"], + ) + ) + # mlp gate + module_config.append( + dict( + prev_op=self.modules[i].post_attention_layernorm, + layers=[self.modules[i].mlp.gate_proj, self.modules[i].mlp.up_proj], + inp=input_feat["mlp.gate_proj"], + module2inspect=self.modules[i].mlp, + ) + ) + # mlp down + module_config.append( + dict( + prev_op=self.modules[i].mlp.up_proj, + layers=[self.modules[i].mlp.down_proj], + inp=input_feat["mlp.down_proj"], + ) + ) + scales_list = [ + self._search_best_scale(self.modules[i], **layer) + for layer in module_config + ] + # print(scales_list); exit(0) + AwqQuantizer.apply_scale(self.modules[i], scales_list, input_feat_dict=input_feat) + # [STEP 3]: Compute and apply clipping list + if self.apply_clip: + clip_list = self._search_best_clip( + self.modules[i], named_linears, input_feat + ) + AwqQuantizer.apply_clip(self.modules[i], clip_list) + + AwqQuantizer.clear_memory() + + @torch.no_grad() + def _module_forward( + self, x: torch.Tensor, module: torch.nn.Module, module_kwargs: Dict + ) -> torch.Tensor: + if self.n_parallel_calib_samples is None: + # runs through all samples at once + # print(module, x, module_kwargs); exit(0) + module_output = module(x, **module_kwargs) + if isinstance(module_output, tuple): + module_output = module_output[0] + else: + # memory efficiently runs through all calibration samples + # but only n_parallel_calib_samples at a time + module_output = [] + partitioned_inputs = torch.split(x, self.n_parallel_calib_samples) + for x_partial in partitioned_inputs: + partial_output = module(x_partial, **module_kwargs) + + if isinstance(partial_output, tuple): + partial_output = partial_output[0] + + module_output.append(partial_output.cpu()) + + module_output = torch.cat(module_output, dim=0) + + return module_output + + @torch.no_grad() + def _search_best_scale( + self, + module, + prev_op, + layers: List[torch.nn.Linear], + inp: torch.Tensor, + module2inspect=None, + kwargs={}, + ): + if module2inspect is None: + assert len(layers) == 1 + module2inspect = layers[0] + + if "use_cache" in kwargs: + kwargs.pop("use_cache") + + # Put x on the right device + inp = inp.to(next(module2inspect.parameters()).device) + + # [STEP 1]: Compute per-channel mean of normalised weights + # All layer weights are concatted together + weight = torch.cat([_m.weight for _m in layers], dim=0) + org_shape = weight.shape + # The weights are reshaped to be organised by quantization group + weight = weight.view(-1, self.group_size) + # Calculates the relative magnitude of the weights within each of the quantization groups, + # and rescales each group individually so that each group has weights on a 0-1 scale. + w_scale = weight.abs() / (weight.abs().amax(dim=1, keepdim=True) + 1e-6) + # Resizes the rescaled weight matrix back up to its original dimensions + w_scale = w_scale.view(org_shape) + # Gets the average rescaled magnitude for each output channel + w_mean = w_scale.mean(0) + AwqQuantizer.clear_memory(weight) + + # [STEP 2]: Compute per-channel mean of the input activation with chunking + # move inp to cpu to avoid memory leak + inp_flat = inp.cpu().abs().view(-1, inp.shape[-1]) + num_elements = inp_flat.size(0) + num_channels = inp_flat.size(1) + element_size_bytes = inp_flat.element_size() * 2 # multiplied by 2 for FP32 + + # Calculate chunk size dynamically based on max_chunk_memory + chunk_size = int(self.max_chunk_memory // (element_size_bytes * num_channels)) + chunk_size = min(chunk_size, num_elements) + + # Use float32 for sum calculation + x_sum = torch.zeros(num_channels, dtype=torch.float32, device=inp.device) + + for i in range(0, num_elements, chunk_size): + end = min(i + chunk_size, num_elements) + chunk_sum = inp_flat[i:end].to(torch.float32).sum(dim=0) + x_sum += chunk_sum.to(inp.device) + + x_mean = (x_sum / num_elements).to(inp.dtype) + AwqQuantizer.clear_memory(x_sum) + + # [STEP 3]: Compute output of module + with torch.no_grad(): + module_kwargs = self._sanitize_kwargs(kwargs, module2inspect) + fp16_output = self._module_forward(inp, module2inspect, module_kwargs) + + # [STEP 4]: Compute loss + best_scales = self._compute_best_scale( + inp, w_mean, x_mean, module2inspect, layers, fp16_output, module_kwargs + ) + + return ( + AwqQuantizer.get_op_name(module, prev_op), + tuple([AwqQuantizer.get_op_name(module, m) for m in layers]), + best_scales, + ) + + def _compute_best_scale( + self, + x: torch.Tensor, + w_mean: torch.Tensor, + x_mean: torch.Tensor, + module2inspect: torch.nn.Module, + linears2scale: List[torch.nn.Linear], + fp16_output: torch.Tensor, + kwargs: Dict={}, + ): + """ + Compute loss and select best scales + + L(s) = || Q(W * s) (s^-1 * X) - W * X || + Q: weight quantization function | pseudo_quantize_tensor(W * s) + X: inputs from calib dataset | X + W: original weights in FP16 | layer + s: per channel scaling factor | s^-1 * X + """ + n_grid = 20 + history = [] + best_ratio = -1 + best_scales = None + best_error = float("inf") + + device = x.device + x_mean = x_mean.view(-1).to(device) + w_mean = w_mean.view(-1).to(device) + + ord_weights = [] + for fc in linears2scale: + ord_weights.append(fc.weight.data.clone()) + + for ratio in range(n_grid): + # create new scales + ratio = ratio / n_grid + + # NOTE: s^-1 * x is fused here, according to paper + if self.duo_scaling: + scales = (x_mean.pow(ratio) / (w_mean.pow(1 - ratio) + 1e-4)).clamp(min=1e-4) + else: + scales = x_mean.pow(ratio).clamp(min=1e-4).view(-1) + scales = scales / (scales.max() * scales.min()).sqrt() + scales_view = scales.view(1, -1).to(device) + + # avoid scaling values that overflow + scales[torch.isinf(scales)] = 1 + scales[torch.isnan(scales)] = 1 + + # Q(W * s) + for fc in linears2scale: + fc.weight.mul_(scales_view) + fc.weight.data = ( + self.pseudo_quantize_tensor(fc.weight.data)[0] / scales_view + ) + + # W * X + int_w_output = self._module_forward(x, module2inspect, kwargs) + + # compute mean squared error (L2 norm) + loss = self._compute_loss(fp16_output, int_w_output, device) + + history.append(loss) + if loss < best_error: + best_error = loss + best_ratio = ratio + best_scales = scales.clone() + + for fc, ord_weight in zip(linears2scale, ord_weights): + fc.weight.data = ord_weight.clone() + + del ord_weights + + if best_ratio == -1: + logging.debug(history) + raise Exception + + assert torch.isnan(best_scales).sum() == 0, best_scales + + return best_scales.detach().cpu() + + @torch.no_grad() + def _compute_loss( + self, + fp16_output: torch.Tensor, + int_w_output: torch.Tensor, + device: torch.device, + ): + loss = 0.0 + fp16_output_flat = fp16_output.view(-1) + int_w_output_flat = int_w_output.view(-1) + num_elements = fp16_output_flat.size(0) + element_size_bytes = fp16_output.element_size() + + # Calculate chunk size dynamically based on max_chunk_memory + # Divide the max_chunk_memory by twice the element size + chunk_size = self.max_chunk_memory // (element_size_bytes * 2) + chunk_size = min(chunk_size, num_elements) + + # Split the computation into chunks + fp16_chunks = torch.split(fp16_output_flat, chunk_size) + int_w_chunks = torch.split(int_w_output_flat, chunk_size) + + # Compute the loss for each chunk + for fp16_chunk, int_w_chunk in zip(fp16_chunks, int_w_chunks): + chunk_loss = (fp16_chunk.to(device) - int_w_chunk.to(device)).float().pow(2).sum().item() + loss += chunk_loss + + # Normalize the loss by the total number of elements + loss /= num_elements + + return loss + + @torch.no_grad() + def _search_best_clip(self, layer, named_linears, input_feat): + clip_list = [] + avoid_clipping = ["q_", "k_", "query", "key", "Wqkv"] + + for name in named_linears: + # due to qk bmm, it is hard to clip precisely + if any([_ in name for _ in avoid_clipping]): + continue + + named_linears[name].to(AwqQuantizer.get_best_device()) + max_val = self._compute_best_clip( + named_linears[name].weight, input_feat[name] + ) + clip_list.append((name, max_val)) + named_linears[name].cpu() + + return clip_list + + @torch.no_grad() + def _compute_best_clip( + self, + w: torch.Tensor, + input_feat: torch.Tensor, + n_grid=20, + max_shrink=0.5, + n_sample_token=512, + ): + assert w.dim() == 2 + org_w_shape = w.shape + # w [co, ci] -> [co, 1, n_group, group size] + # input_feat [n_token, ci] -> [1, n_token, n_group, group size] + group_size = self.group_size if self.group_size > 0 else org_w_shape[1] + input_feat = input_feat.view(-1, input_feat.shape[-1]) + input_feat = input_feat.reshape(1, input_feat.shape[0], -1, group_size) + + # Compute input feature step size (minimum 1) + step_size = max(1, input_feat.shape[1] // n_sample_token) + input_feat = input_feat[:, ::step_size] + + w = w.reshape(org_w_shape[0], 1, -1, group_size) + + oc_batch_size = 256 if org_w_shape[0] % 256 == 0 else 64 # prevent OOM + assert org_w_shape[0] % oc_batch_size == 0 + w_all = w + best_max_val_all = [] + + for i_b in range(org_w_shape[0] // oc_batch_size): + w = w_all[i_b * oc_batch_size : (i_b + 1) * oc_batch_size] + + org_max_val = w.abs().amax(dim=-1, keepdim=True) # co, 1, n_group, 1 + + best_max_val = org_max_val.clone() + min_errs = torch.ones_like(org_max_val) * 1e9 + input_feat = input_feat.to(w.device) + org_out = (input_feat * w).sum(dim=-1) # co, n_token, n_group + + for i_s in range(int(max_shrink * n_grid)): + max_val = org_max_val * (1 - i_s / n_grid) + min_val = -max_val + cur_w = torch.clamp(w, min_val, max_val) + q_w = self.pseudo_quantize_tensor(cur_w)[0] + cur_out = (input_feat * q_w).sum(dim=-1) + + # co, 1, n_group, 1 + err = (cur_out - org_out).pow(2).mean(dim=1).view(min_errs.shape) + del cur_w + del cur_out + cur_best_idx = err < min_errs + min_errs[cur_best_idx] = err[cur_best_idx] + best_max_val[cur_best_idx] = max_val[cur_best_idx] + best_max_val_all.append(best_max_val) + + best_max_val = torch.cat(best_max_val_all, dim=0) + + AwqQuantizer.clear_memory(input_feat) + AwqQuantizer.clear_memory(org_out) + + return best_max_val.squeeze(1) + + @staticmethod + @torch.no_grad() + def apply_clip(module, clip_list: Tuple[str, torch.Tensor]): + for name, max_val in clip_list: + layer: torch.nn.Linear = AwqQuantizer.get_op_by_name(module, name) + layer.to(AwqQuantizer.get_best_device()) + max_val = max_val.to(layer.weight.device) + org_shape = layer.weight.shape + layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1) + layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val) + layer.weight.data = layer.weight.data.reshape(org_shape) + layer.cpu() + + @staticmethod + @torch.no_grad() + def scale_fc_fcs(fc1: torch.nn.Linear, fcs: List[torch.nn.Linear], scales: torch.Tensor): + if not isinstance(fcs, list): + fcs = [fcs] + + scales = scales.to(fc1.weight.device) + + fc1.weight[-scales.size(0) :].div_(scales.view(-1, 1)) + if fc1.bias is not None: + fc1.bias.div_(scales.view(-1)) + + for fc in fcs: + fc.weight.mul_(scales.view(1, -1)) + + for p in fc1.parameters(): + assert torch.isnan(p).sum() == 0 + for fc in fcs: + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + @staticmethod + def is_allowed_act_fns(op): + from transformers.activations import NewGELUActivation, PytorchGELUTanh, GELUActivation + allowed_act_fns = [ + torch.nn.GELU, + NewGELUActivation, + PytorchGELUTanh, + GELUActivation, + ] + return (op in allowed_act_fns) + + @staticmethod + def is_allowed_norms(op): + if isinstance(op, torch.nn.LayerNorm): + return True + if any(t in str(type(op)) for t in ['LlamaRMSNorm', 'GemmaRMSNorm', 'CohereLayerNorm']): + return True + return False + + @staticmethod + @torch.no_grad() + def scale_fc_fc(fc1: torch.nn.Linear, fc2: torch.nn.Linear, scales: torch.Tensor): + assert isinstance(fc1, torch.nn.Linear) + assert isinstance(fc2, torch.nn.Linear) + + scales = scales.to(fc1.weight.device) + fc1.weight[-scales.size(0) :].div_(scales.view(-1, 1)) + if fc1.bias is not None: + fc1.bias.div_(scales.view(-1)) + + fc2.weight.mul_(scales.view(1, -1)) + + for p in fc1.parameters(): + assert torch.isnan(p).sum() == 0 + for p in fc2.parameters(): + assert torch.isnan(p).sum() == 0 + + @staticmethod + @torch.no_grad() + def scale_ln_fcs(ln: torch.nn.Linear, fcs: List[torch.nn.Linear], scales: torch.Tensor): + if not isinstance(fcs, list): + fcs = [fcs] + + scales = scales.to(ln.weight.device) + + # GemmaRMSNorm is different from Llama's in that it multiplies + # (1 + weight) to the output, instead of just weight. + if 'GemmaRMSNorm' in str(type(ln)): + ln.weight += 1 + ln.weight.div_(scales) + ln.weight -= 1 + else: + ln.weight.div_(scales) + + if hasattr(ln, "bias") and ln.bias is not None: + ln.bias.div_(scales) + + for fc in fcs: + fc.weight.mul_(scales.view(1, -1)) + + for p in ln.parameters(): + assert torch.isnan(p).sum() == 0 + for fc in fcs: + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + @staticmethod + @torch.no_grad() + def scale_gelu_fc(gelu, fc: torch.nn.Linear, scales: torch.Tensor): + assert AwqQuantizer.is_allowed_act_fns(gelu) + assert isinstance(fc, torch.nn.Linear) + + fc.weight.mul_(scales.view(1, -1).to(fc.weight.device)) + + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + @staticmethod + def apply_scale(module, scales_list, input_feat_dict=None): + for prev_op_name, layer_names, scales in scales_list: + prev_op = AwqQuantizer.get_op_by_name(module, prev_op_name) + layers = [AwqQuantizer.get_op_by_name(module, name) for name in layer_names] + + best_device = AwqQuantizer.get_best_device() + prev_op.to(best_device) + for layer in layers: + layer.to(best_device) + scales.to(best_device) + if ( + isinstance(prev_op, torch.nn.Linear) + and type(layers) == list + and isinstance(layers[0], torch.nn.Linear) + ): + if len(layers) == 1: + AwqQuantizer.scale_fc_fc(prev_op, layers[0], scales) + else: + AwqQuantizer.scale_fc_fcs(prev_op, layers, scales) + elif ( + AwqQuantizer.is_allowed_norms(prev_op) + or "rmsnorm" in str(prev_op.__class__).lower() + ): + AwqQuantizer.scale_ln_fcs(prev_op, layers, scales) + + elif AwqQuantizer.is_allowed_act_fns(prev_op): + #new_module = ScaledActivation(prev_op, scales) + #set_op_by_name(module, prev_op_name, new_module) + AwqQuantizer.scale_gelu_fc(prev_op, layers[0], scales) + else: + raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!") + + # apply the scaling to input feat if given; prepare it for clipping + if input_feat_dict is not None: + for layer_name in layer_names: + # Skip the modules that are not quantized + if layer_name in input_feat_dict: + inp = input_feat_dict[layer_name] + inp.div_(scales.view(1, -1).to(inp.device)) + + prev_op.cpu() + for layer in layers: + layer.cpu() + scales.cpu() + + @staticmethod + def exclude_layers_to_not_quantize(linear_layers, modules_to_not_convert): + if modules_to_not_convert is None: + return linear_layers + + filtered_layers = {} + for name, linear_layer in linear_layers.items(): + if not any(key in name for key in modules_to_not_convert): + filtered_layers[name] = linear_layer + return filtered_layers + + @staticmethod + def get_named_linears(module): + return {name: m for name, m in module.named_modules() if isinstance(m, torch.nn.Linear)} + + @staticmethod + def get_op_by_name(module, op_name): + # get the op by its name relative to the module + for name, m in module.named_modules(): + if name == op_name: + return m + raise ValueError(f"Cannot find op {op_name} in module {module}") + + @staticmethod + def get_calib_dataset( + data: Union[str, List[str], List[List[int]]] = "pileval", + tokenizer=None, + n_samples=128, + max_seq_len=512, + split="train", + text_column="text", + ): + if isinstance(data, str): + from datasets import load_dataset + if data == "pileval": + dataset = load_dataset("mit-han-lab/pile-val-backup", split="validation") + else: + dataset = load_dataset(data, split=split) + # dataset = dataset.shuffle(seed=42) + elif isinstance(data, list): + if isinstance(data[0], str): + dataset = [{text_column: text} for text in data] + elif isinstance(data[0][0], int): + dataset = data + else: + raise NotImplementedError( + "Either pass a string to a huggingface dataset or a list" + "that is preprocessed with one sample of text per element" + " or a list of list of int for tokenized words." + ) + else: + raise NotImplementedError( + "Either pass a string to a huggingface dataset or a list" + "that is preprocessed with one sample of text per element" + " or a list of list of int for tokenized words." + ) + + samples = [] + n_run = 0 + for data in dataset: + if isinstance(data, list): + line_encoded = data + else: + line = data[text_column] + line = line.strip() + line_encoded = tokenizer.encode(line) + if len(line_encoded) > max_seq_len: + continue + sample = torch.tensor([line_encoded]) + if sample.numel() == 0: + continue + samples.append(sample) + n_run += 1 + if n_run == n_samples: + break + # now concatenate all samples and split according to max sequence length + cat_samples = torch.cat(samples, dim=1) + n_split = cat_samples.shape[1] // max_seq_len + logging.debug(f" * Split into {n_split} blocks") + return [ + cat_samples[:, i * max_seq_len : (i + 1) * max_seq_len] for i in range(n_split) + ] + + @staticmethod + def get_best_device(): + if torch.backends.mps.is_available(): + return "mps" + elif torch.cuda.is_available(): + return "cuda:0" + else: + return "cpu" + + @staticmethod + def clear_memory(weight=None): + if weight is not None: + del weight + gc.collect() + torch.cuda.empty_cache() + + @staticmethod + def get_op_name(module, op): + # get the name of the op relative to the module + for name, m in module.named_modules(): + if m is op: + return name + raise ValueError(f"Cannot find op {op} in module {module}") + + @staticmethod + def append_str_prefix(x, prefix): + if isinstance(x, str): + return prefix + x + elif isinstance(x, tuple): + return tuple([AwqQuantizer.append_str_prefix(y, prefix) for y in x]) + elif isinstance(x, list): + return [AwqQuantizer.append_str_prefix(y, prefix) for y in x] + else: + return x + + def init_quant(self, n_samples=128, max_seq_len=512): + modules = self.awq_model.blocks + samples = AwqQuantizer.get_calib_dataset( + data=self.calib_data, + tokenizer=self.tokenizer, + n_samples=n_samples, + max_seq_len=max_seq_len, + split=self.split + ) + # samples = torch.cat(samples, dim=0) + samples = torch.cat(samples[:1], dim=0) # just using 1 batch + inps = [] + layer_kwargs = {} + # build inps + self.model.seq_len = samples.numel() + self.model.context_len = samples.numel() - 2 + self.model.token_len = 0 + best_device = AwqQuantizer.get_best_device() + inps = self.model.embedding(samples).to(best_device) + position_ids = self.model.get_position_ids() + rotary_pos_emb = self.model.rotary(position_ids) + attention_mask = self.model.get_attention_mask() + layer_kwargs["rotary_pos_emb"] = rotary_pos_emb.to(best_device) + layer_kwargs["attention_mask"] = attention_mask.to(best_device) + del samples + AwqQuantizer.clear_memory() + return modules, layer_kwargs, inps + + def _get_input_feat(self, layer, named_linears): + # firstly, get input features of all linear layers + def cache_input_hook(m, x, y, name, feat_dict): + x = x[0] + x = x.detach().cpu() + feat_dict[name].append(x) + input_feat = defaultdict(list) + handles = [] + for name in named_linears: + handles.append( + named_linears[name].register_forward_hook( + functools.partial(cache_input_hook, name=name, feat_dict=input_feat) + ) + ) + self.inps = self.inps.to(next(layer.parameters()).device) # in case multi-gpu + # get output as next layer's input + + # Sanitize the kwargs in case we use transformers version that contains + # kwargs that are not handled by the module. + # Useful for trust_remote_code models. + module_kwargs = self._sanitize_kwargs(self.module_kwargs, layer) + + self.inps = self._module_forward(self.inps, layer, module_kwargs) + for h in handles: + h.remove() + # now solve for scaling and clipping + input_feat = {k: torch.cat(v, dim=0) for k, v in input_feat.items()} + + return input_feat + + def _sanitize_kwargs(self, inputs_kwargs, module): + """ + Remove the arguments that are not supported in the module's + forward pass to avoid breaking behaviour between different versions + of transformers. + + Args: + inputs_kwargs (`dict`): + The input dictionary to pass to the model layer + module (`torch.nn.Module`): + Target module to quantize. + """ + module_signature = inspect.signature(module.forward).parameters + sanitized_kwargs = {} + for k, v in inputs_kwargs.items(): + if k in module_signature: + sanitized_kwargs[k] = v + return sanitized_kwargs \ No newline at end of file diff --git a/transformers/llm/export/utils/custom_op.py b/transformers/llm/export/utils/custom_op.py new file mode 100644 index 000000000..d1a270459 --- /dev/null +++ b/transformers/llm/export/utils/custom_op.py @@ -0,0 +1,59 @@ +import torch + +class FakeLinearOp(torch.autograd.Function): + @staticmethod + def symbolic(g, input, in_features, out_features, has_bias, name): + # These become the operator attributes. + kwargs = { + "in_features_i": in_features, + "out_features_i": out_features, + "has_bias_i": has_bias, + "name_s": name + } + from torch.onnx.symbolic_helper import _get_tensor_sizes + out_sizes = _get_tensor_sizes(input)[:-1] + [out_features] + output_type = input.type().with_sizes(out_sizes) + return g.op("LlmExporter::FakeLinear", input, **kwargs).setType(output_type) + + @staticmethod + def forward(ctx, input, in_features, out_features, has_bias, name): + out_shape = list(input.shape)[:-1] + [out_features] + return input.new_zeros(out_shape) + +class FakeLinear(torch.nn.Module): + def __init__(self, in_features, out_features, has_bias, name): + super(FakeLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.has_bias = has_bias + self.name = name + + def forward(self, x): + return FakeLinearOp.apply(x, self.in_features, self.out_features, self.has_bias, self.name) + +class FusedAttentionOp(torch.autograd.Function): + @staticmethod + def symbolic(g, query, key, value, attention_mask, hidden_size, name): + # These become the operator attributes. + kwargs = { + "hidden_size_i": hidden_size, + "name_s": name + } + from torch.onnx.symbolic_helper import _get_tensor_sizes + out_sizes = _get_tensor_sizes(query) + output_type = query.type().with_sizes(out_sizes) + return g.op("LlmExporter::FusedAttention", query, key, value, attention_mask, **kwargs).setType(output_type) + + @staticmethod + def forward(ctx, query, key, value, attention_mask, hidden_size, name): + out_shape = list(query.shape)[:2] + [hidden_size] + return query.new_zeros(out_shape) + +class FusedAttention(torch.nn.Module): + def __init__(self, hidden_size, name): + super(FusedAttention, self).__init__() + self.hidden_size = hidden_size + self.name = name + + def forward(self, query, key, value, attention_mask): + return FusedAttentionOp.apply(query, key, value, attention_mask, self.hidden_size, self.name) \ No newline at end of file diff --git a/transformers/llm/export/utils/gptq.py b/transformers/llm/export/utils/gptq.py new file mode 100644 index 000000000..5c7e4880a --- /dev/null +++ b/transformers/llm/export/utils/gptq.py @@ -0,0 +1,147 @@ +import glob +import json +import torch +from safetensors import safe_open + +class GPTQWeight: + def __init__(self, name): + self.name = name + + def __repr__(self) -> str: + if hasattr(self, 'qweight'): + return f'{self.name}, {self.qweight.shape}, {self.scales.shape}' + return 'None' + + def add(self, name, tensor): + setattr(self, name, tensor) + + def weight(self, idx): + shape = self.qweight.shape + if len(shape) == 2: + ic, oc = shape + self.qweight = self.qweight.reshape(ic//16, 16, oc) + return self.qweight[idx] + + def scale(self, idx): + return self.scales[idx] + +class MNNWeight: + def __init__(self, name, external, weight_elements): + self.name = name + self.external = external + self.quant_bits = 4 + if round(weight_elements / external[1]) == 2: + self.quant_bits = 4 + self.a_min = -8 + else: + self.quant_bits = 8 + self.a_min = -128 + self.parse_name() + + def __repr__(self) -> str: + return f'{self.layer_id}.{self.op_id}.{self.block_id}, {self.external}' + + def parse_name(self): + parts = self.name.split('/') + if len(parts) > 4: + self.layer_id = parts[1].split('.')[1] + self.op_id = parts[2] + '.' + parts[3] + self.block_id = parts[-1].split('__')[-1] + else: + self.layer_id = -1 + self.op_id = parts[2] + self.block_id = parts[-1].split('__')[-1] + + def key(self): + if self.layer_id == -1: return self.op_id + return f'{self.layer_id}.{self.op_id}' + def offset(self): return self.external[0] + def weight_size(self): return self.external[1] + def scale_size(self): return self.external[2] + +class GPTQ: + def __init__(self, gptq_path): + self.load(gptq_path) + + def load(self, path): + for tensor in glob.glob(f'{path}/*.safetensors'): + self.load_safetensor(tensor) + + def prefix(self, name): + splits = name.split('.') + if 'lm_head' in splits[0] and len(splits) == 2: + return splits[0], splits[1] + if len(splits) < 5: + return None, None + pre = f'{splits[2]}.{splits[3]}.{splits[4]}' + suf = splits[-1] + return pre, suf + + def get(self, key : str): + if key in self.weight_dict: + return self.weight_dict[key] + return None + + def load_safetensor(self, tensor): + self.weight_dict = dict() + with safe_open(tensor, framework="pt") as f: + for k in f.keys(): + p, s = self.prefix(k) + if p is None: continue + if s not in ['qweight', 'scales']: continue + if p not in self.weight_dict: + self.weight_dict[p] = GPTQWeight(p) + self.weight_dict[p].add(s, f.get_tensor(k)) + + @staticmethod + def weight_reorder(qweight, bits=4, group_size=128): + oc = qweight.shape[-1] + wf = torch.tensor(list(range(0, 32, bits)), dtype=torch.int32).unsqueeze(0) + weight = torch.bitwise_right_shift(torch.unsqueeze(qweight, 1).expand(-1, 32 // bits, -1), wf.unsqueeze(-1)).to(torch.int16 if bits == 8 else torch.int8) + torch.bitwise_and(weight, (2 ** bits) - 1, out=weight) + weight = weight.reshape(-1, oc).transpose(1, 0) + if bits == 8: + weight = weight.to(torch.uint8) + return weight + weight = weight.reshape(-1, 2).to(torch.uint8) + weight = weight[:, 0] * 16 + weight[:, 1] + return weight + + def apply(self, graph_path, weight_path): + # parse mnn graph + mnn_weights = [] + mnn_graph = json.load(open(graph_path, 'rt')) + for op in mnn_graph['oplists']: + if op['type'] == 'Convolution': + name = op['name'] + external = op['main']['external'] + weight_elements = op['main']['common']['outputCount'] * op['main']['common']['inputCount'] + mnn_weights.append(MNNWeight(name, external, weight_elements)) + # load mnn weight + external_weight = open(weight_path, 'r+b') + for mnn_weight in mnn_weights: + gptq_weight = self.get(mnn_weight.key()) + if gptq_weight is None: continue + # print(f'write {mnn_weight.key()} ... ', end='') + weight = gptq_weight.qweight + scale = gptq_weight.scales.float().transpose(1, 0) + # write weight data + weight = GPTQ.weight_reorder(weight, mnn_weight.quant_bits) + weight_bytes = weight.numpy().tobytes() + weight_size = mnn_weight.weight_size() + header_len = weight_size - len(weight_bytes) + assert(header_len > 0) + external_weight.seek(mnn_weight.offset() + header_len) + external_weight.write(weight_bytes) + scale_size = mnn_weight.scale_size() + is_asy = scale.numel() * scale.element_size() < scale_size + # write scale data + if is_asy: + # zeros = mnn_weight.a_min * scale + zeros = torch.zeros_like(scale) + scale = torch.stack([zeros, scale], axis=-1) + scale_bytes = scale.numpy().tobytes() + assert(scale_size == len(scale_bytes)) + external_weight.write(scale_bytes) + # print('Done!') + external_weight.close() \ No newline at end of file diff --git a/tools/script/apply_lora.py b/transformers/llm/export/utils/lora.py similarity index 62% rename from tools/script/apply_lora.py rename to transformers/llm/export/utils/lora.py index e0e03e79f..bdf3648f7 100644 --- a/tools/script/apply_lora.py +++ b/transformers/llm/export/utils/lora.py @@ -1,24 +1,54 @@ import os import json -import argparse +from safetensors import safe_open -class Base: - def __init__(self, path, fuse_lora): - self.fuse_lora = fuse_lora - self.load(path) +class LoRA: + def __init__(self, lora_path, scale = 4.0): + self.lora_A = {} + self.lora_B = {} + self.lora_keys = set() + self.scale = scale + self.load(lora_path) def __str__(self): return str(self.lora_keys) + def has_lora(self, op_name): + if op_name[0] != '/': + return False + for key in self.lora_keys: + if key in op_name: + return True + return False + + def get_lora(self, tag): + lora_a, lora_b = self.lora_A[tag], self.lora_B[tag] + return lora_a, lora_b + def load(self, path): - self.base_model = json.load(open(path, 'rt')) + if os.path.isdir(path): + base_dir = path + config = json.load(open(os.path.join(base_dir, 'adapter_config.json'), 'rt')) + lora_alpha = config['lora_alpha'] + r = config['r'] + self.scale = float(lora_alpha) / r + path = os.path.join(base_dir, 'adapter_model.safetensors') + with safe_open(path, framework="pt") as f: + for k in f.keys(): + names = k.split('.') + layer, key, name = names[4], names[6], names[7] + tag = layer + key + tensor = f.get_tensor(k).float() + self.lora_keys.add(key) + if 'lora_A' == name: + self.lora_A[tag] = tensor + else: + self.lora_B[tag] = tensor * self.scale - def build_conv(self, input_index, output_name, dims, weight, mul_scale = 1.0): + def build_conv(self, input_index, output_name, dims, weight): output_index = len(self.base_model['tensorName']) oc, ic = dims bias = [0.0 for i in range(oc)] - if mul_scale != 1.0: - weight = [w * mul_scale for w in weight] op = { 'type': 'Convolution', 'name': output_name, @@ -37,8 +67,7 @@ def build_conv(self, input_index, output_name, dims, weight, mul_scale = 1.0): }, 'defaultDimentionFormat': 'NHWC' } - self.base_model['oplists'].insert(self.idx, op) - self.idx += 1 + self.new_ops.append(op) self.base_model['tensorName'].append(output_name) return output_index @@ -51,11 +80,10 @@ def build_binary(self, op_type, input_indexes, output_name): "inputIndexes": input_indexes, "outputIndexes": [ output_index ], "main_type": "BinaryOp", - "main": { "opType": 0, "T": "DT_FLOAT", "activationType": 0 }, + "main": { "opType": op_type, "T": "DT_FLOAT", "activationType": 0 }, "defaultDimentionFormat": "NHWC" } - self.base_model['oplists'].insert(self.idx, op) - self.idx += 1 + self.new_ops.append(op) self.base_model['tensorName'].append(output_name) return output_index @@ -64,93 +92,28 @@ def replace_input(self, origin_idx, new_idx): if op['type'] == 'ConvertTensor' and origin_idx in op['inputIndexes']: op['inputIndexes'] = [new_idx] - def apply_lora(self, op, lora): + def apply_lora(self, op): names = op['name'].split('/') - mul_scale = lora.scale tag = names[1].split('.')[1] + names[3] - lora_a, lora_b = lora.get_lora(tag) + lora_a, lora_b = self.get_lora(tag) input_index = op['inputIndexes'][0] outpt_index = op['outputIndexes'][0] - if self.fuse_lora: - w = (lora_a @ lora_b) - weight = w.reshape(-1).tolist() - b_out = self.build_conv(input_index, f'{tag}_B', w.shape, weight, mul_scale) - n_out = self.build_binary(0, [outpt_index, b_out], f'{tag}_add') - self.replace_input(outpt_index, n_out) - return # lora_B @ lora_A @ x -> lora_B @ (lora_A @ x) a_out = self.build_conv(input_index, f'{tag}_A', list(lora_a.shape), lora_a.flatten().tolist()) - b_out = self.build_conv(a_out, f'{tag}_B', list(lora_b.shape), lora_b.flatten().tolist(), mul_scale) + b_out = self.build_conv(a_out, f'{tag}_B', list(lora_b.shape), lora_b.flatten().tolist()) n_out = self.build_binary(0, [outpt_index, b_out], f'{tag}_add') self.replace_input(outpt_index, n_out) - def apply(self, lora, out): - ops = [] + def apply(self, base_path, out): + self.base_model = json.load(open(base_path, 'rt')) + self.new_ops = [] for i in range(len(self.base_model['oplists'])): op = self.base_model['oplists'][i] + self.new_ops.append(op) if op['type'] == 'Convolution': - if lora.has_lora(op['name']): - self.idx = i + 1 - self.apply_lora(op, lora) + if self.has_lora(op['name']): + self.apply_lora(op) + self.base_model['oplists'] = self.new_ops with open(out, 'w', encoding='utf-8') as file: json.dump(self.base_model, file, ensure_ascii=False, indent=4) - -class LoRA: - def __init__(self, path, scale): - self.lora_A = {} - self.lora_B = {} - self.lora_keys = set() - self.scale = scale - self.load(path) - - def __str__(self): - return str(self.lora_keys) - - def has_lora(self, op_name): - if op_name[0] != '/': - return False - for key in self.lora_keys: - if key in op_name: - return True - return False - - def get_lora(self, tag): - lora_a, lora_b = self.lora_A[tag], self.lora_B[tag] - return lora_a, lora_b - - def load(self, path): - if os.path.isdir(path): - base_dir = path - config = json.load(open(os.path.join(base_dir, 'adapter_config.json'), 'rt')) - lora_alpha = config['lora_alpha'] - r = config['r'] - self.scale = float(lora_alpha) / r - print(self.scale) - path = os.path.join(base_dir, 'adapter_model.safetensors') - from safetensors import safe_open - with safe_open(path, framework="pt") as f: - for k in f.keys(): - names = k.split('.') - layer, key, name = names[4], names[6], names[7] - tag = layer + key - tensor = f.get_tensor(k).float() - self.lora_keys.add(key) - if 'lora_A' == name: - self.lora_A[tag] = tensor - else: - self.lora_B[tag] = tensor - -def main(args): - base = Base(args.base, args.fuse) - lora = LoRA(args.lora, args.scale) - base.apply(lora, args.out) - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='apply_lora', formatter_class=argparse.RawTextHelpFormatter) - parser.add_argument('--base', type=str, required=True, help='base model json path.') - parser.add_argument('--lora', type=str, required=True, help='lora dir path or *.safetensors path.') - parser.add_argument('--scale', type=float, default=4.0, help='lora scale: `alpha/r`.') - parser.add_argument('--fuse', type=bool, default=False, help='fuse A and B.') - parser.add_argument('--out', type=str, default='lora.json', help='out file name.') - args = parser.parse_args() - main(args) + return out diff --git a/transformers/llm/export/utils/mnn_converter.py b/transformers/llm/export/utils/mnn_converter.py new file mode 100644 index 000000000..d87ae4118 --- /dev/null +++ b/transformers/llm/export/utils/mnn_converter.py @@ -0,0 +1,416 @@ +import os +import sys +import json +import torch +import numpy as np + +from tqdm import tqdm +from .spinner import spinner_run +from .gptq import GPTQ +from .lora import LoRA + +EXPORT_LOG = '.export.log' + +class MNNConveter: + def __init__(self, onnx_path, weight_ops, config): + self.weight_ops = weight_ops + self.config = config + self.quant_block = config.args.quant_block + self.quant_bit = config.args.quant_bit + self.lm_quant_bit = config.args.lm_quant_bit + self.symmetric = config.args.sym + self.mnn_weight_offset = 0 + self.onnx_model_path = onnx_path + self.mnn_name = os.path.basename(onnx_path).replace('.onnx', '.mnn') + self.mnn_model_path = os.path.join(config.args.dst_path, self.mnn_name) + self.mnn_weight_path = f'{self.mnn_model_path}.weight' + if os.path.exists(config.args.mnnconvert): + self.mnnconvert = config.args.mnnconvert + else: + self.mnnconvert = None + + def convert(self, convert_args): + sfd = os.dup(1) + log_fp = open(EXPORT_LOG, "a") + log_fd = log_fp.fileno() + # mnnconvert ... > .export.log + os.dup2(log_fd, 1) + try: + sys.argv = convert_args + sys.argc = len(convert_args) + if self.mnnconvert is None: + from MNN.tools import mnnconvert + mnnconvert.main() + else: + convert_args[0] = self.mnnconvert + cmd = ' '.join(convert_args) + message = os.popen(cmd).read() + print(message) + sys.argv = [] + finally: + os.dup2(sfd, 1) + os.close(log_fd) + + @spinner_run(f'convert onnx model to ') + def onnx2mnn(self, onnx_path, mnn_path, args = []): + convert_args = [ + '', + '-f', + 'ONNX', + '--modelFile', + str(onnx_path), + '--MNNModel', + str(mnn_path), + '--transformerFuse', + '--allowCustomOp' + ] + convert_args += args + self.convert(convert_args) + return mnn_path + + def mnn2json(self, mnn_path, json_path): + convert_args = [ + '', + '-f', + 'MNN', + '--modelFile', + str(mnn_path), + '--JsonFile', + str(json_path) + ] + self.convert(convert_args) + return json_path + + def json2mnn(self, json_path, mnn_path): + convert_args = [ + '', + '-f', + 'JSON', + '--modelFile', + str(json_path), + '--MNNModel', + str(mnn_path) + ] + self.convert(convert_args) + return mnn_path + + def removeDupOps(self, mnn_path): + convert_args = [ + '', + '-f', + 'MNN', + '--modelFile', + str(mnn_path), + '--MNNModel', + str(mnn_path), + '--optimizeLevel=1' + ] + self.convert(convert_args) + return mnn_path + + def export(self, quant_bit = None, quant_block = None): + if self.weight_ops is None: + if quant_bit is None: + quant_bit = self.quant_bit + if quant_block is None: + quant_block = self.quant_block + if quant_bit == 16: + quant_args = ['--fp16'] + else: + quant_args = [ + '--weightQuantBits', + str(quant_bit), + '--weightQuantBlock', + str(quant_block) + ] + self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, quant_args) + else: + mnn_json = f'{self.mnn_model_path}.json' + self.onnx2mnn(self.onnx_model_path, self.mnn_model_path) + self.mnn2json(self.mnn_model_path, mnn_json) + self.rebuild(mnn_json) + self.json2mnn(mnn_json, self.mnn_model_path) + self.removeDupOps(self.mnn_model_path) + if self.config.args.gptq_path is not None: + self.apply_gptq(mnn_json) + if self.config.args.lora_path is not None and self.config.args.lora_split: + self.export_lora(mnn_json) + + @spinner_run(f'apply gptq to ') + def apply_gptq(self, mnn_json): + GPTQ(self.config.args.gptq_path).apply(mnn_json, self.mnn_weight_path) + return self.mnn_weight_path + + @spinner_run(f'export split lora to ') + def export_lora(self, mnn_json): + lora_model = os.path.join(self.config.args.dst_path, 'lora.mnn') + lora_json = f'{lora_model}.json' + LoRA(self.config.args.lora_path).apply(mnn_json, lora_json) + self.json2mnn(lora_json, lora_model) + if os.path.exists(lora_json): + os.remove(lora_json) + return lora_model + + @spinner_run(f'quant model weight to ', True) + def rebuild(self, json_path): + mnn_graph = json.load(open(json_path, 'rt')) + new_ops = [] + with open(self.mnn_weight_path, 'wb') as self.mnn_weight: + for op in tqdm(mnn_graph['oplists'], 'Quant weights'): + if op['type'] == 'Extra': + new_ops += self.rebuild_op(op, mnn_graph) + else: + new_ops.append(op) + mnn_graph['oplists'] = new_ops + with open(json_path, 'w', encoding='utf-8') as file: + json.dump(mnn_graph, file, ensure_ascii=False, indent=4) + return self.mnn_weight_path + + def quant(self, weight, quant_bit, quant_block, symmetric): + if torch.cuda.is_available(): + weight = weight.cuda() + if torch.backends.mps.is_available(): + weight = weight.to('mps') + oc, ic = weight.shape + if quant_block == 0: + block_size = ic + else: + block_size = quant_block + while ic % block_size != 0: + block_size /= 2 + block_size = int(block_size) + block_num = ic // block_size + weight = weight.reshape(oc, block_num, block_size) + offset = 1 << (quant_bit - 1) + clip_max = offset - 1 + if symmetric: + clip_min = -clip_max + abs_max, _ = torch.max(torch.abs(weight), axis=-1, keepdims=True) + scale = abs_max / clip_max + q_weight = torch.round(weight / scale) + q_weight = (torch.clamp(q_weight.flatten(), clip_min, clip_max) + offset).to(torch.uint8) + alpha = scale.flatten() + else: + clip_min = -offset + max_val, _ = torch.max(weight, axis=-1, keepdims=True) + min_val, _ = torch.min(weight, axis=-1, keepdims=True) + scale = (max_val - min_val) / (clip_max - clip_min) + + if self.config.args.awq: + q_weight = torch.round(weight / scale) - torch.round(min_val / scale) + clip_min + zeros = (torch.round(min_val / scale) - clip_min) * scale + else: + q_weight = torch.round((weight - min_val) / scale) + clip_min + zeros = min_val - scale * clip_min + q_weight = (torch.clamp(q_weight.flatten(), clip_min, clip_max) + offset).to(torch.uint8) + alpha = torch.stack([zeros.flatten(), scale.flatten()], axis=-1).flatten() + + q_weight = q_weight.reshape(-1, 2) + if quant_bit == 4: + q_weight = q_weight[:, 0] * 16 + q_weight[:, 1] + + # support for `MNN >= 2.9.6` + clip_min = 1 + + if q_weight.device is not torch.device('cpu'): + return q_weight.cpu(), alpha.float().cpu(), clip_min + return q_weight, alpha.float(), clip_min + + def write_weight(self, data): + if isinstance(data, torch.Tensor): + data = data.numpy() + return self.mnn_weight.write(data.tobytes()) + + def write_header(self, ic, oc, quant_bit): + dim_num = self.mnn_weight.write(b'\x02') + shape_dtype = np.int16 + if oc > 65535 or ic > 65535: + shape_dtype = np.int32 + dim_length = self.write_weight(np.array([oc, ic]).astype(shape_dtype)) + offset = 1 << (quant_bit - 1) + weight_map = [i for i in range(-offset, offset)] + if len(weight_map) == 256: + weight_map.insert(0, 0) + else: + weight_map.insert(0, len(weight_map)) + map_length = self.write_weight(np.array(weight_map, dtype=np.int8)) + header_length = dim_num + dim_length + map_length + return header_length, shape_dtype == np.int32 + + def build_weight(self, linear, quant_bit, quant_block, symmetric): + ic, oc = linear.in_features, linear.out_features + if quant_bit == 16: + half_weight = linear.weight.data.flatten().half() + weight_len = self.write_weight(half_weight) + alpha_len, q_min, shape_int32, header_len = 0, 0, False, 0 + else: + assert(quant_bit in (4, 8)) + q_weight, alpha, q_min = self.quant(linear.weight.data, quant_bit, quant_block, symmetric) + header_len, shape_int32 = self.write_header(ic, oc, quant_bit) + weight_len = self.write_weight(q_weight) + header_len + alpha_len = self.write_weight(alpha) + if linear.bias is not None: + bias = linear.bias.data.flatten().float() + bias_length = self.write_weight(bias) + else: + bias_length = 0 + # bias = np.zeros([oc], dtype=np.float32) + # bias_length = self.write_weight(bias) + external = [self.mnn_weight_offset, weight_len, alpha_len, bias_length, 0] + self.mnn_weight_offset += (weight_len + alpha_len + bias_length) + return external, q_min, shape_int32, header_len + + def build_tensor(self, graph, tensor_name): + tensor_idx = [len(graph['tensorName'])] + graph['tensorName'].append(tensor_name) + return tensor_idx + + def rebuild_op(self, op, graph): + op_type = op['main']['type'] + if op_type == 'FakeLinear': + return self.rebuild_linear(op, graph) + if op_type == 'FusedAttention': + return self.rebuild_attnention(op, graph) + + def rebuild_attnention(self, op, graph): + attrs = op['main']['attr'] + for attr in attrs: + if attr['key'] == 'name': + name = attr['s'] + origin_input = op['inputIndexes'] + origin_output = op['outputIndexes'] + fused_attention = { + "inputIndexes": origin_input, + "main_type": "AttentionParam", + "main": { "kv_cache": True }, + "name": name, + "outputIndexes": origin_output, + "type": "Attention", + "defaultDimentionFormat": "NHWC" + } + return [fused_attention] + + def rebuild_linear(self, op, graph): + attrs = op['main']['attr'] + for attr in attrs: + if attr['key'] == 'name': + name = attr['s'] + elif attr['key'] == "in_features": + ic = attr["i"] + elif attr['key'] == "out_features": + oc = attr["i"] + elif attr['key'] == "has_bias": + has_bias = attr["i"] + linear = self.weight_ops[name] + assert(linear.in_features == ic and + linear.out_features == oc and + (linear.bias is not None) == has_bias) + + is_lm = 'lm_head' in name + quant_bit = self.lm_quant_bit if is_lm else self.quant_bit + block_size = ic if self.quant_block == 0 else self.quant_block + external, q_min, shape_int32, header_len = self.build_weight(linear, quant_bit, self.quant_block, self.symmetric) + if is_lm and self.config.tie_word_embeddings: + weight_offset = external[0] + header_len + alpha_offset = external[0] + external[1] + alpha_size = external[2] + self.config.llm_config['tie_embeddings'] = [weight_offset, alpha_offset, alpha_size, quant_bit, self.quant_block] + + origin_input = op['inputIndexes'] + origin_output = op['outputIndexes'] + # build new tensor + pre_reshape_name = f'{name}/pre_reshape' + pre_convert_name = f'{name}/pre_convert' + conv_name = name + post_convert_name = f'{name}/post_convert' + post_reshape_name = f'{name}/post_reshape' + pre_reshape_output = self.build_tensor(graph, pre_reshape_name) + pre_convert_output = self.build_tensor(graph, pre_convert_name) + conv_output = self.build_tensor(graph, conv_name) + post_convert_output = self.build_tensor(graph, post_convert_name) + # [batch, seq, hidden_size_i] -[Linear] -> [batch, seq, hidden_size_o] + # [1, seq, hidden_size_i] ->[Reshape]-> [seq, hidden_size_i, 1, 1] + # -[Convert]-[Convolution]-[Convert]-> [Reshape] -> [1, seq, hidden_size_o] + pre_reshape = { + "name": pre_reshape_name, + "type": "Reshape", + "inputIndexes": origin_input, + "outputIndexes": pre_reshape_output, + "main_type": "Reshape", + "main": { + "dims": [-1, ic, 1, 1], + "dimType": "NCHW" + }, + "defaultDimentionFormat": "NHWC" + } + pre_convert = { + "name": pre_convert_name, + "inputIndexes": pre_reshape_output, + "outputIndexes": pre_convert_output, + "type": "ConvertTensor", + "main_type": "TensorConvertInfo", + "main": { + "source": "NCHW", + "dest": "NC4HW4" + }, + "defaultDimentionFormat": "NHWC" + } + + if quant_bit == 16: + quanParameter = { "type": 3 } + else: + if self.symmetric: + aMin = 0 + readType = 0 + else: + aMin = q_min + readType = oc * (ic // block_size) + + quanParameter = { + "quantScale": 1.0, "scaleIn": 0.0, "scaleOut": 0.0, + "useInt32": False, "has_scaleInt": False, "shapeInt32": shape_int32, + "type": 1, "aMax": 0, "aMin": aMin, "readType": readType, "weightSize": 0 + } + conv_op = { + "name": conv_name, + "inputIndexes": pre_convert_output, + "outputIndexes": conv_output, + "type": "Convolution", + "main_type": "Convolution2D", + "main": { + 'common': { + 'dilateX': 1, 'dilateY': 1, 'strideX': 1, 'strideY': 1, + 'kernelX': 1, 'kernelY': 1, 'padX': 0, 'padY': 0, 'group': 1, + 'outputCount': oc, 'relu': False, 'padMode': 'CAFFE', + 'relu6': False, 'inputCount': ic, 'hasOutputShape': False + }, + "quanParameter": quanParameter, + "external": external + }, + "defaultDimentionFormat": "NHWC" + } + post_convert = { + "name": post_convert_name, + "inputIndexes": conv_output, + "outputIndexes": post_convert_output, + "type": "ConvertTensor", + "main_type": "TensorConvertInfo", + "main": { + "source": "NC4HW4", + "dest": "NCHW" + }, + "defaultDimentionFormat": "NHWC" + } + post_reshape = { + "name": post_reshape_name, + "type": "Reshape", + "inputIndexes": post_convert_output, + "outputIndexes": origin_output, + "main_type": "Reshape", + "main": { + "dims": [1, -1, oc], + "dimType": "NCHW" + }, + "defaultDimentionFormat": "NHWC" + } + return [pre_reshape, pre_convert, conv_op, post_convert, post_reshape] \ No newline at end of file diff --git a/transformers/llm/export/utils/model_mapper.py b/transformers/llm/export/utils/model_mapper.py new file mode 100644 index 000000000..e55bc6840 --- /dev/null +++ b/transformers/llm/export/utils/model_mapper.py @@ -0,0 +1,295 @@ +import copy + +class ModelMapper: + def __init__(self): + self.attrs = [] + self.mapper = dict() + self.regist_models() + + def get_map(self, config): + model_type = config.model_type + if model_type == 'chatglm': + if hasattr(config, 'vocab_size') and config.vocab_size == 130528: + model_type = 'chatglm' + else: + model_type = 'chatglm2' + if model_type in self.mapper: + return model_type, self.mapper[model_type] + return model_type, self.default_map + + def regist(self, model_type, model_map): + assert('config' in model_map and + 'decoder' in model_map and + 'attention' in model_map) + self.mapper[model_type] = model_map + + def regist_models(self): + self.defualt_map() + # regist models + self.regist_llama() + self.regist_mllama() + self.regist_qwen() + self.regist_glm() + self.regist_glm2() + self.regist_phi() + self.regist_gemma2() + self.register_openelm() + + def regist_llama(self): + llama_map = self.default_map + self.regist('llama', llama_map) + self.regist('qwen2', llama_map) + self.regist('internlm', llama_map) + self.regist('mobilellm', llama_map) + # baichuan + baichuan_map = copy.deepcopy(self.default_map) + baichuan_map[self.attention_key] = { + 'qkv_proj': 'W_pack', + 'o_proj': 'o_proj' + } + self.regist('baichuan', baichuan_map) + + def regist_mllama(self): + mllama_map = { + 'config': { + 'hidden_size': 'text_config.hidden_size', + 'num_attention_heads': 'text_config.num_attention_heads', + 'num_hidden_layers': 'text_config.num_hidden_layers', + 'num_key_value_heads': 'text_config.num_key_value_heads', + 'rope_theta': 'text_config.rope_theta' + }, + 'model': { + 'lm_': 'language_model.lm_head', + 'embed_': 'language_model.model.embed_tokens', + 'blocks_': 'language_model.model.layers', + 'final_layernorm_': 'language_model.model.norm', + 'visual': 'vision_model', + 'multi_modal_projector': 'multi_modal_projector' + }, + 'decoder': { + 'self_attn': 'self_attn', + 'cross_attn': 'cross_attn', + 'mlp': 'mlp', + 'input_layernorm': 'input_layernorm', + 'post_attention_layernorm': 'post_attention_layernorm' + }, + 'attention': { + 'q_proj': 'q_proj', + 'k_proj': 'k_proj', + 'v_proj': 'v_proj', + 'o_proj': 'o_proj', + 'q_norm': 'q_norm', + 'k_norm': 'k_norm', + 'cross_attn_attn_gate': 'cross_attn_attn_gate', + 'cross_attn_mlp_gate': 'cross_attn_mlp_gate' + } + } + self.regist('mllama', mllama_map) + + def regist_qwen(self): + qwen_map = { + 'config': { + 'hidden_size': 'hidden_size', + 'num_attention_heads': 'num_attention_heads', + 'num_hidden_layers': 'num_hidden_layers', + 'rope_theta': 'rotary_emb_base', + }, + 'model': { + 'lm_': 'lm_head', + 'embed_': 'transformer.wte', + 'blocks_': 'transformer.h', + 'final_layernorm_': 'transformer.ln_f', + 'visual': 'transformer.visual' + }, + 'decoder': { + 'self_attn': 'attn', + 'mlp': 'mlp', + 'input_layernorm': 'ln_1', + 'post_attention_layernorm': 'ln_2' + }, + 'attention': { + 'qkv_proj': 'c_attn', + 'o_proj': 'c_proj' + } + } + self.regist('qwen', qwen_map) + + def regist_glm(self): + glm_map = { + 'config': { + 'hidden_size': 'hidden_size', + 'num_attention_heads': 'num_attention_heads', + 'num_hidden_layers': 'num_layers' + }, + 'model': { + 'lm_': 'lm_head', + 'embed_': 'transformer.word_embeddings', + 'blocks_': 'transformer.layers', + 'final_layernorm_': 'transformer.final_layernorm', + }, + 'decoder': { + 'self_attn': 'attention', + 'mlp': 'mlp', + 'input_layernorm': 'input_layernorm', + 'post_attention_layernorm': 'post_attention_layernorm' + }, + 'attention': { + 'qkv_proj': 'query_key_value', + 'o_proj': 'dense' + } + } + self.regist('chatglm', glm_map) + + def regist_glm2(self): + glm2_map = { + 'config': { + 'hidden_size': 'hidden_size', + 'num_attention_heads': 'num_attention_heads', + 'num_key_value_heads': 'multi_query_group_num', + 'num_hidden_layers': 'num_layers', + }, + 'model': { + 'lm_': 'transformer.output_layer', + 'embed_': 'transformer.embedding.word_embeddings', + 'blocks_': 'transformer.encoder.layers', + 'final_layernorm_': 'transformer.encoder.final_layernorm', + }, + 'decoder': { + 'self_attn': 'self_attention', + 'mlp': 'mlp', + 'input_layernorm': 'input_layernorm', + 'post_attention_layernorm': 'post_attention_layernorm' + }, + 'attention': { + 'qkv_proj': 'query_key_value', + 'o_proj': 'dense' + } + } + self.regist('chatglm2', glm2_map) + + def regist_phi(self): + phi_map = { + 'config': { + 'hidden_size': 'n_embd', + 'num_attention_heads': 'n_head', + 'num_hidden_layers': 'n_layer', + 'rotary_dim': 'rotary_dim' + }, + 'model': { + 'lm_': 'lm_head.linear', + 'embed_': 'transformer.embd.wte', + 'blocks_': 'transformer.h', + 'final_layernorm_': 'lm_head.ln', + }, + 'decoder': { + 'self_attn': 'mixer', + 'mlp': 'mlp', + 'input_layernorm': 'ln', + }, + 'attention': { + 'qkv_proj': 'Wqkv', + 'o_proj': 'out_proj' + } + } + self.regist('phi-msft', phi_map) + + def regist_gemma2(self): + gemma2_config = copy.deepcopy(self.default_config) + gemma2_config['head_dim'] = 'head_dim' + gemma2_decoder = copy.deepcopy(self.default_decoder) + gemma2_decoder['pre_feedforward_layernorm'] = 'pre_feedforward_layernorm' + gemma2_decoder['post_feedforward_layernorm'] = 'post_feedforward_layernorm' + gemma2_map = { + 'config': gemma2_config, + 'model': self.defualt_model, + 'decoder': gemma2_decoder, + 'attention': self.default_attention + } + self.regist('gemma2', gemma2_map) + + def register_openelm(self): + openelm_config = { + 'hidden_size': 'model_dim', + 'head_dim': 'head_dim', + 'num_attention_heads': 'num_query_heads', + 'num_hidden_layers': 'num_transformer_layers', + 'num_key_value_heads': 'num_kv_heads', + 'rope_theta': 'rope_freq_constant' + } + openelm_model = { + 'lm_': 'lm_head', + 'embed_': 'transformer.token_embeddings', + 'blocks_': 'transformer.layers', + 'final_layernorm_': 'transformer.norm' + } + openelm_decoder = { + 'self_attn': 'attn', + 'mlp': 'ffn', + 'input_layernorm': 'attn_norm', + 'post_attention_layernorm': 'ffn_norm' + } + openelm_attention = { + 'qkv_proj': 'qkv_proj', + 'o_proj': 'out_proj', + 'q_norm': 'q_norm', + 'k_norm': 'k_norm' + } + openelm_map = { + 'config': openelm_config, + 'model': openelm_model, + 'decoder': openelm_decoder, + 'attention': openelm_attention + } + self.regist('openelm', openelm_map) + + def defualt_map(self): + # default map is `LlamaForCausalLM` + self.config_key = 'config' + self.model_key = 'model' + self.decoder_key = 'decoder' + self.attention_key = 'attention' + self.default_config = { + 'hidden_size': 'hidden_size', + 'num_attention_heads': 'num_attention_heads', + 'num_hidden_layers': 'num_hidden_layers', + 'num_key_value_heads': 'num_key_value_heads', + 'rope_theta': 'rope_theta' + } + self.defualt_model = { + 'lm_': 'lm_head', + 'embed_': 'model.embed_tokens', + 'blocks_': 'model.layers', + 'final_layernorm_': 'model.norm', + 'visual': 'visual' + } + self.default_decoder = { + 'self_attn': 'self_attn', + 'mlp': 'mlp', + 'input_layernorm': 'input_layernorm', + 'post_attention_layernorm': 'post_attention_layernorm' + } + self.default_attention = { + 'q_proj': 'q_proj', + 'k_proj': 'k_proj', + 'v_proj': 'v_proj', + 'o_proj': 'o_proj' + } + self.default_map = { + 'config': self.default_config, + 'model': self.defualt_model, + 'decoder': self.default_decoder, + 'attention': self.default_attention + } + + @staticmethod + def do_map(dst, src, map): + for dst_attr, src_attr in map.items(): + attributes = src_attr.split('.') + obj = src + for attr in attributes: + if hasattr(obj, attr): + obj = getattr(obj, attr) + else: + obj = None + break + setattr(dst, dst_attr, obj) \ No newline at end of file diff --git a/transformers/llm/export/utils/onnx.py b/transformers/llm/export/utils/onnx.py new file mode 100644 index 000000000..b2961e96d --- /dev/null +++ b/transformers/llm/export/utils/onnx.py @@ -0,0 +1,70 @@ +import os +import onnx + +class OnnxRebuilder: + def __init__(self, onnx_path, weight_ops): + self.weight_ops = weight_ops + self.onnx_model = onnx.load(onnx_path) + self.dst_path = onnx_path + self.onnx_weight_path = f'{onnx_path}.data' + self.onnx_weight_offset = 0 + + def make_external(self, name, data, shape): + # write to external weight + length = self.onnx_weight.write(data.tobytes()) + location = os.path.basename(self.onnx_weight_path) + offset = self.onnx_weight_offset + self.onnx_weight_offset += length + tensor = onnx.TensorProto() + tensor.name = name + tensor.data_type = onnx.TensorProto.FLOAT + tensor.dims.extend(shape) + # external info + tensor.data_location = onnx.TensorProto.EXTERNAL + for k, v in { "location": location, "offset": offset, "length": length }.items(): + entry = tensor.external_data.add() + entry.key = k + entry.value = str(v) + self.onnx_model.graph.initializer.append(tensor) + + def build_weight(self, name, has_bias, ic, oc): + assert(name in self.weight_ops) + linear = self.weight_ops[name] + assert(linear.in_features == ic and + linear.out_features == oc and + (linear.bias is not None) == has_bias) + weight_name, bias_name = f'{name}_weight', f'{name}_bias' + weight = linear.weight.data.transpose(1, 0).flatten().float().numpy() + self.make_external(weight_name, weight, [ic, oc]) + if has_bias: + bias = linear.bias.data.flatten().float().numpy() + self.make_external(bias_name, bias, [oc]) + return weight_name, bias_name + + def rebuild(self): + from onnx import helper + new_nodes = [] + self.onnx_weight = open(self.onnx_weight_path, 'wb') + for node in self.onnx_model.graph.node: + if node.op_type == 'FakeLinear': + attributes = {a.name: a for a in node.attribute} + name = attributes.get('name').s.decode('utf-8') + has_bias = attributes.get('has_bias').i + ic = attributes.get('in_features').i + oc = attributes.get('out_features').i + weight, bias = self.build_weight(name, has_bias, ic, oc) + if has_bias: + # fakelinear -> matmul + add + middle_tensor = f'{name}_matmul' + new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], [middle_tensor], name)) + new_nodes.append(helper.make_node('Add', [middle_tensor, bias], node.output, f'{name}/Add')) + else: + # fakelinear -> matmul + new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], node.output, name)) + else: + new_nodes.append(node) + self.onnx_weight.close() + del self.onnx_model.graph.node[:] + self.onnx_model.graph.node.extend(new_nodes) + onnx.save(self.onnx_model, self.dst_path) + return self.onnx_weight_path \ No newline at end of file diff --git a/transformers/llm/export/utils/onnx_rebuilder.py b/transformers/llm/export/utils/onnx_rebuilder.py new file mode 100644 index 000000000..b2961e96d --- /dev/null +++ b/transformers/llm/export/utils/onnx_rebuilder.py @@ -0,0 +1,70 @@ +import os +import onnx + +class OnnxRebuilder: + def __init__(self, onnx_path, weight_ops): + self.weight_ops = weight_ops + self.onnx_model = onnx.load(onnx_path) + self.dst_path = onnx_path + self.onnx_weight_path = f'{onnx_path}.data' + self.onnx_weight_offset = 0 + + def make_external(self, name, data, shape): + # write to external weight + length = self.onnx_weight.write(data.tobytes()) + location = os.path.basename(self.onnx_weight_path) + offset = self.onnx_weight_offset + self.onnx_weight_offset += length + tensor = onnx.TensorProto() + tensor.name = name + tensor.data_type = onnx.TensorProto.FLOAT + tensor.dims.extend(shape) + # external info + tensor.data_location = onnx.TensorProto.EXTERNAL + for k, v in { "location": location, "offset": offset, "length": length }.items(): + entry = tensor.external_data.add() + entry.key = k + entry.value = str(v) + self.onnx_model.graph.initializer.append(tensor) + + def build_weight(self, name, has_bias, ic, oc): + assert(name in self.weight_ops) + linear = self.weight_ops[name] + assert(linear.in_features == ic and + linear.out_features == oc and + (linear.bias is not None) == has_bias) + weight_name, bias_name = f'{name}_weight', f'{name}_bias' + weight = linear.weight.data.transpose(1, 0).flatten().float().numpy() + self.make_external(weight_name, weight, [ic, oc]) + if has_bias: + bias = linear.bias.data.flatten().float().numpy() + self.make_external(bias_name, bias, [oc]) + return weight_name, bias_name + + def rebuild(self): + from onnx import helper + new_nodes = [] + self.onnx_weight = open(self.onnx_weight_path, 'wb') + for node in self.onnx_model.graph.node: + if node.op_type == 'FakeLinear': + attributes = {a.name: a for a in node.attribute} + name = attributes.get('name').s.decode('utf-8') + has_bias = attributes.get('has_bias').i + ic = attributes.get('in_features').i + oc = attributes.get('out_features').i + weight, bias = self.build_weight(name, has_bias, ic, oc) + if has_bias: + # fakelinear -> matmul + add + middle_tensor = f'{name}_matmul' + new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], [middle_tensor], name)) + new_nodes.append(helper.make_node('Add', [middle_tensor, bias], node.output, f'{name}/Add')) + else: + # fakelinear -> matmul + new_nodes.append(helper.make_node('MatMul', [node.input[0], weight], node.output, name)) + else: + new_nodes.append(node) + self.onnx_weight.close() + del self.onnx_model.graph.node[:] + self.onnx_model.graph.node.extend(new_nodes) + onnx.save(self.onnx_model, self.dst_path) + return self.onnx_weight_path \ No newline at end of file diff --git a/transformers/llm/export/utils/spinner.py b/transformers/llm/export/utils/spinner.py new file mode 100644 index 000000000..fd4d561a4 --- /dev/null +++ b/transformers/llm/export/utils/spinner.py @@ -0,0 +1,31 @@ +import time +import functools +import traceback +from yaspin import yaspin + +RESET = "\033[0m" +GREEN = "\033[32;1m" +YELLOW = "\033[33;4m" + +def spinner_run(text='Processing...', hide=False): + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + with yaspin(text=text, color="cyan") as spinner: + start = time.time() + try: + if hide: spinner.hide() + result = func(*args, **kwargs) + if hide: spinner.show() + except Exception as e: + spinner.fail("💥 Failed") + traceback.print_exc() + exit(1) + end = time.time() + during = f'[{end-start:05.2f} s]'.replace('[0', '[ ') + padding = ' ' * (64 - len(spinner.text) - len(result)) + spinner.text = f'{spinner.text}{YELLOW}{result}{RESET}{padding}{GREEN}{during}{RESET}' + spinner.ok("✅ Done") + return result + return wrapper + return decorator \ No newline at end of file diff --git a/transformers/llm/export/utils/transformers.py b/transformers/llm/export/utils/transformers.py new file mode 100644 index 000000000..3134ed6f6 --- /dev/null +++ b/transformers/llm/export/utils/transformers.py @@ -0,0 +1,333 @@ +import math +import torch +from typing import Optional, Tuple + +from .model_mapper import ModelMapper +from .custom_op import FusedAttention + +class Embedding(torch.nn.Module): + def __init__(self, embed, config): + super().__init__() + self.hidden_size = config.hidden_size + self.embed = embed + if config.model_type == 'gemma2': + normalizer = torch.tensor(self.hidden_size**0.5) + self.embed.weight.data *= normalizer + + def forward(self, input_ids): + inputs_embeds = self.embed(input_ids).view(-1, 1, self.hidden_size) + return inputs_embeds + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + +class Attention(torch.nn.Module): + def __init__(self, attn, layer_id, config): + super().__init__() + self.export_fused_attn = False + self.fused_attn = FusedAttention(config.hidden_size, f'/layers.{layer_id}/self_attn/FusedAttention') + self.layer_id = layer_id + self.hidden_size = config.hidden_size + self.head_dim = config.head_dim + if isinstance(config.num_attention_heads, list): + self.num_heads = config.num_attention_heads[layer_id] + self.num_key_value_heads = config.num_key_value_heads[layer_id] + else: + self.head_dim = config.head_dim + self.num_heads = config.num_attention_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.rotary = config.rotary + + ModelMapper.do_map(self, attn, config.model_map['attention']) + + if hasattr(self, 'qkv_proj') and self.qkv_proj is not None: + # split qkv linear to q, k, v + split_sizes = [self.hidden_size] * 3 + if self.qkv_proj.weight.shape[0] != self.hidden_size * 3: + # M/GQA + split_sizes = [ + self.num_heads * self.head_dim, # q_size + self.num_key_value_heads * self.head_dim, # k_size + self.num_key_value_heads * self.head_dim # v_size + ] + + self.q_proj = torch.nn.Linear(self.hidden_size, split_sizes[0]) + self.k_proj = torch.nn.Linear(self.hidden_size, split_sizes[1]) + self.v_proj = torch.nn.Linear(self.hidden_size, split_sizes[2]) + if config.model_type == 'chatglm': + # chatglm-6b + qkv_weight = self.qkv_proj.weight.data.view(self.num_heads, 3, self.head_dim, self.hidden_size) + self.q_proj.weight.data = qkv_weight[:, 0, :, :].reshape(self.hidden_size, self.hidden_size) + self.k_proj.weight.data = qkv_weight[:, 1, :, :].reshape(self.hidden_size, self.hidden_size) + self.v_proj.weight.data = qkv_weight[:, 2, :, :].reshape(self.hidden_size, self.hidden_size) + qkv_bias = self.qkv_proj.bias.data.view(self.num_heads, 3, self.head_dim) + self.q_proj.bias.data = qkv_bias[:, 0, :].reshape(self.hidden_size) + self.k_proj.bias.data = qkv_bias[:, 1, :].reshape(self.hidden_size) + self.v_proj.bias.data = qkv_bias[:, 2, :].reshape(self.hidden_size) + else: + # other + qw, kw, vw = torch.split(self.qkv_proj.weight, split_sizes) + self.q_proj.weight.data = qw + self.k_proj.weight.data = kw + self.v_proj.weight.data = vw + if self.qkv_proj.bias is not None: + qb, kb, vb = torch.split(self.qkv_proj.bias, split_sizes) + self.q_proj.bias.data = qb + self.k_proj.bias.data = kb + self.v_proj.bias.data = vb + else: + self.q_proj.bias.data = torch.zeros(split_sizes[0]) + self.k_proj.bias.data = torch.zeros(split_sizes[1]) + self.v_proj.bias.data = torch.zeros(split_sizes[2]) + self.q_proj.weight.requires_grad = False + self.k_proj.weight.requires_grad = False + self.v_proj.weight.requires_grad = False + self.q_proj.bias.requires_grad = False + self.k_proj.bias.requires_grad = False + self.v_proj.bias.requires_grad = False + + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + rotary_pos_emb: Optional[torch.Tensor] = None, + cross_attention_states: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + bsz, q_len, _ = hidden_states.size() + query_states = self.q_proj(hidden_states) + if cross_attention_states is not None: + hidden_states = cross_attention_states + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) + # openelm model has qk_norm + if hasattr(self, 'q_norm') and self.q_norm is not None and \ + hasattr(self, 'k_norm') and self.k_norm is not None : + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + kv_seq_len = key_states.shape[1] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[1] + + # rope + cos, sin = rotary_pos_emb[0], rotary_pos_emb[1] + query_states = self.rotary.apply_rotary_pos(query_states, cos, sin) + key_states = self.rotary.apply_rotary_pos(key_states, cos, sin) + + if self.export_fused_attn: + attn_output = self.fused_attn(query_states, key_states, value_states, attention_mask) + attn_output = self.o_proj(attn_output) + return attn_output, past_key_value + + # kv cache + if past_key_value is not None: + past_key, past_value = past_key_value[0], past_key_value[1] + key_states = torch.cat((past_key, key_states), dim=1) + value_states = torch.cat((past_value, value_states), dim=1) + + past_key_value = torch.stack((key_states, value_states)) + query_states = query_states.transpose(1, 2) + key_states = key_states.permute([0, 2, 3, 1]) + value_states = value_states.transpose(1, 2) + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + #------- attention ---------- + # query_states @ key_states + attn_weights = torch.matmul(query_states, key_states) / math.sqrt(self.head_dim) + # attention_mask + if attention_mask.dtype in (torch.bool, torch.int32): + # chatglm + attn_weights.masked_fill_(attention_mask, -10000.0) + else: + attn_weights = attn_weights + attention_mask + # upcast softmax to fp32 + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + # attn_weights @ value_states + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, -1) + attn_output = self.o_proj(attn_output) + return attn_output, past_key_value + +def rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + +class Rotary(torch.nn.Module): + def __init__(self, config): + super().__init__() + self.rope_theta = config.rope_theta + self.rotary_dim = config.head_dim + self.model_type = config.model_type + if hasattr(config, 'rotary_dim'): + self.rotary_dim = config.rotary_dim + if self.model_type == 'chatglm': + self.rotary_dim = config.head_dim // 2 + self.theta = 1.0 / (self.rope_theta ** (torch.arange(0, self.rotary_dim, 2, dtype=torch.float32) / self.rotary_dim)) + + def forward(self, position_ids): + position_ids = position_ids.float().reshape(-1, 1) + idx_theta = position_ids * self.theta + rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)]) + if self.model_type != 'chatglm2': + rotary_pos_emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) + rotary_pos_emb = rotary_pos_emb.unsqueeze(2).unsqueeze(1) + return rotary_pos_emb + + def apply_rotary_pos(self, x, cos, sin): + if self.model_type == 'chatglm': + return self.chatglm_rotary_pos(x, cos, sin) + if self.model_type == 'chatglm2': + return self.chatglm2_rotary_pos(x, cos, sin) + if self.model_type == 'phi-msft': + return self.phi_rotary_pos(x, cos, sin) + return self.llama_rotary_pos(x, cos, sin) + + def llama_rotary_pos(self, x, cos, sin): + x = (x * cos) + (rotate_half(x) * sin) + return x + + def phi_rotary_pos(self, x, cos, sin): + x, x_pass = x[..., :self.rotary_dim], x[..., self.rotary_dim:] + x = (x * cos) + (rotate_half(x) * sin) + return torch.cat((x, x_pass), dim=-1) + + def chatglm2_rotary_pos(self, x, cos, sin): + x, x_pass = x[..., :self.rotary_dim], x[..., self.rotary_dim:] + b, s, n, h = x.shape + xshaped = x.view(b, s, n, h//2, 2) + x = torch.concat( + [ + xshaped[..., 0] * cos - xshaped[..., 1] * sin, + xshaped[..., 1] * cos + xshaped[..., 0] * sin, + ], + -1, + ) + return torch.cat((x, x_pass), dim=-1) + + def chatglm_rotary_pos(self, x, cos, sin): + seq = x.shape[1] + x1, x2 = x[..., :self.rotary_dim], x[..., self.rotary_dim:] + cos1, sin1 = cos[:, :seq, ...], sin[:, :seq, ...] + cos2, sin2 = cos[:, seq:, ...], sin[:, seq:, ...] + x1 = (x1 * cos1) + (rotate_half(x1) * sin1) + x2 = (x2 * cos2) + (rotate_half(x2) * sin2) + return torch.cat((x1, x2), dim=-1) + +class VisionRotary(Rotary): + def __init__(self, config): + super().__init__(config) + + # support [h_pos, w_pos] + def forward(self, position_ids): + # [2, patch_len, 1] + position_ids = position_ids.float().unsqueeze(-1) + idx_theta = position_ids * self.theta + # [patch_len, rotary_dim] + idx_theta = idx_theta.permute(1, 0, 2).reshape(-1, self.rotary_dim) + rotary_pos_emb = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)]) + rotary_pos_emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) + rotary_pos_emb = rotary_pos_emb.unsqueeze(2).unsqueeze(1) + return rotary_pos_emb + +class Decoder(torch.nn.Module): + def __init__(self, decoder, layer_id, config): + super().__init__() + self.cross_decoder = False + ModelMapper.do_map(self, decoder, config.model_map['decoder']) + # mllama has cross_attn + if hasattr(self, 'cross_attn') and self.cross_attn is not None: + self.cross_decoder = True + self.self_attn = Attention(self.cross_attn, layer_id, config) + else: + self.self_attn = Attention(self.self_attn, layer_id, config) + self.hidden_size = config.hidden_size + # chatglm + self.alpha = (2 * config.num_hidden_layers) ** 0.5 if config.model_type == 'chatglm' else 1.0 + + def forward( + self, + hidden_states: torch.Tensor, + rotary_pos_emb: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + cross_attention_states: Optional[torch.Tensor] = None, + cross_attention_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + hidden_states = hidden_states.view(1, -1, self.hidden_size) + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + norm_hidden_states = hidden_states + # Self Attention + hidden_states, present_key_value = self.self_attn( + hidden_states=hidden_states, + rotary_pos_emb=rotary_pos_emb, + attention_mask=attention_mask, + past_key_value=past_key_value, + cross_attention_states=cross_attention_states, + ) + # Fully Connected + if not hasattr(self, 'post_attention_layernorm'): + # phi + feed_forward_hidden_states = self.mlp(norm_hidden_states) + hidden_states = hidden_states + feed_forward_hidden_states + residual + elif self.alpha != 1.0: + # chatglm-6b + hidden_states = norm_hidden_states * self.alpha + hidden_states + mlp_input = self.post_attention_layernorm(hidden_states) + mlp_output = self.mlp(mlp_input) + hidden_states = mlp_input * self.alpha + mlp_output + elif hasattr(self, 'pre_feedforward_layernorm'): + # gemma2 + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.pre_feedforward_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + elif cross_attention_mask is not None and self.cross_decoder: + hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = cross_attention_mask * hidden_states + hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states + else: + # general + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states, present_key_value + +class Lm(torch.nn.Module): + def __init__(self, lm_, final_layernorm_, config): + super().__init__() + self.final_layernorm = final_layernorm_ + self.lm = lm_ + self.hidden_size = config.hidden_size + self.ppl = config.args.ppl + + def forward(self, hidden_states): + if not self.ppl: + # just need last logit for predict next token + hidden_states = hidden_states.view(-1, self.hidden_size)[-1].view(1, 1, self.hidden_size) + hidden_states = self.final_layernorm(hidden_states) + m_logits = self.lm(hidden_states) + return m_logits \ No newline at end of file diff --git a/transformers/llm/export/utils/vision.py b/transformers/llm/export/utils/vision.py new file mode 100644 index 000000000..2e3345cb9 --- /dev/null +++ b/transformers/llm/export/utils/vision.py @@ -0,0 +1,394 @@ +import math +import torch +import numpy as np + +from .transformers import VisionRotary, Decoder + +class Vision(torch.nn.Module): + def __init__(self, visual, base): + super().__init__() + self.model_type = base.model_type + self.visual = visual.eval() + self.embed_ = base.embed + self.tokenizer = base.tokenizer + self.config = base.config + self.hidden_size = base.hidden_size + self.llm_config = base.llm_config + # mllama + self.cross_attention_states = None + self.cross_attention_mask = None + self.init_config() + self.load() + + @staticmethod + def get_vision(model_type): + visual_models = { + 'qwen': QwenVision, + 'qwen2_vl': Qwen2Vision, + 'mllama': MllamaVision + } + if model_type in visual_models: + return visual_models[model_type] + return None + + def init_config(self): + from transformers.image_utils import (OPENAI_CLIP_MEAN, OPENAI_CLIP_STD) + self.llm_config['is_visual'] = True + image_mean = np.array(OPENAI_CLIP_MEAN) * 255.0 + image_norm = 1 / (np.array(OPENAI_CLIP_STD) * 255.0) + self.llm_config['image_mean'] = image_mean.tolist() + self.llm_config['image_norm'] = image_norm.tolist() + + def export(self, onnx_path): + raise NotImplementedError + + def load(self): + raise NotImplementedError + + def str_to_ids(self, prompt): + input_ids = self.tokenizer(prompt, return_tensors="pt")['input_ids'] + return input_ids + + def forward(self, images): + raise NotImplementedError + + def embed(self, input_ids, images = None, videos = None): + raise NotImplementedError + +class QwenVision(Vision): + def __init__(self, visual, base): + self.quant_bit = 16 + super().__init__(visual, base) + + def load(self): + self.image_start_id = self.config.visual['image_start_id'] + self.image_size = self.config.visual['image_size'] + self.llm_config['is_visual'] = True + self.llm_config['image_size'] = self.image_size + self.llm_config['vision_start'] = self.tokenizer.img_start_id + self.llm_config['vision_end'] = self.tokenizer.img_end_id + self.llm_config['image_pad'] = self.tokenizer.img_pad_id + + def export(self, onnx_path): + input_images = torch.randn((1, 3, self.image_size, self.image_size)) + onnx_model = f'{onnx_path}/visual.onnx' + torch.onnx.export(self, (input_images), + onnx_model, + input_names=['input_images'], + output_names=['image_embeds'], + dynamic_axes={ + "input_images": { 0: "size" }, + }, + do_constant_folding=True, + verbose=False, + opset_version=15) + return onnx_model + + def forward(self, images): + return self.visual(images).transpose(1, 0) + + def embed(self, input_ids, images = None, videos = None): + if not torch.any(input_ids == self.image_start_id): + return self.embed_(input_ids) + bos_pos = torch.where(input_ids == self.image_start_id) + eos_pos = torch.where(input_ids == self.image_start_id + 1) + img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) + images = [] + for i, a, b in img_pos: + image = input_ids[i][a + 1 : b - 1].tolist() + image = image[ : image.index(self.image_start_id + 2)] + images.append(bytes(image).decode('utf-8')) + images = self.visual.encode(images).transpose(1, 0) + hidden_states = self.embed_(input_ids) + for idx, (i, a, b) in enumerate(img_pos): + hidden_states[a + 1 : b, i] = images[:, idx] + return hidden_states + +class Qwen2Vision(Vision): + def __init__(self, visual, base): + self.quant_bit = 4 + self.temporal_patch_size = 2 + self.patch_size = 14 + self.merge_size = 2 + self.image_height = 420 + self.image_width = 420 + self.image_embeds = None + super().__init__(visual, base) + + def load(self): + self.vision_start_id = self.config.vision_start_token_id + self.vision_end_id = self.config.vision_end_token_id + self.image_pad_id = self.config.image_token_id + self.llm_config['image_size'] = self.image_height + self.llm_config['vision_start'] = self.vision_start_id + self.llm_config['vision_end'] = self.vision_end_id + self.llm_config['image_pad'] = self.image_pad_id + # load model + config = self.visual.config + self.hidden_size = config.embed_dim + self.num_attention_heads = config.num_heads + self.num_key_value_heads = config.num_heads + self.head_dim = self.hidden_size // self.num_attention_heads + self.rope_theta = 10000.0 + self.rotary_dim = self.head_dim // 2 + self.rotary = VisionRotary(self) + self.model_map = { + 'decoder': { + 'self_attn': 'attn', + 'mlp': 'mlp', + 'input_layernorm': 'norm1', + 'post_attention_layernorm': 'norm2' + }, + 'attention': { + 'qkv_proj': 'qkv', + 'o_proj': 'proj' + } + } + self.patch_embed = self.visual.patch_embed + self.blocks = [] + for block in self.visual.blocks.children(): + layer_id = len(self.blocks) + self.blocks.append(Decoder(block, layer_id, self)) + self.merger = self.visual.merger + + def str_to_ids(self, prompt): + if '' in prompt and '' in prompt: + import re + import requests + from PIL import Image + pattern = r'(.*?)' + parts = re.split(pattern, prompt) + txt_prompt = '' + for part in parts: + if re.match(pattern, part): + img_content = re.search(r'(.*?)', part).group(1) + # find in image_content + match = re.search(r'(.*?)', img_content) + img_content = img_content[:match.start()] + img_content[match.end():] + hw = match.group(1).split(',') + self.image_height, self.image_width = int(hw[0]), int(hw[1]) + if img_content.startswith('http://') or img_content.startswith('https://'): + image_obj = Image.open(requests.get(img_content, stream=True).raw) + img_pad_len = self.img_process(image_obj) + img_pad_str = '<|image_pad|>' * img_pad_len + img_str = f'<|vision_start|>{img_pad_str}<|vision_end|>' + txt_prompt += img_str + else: + txt_prompt += part + else: + txt_prompt = prompt + input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] + return input_ids + + def forward(self, flatten_patches, position_ids, attention_mask): + rotary_pos_emb = self.rotary(position_ids) + hidden_states = self.patch_embed(flatten_patches) + if rotary_pos_emb.dtype != hidden_states.dtype: + rotary_pos_emb = rotary_pos_emb.to(hidden_states.dtype) + for blk in self.blocks: + hidden_states, _ = blk(hidden_states, rotary_pos_emb=rotary_pos_emb, attention_mask=attention_mask) + image_embeds = self.merger(hidden_states) + image_embeds = image_embeds.unsqueeze(1) + return image_embeds + + def images_forward(self, images): + images = [images] * self.temporal_patch_size + patches = torch.concat(images, axis=0) + _, channel, height, width = patches.shape + grid_t = patches.shape[0] // self.temporal_patch_size + grid_h, grid_w = height // self.patch_size, width // self.patch_size + patches = patches.reshape( + grid_t, + self.temporal_patch_size, + channel, + grid_h // self.merge_size, + self.merge_size, + self.patch_size, + grid_w // self.merge_size, + self.merge_size, + self.patch_size, + ) + patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8) + flatten_patches = patches.reshape( + grid_t * grid_h * grid_w, channel * self.temporal_patch_size * self.patch_size * self.patch_size + ) + image_grid_thw = torch.tensor([[grid_t, grid_h, grid_w]]) + pos_ids = [] + for t, h, w in image_grid_thw: + hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) + hpos_ids = hpos_ids.reshape( + h // self.merge_size, + self.merge_size, + w // self.merge_size, + self.merge_size, + ) + hpos_ids = hpos_ids.permute(0, 2, 1, 3) + hpos_ids = hpos_ids.flatten() + + wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) + wpos_ids = wpos_ids.reshape( + h // self.merge_size, + self.merge_size, + w // self.merge_size, + self.merge_size, + ) + wpos_ids = wpos_ids.permute(0, 2, 1, 3) + wpos_ids = wpos_ids.flatten() + pos_ids.append(torch.stack([hpos_ids, wpos_ids])) + position_ids = torch.cat(pos_ids, dim=0) + seq_len = grid_t * grid_h * grid_w + attention_mask = torch.zeros([1, seq_len, seq_len], dtype=torch.float) + return self.forward(flatten_patches, position_ids, attention_mask) + + def smart_resize(self, height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280): + if height < factor or width < factor: + raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") + elif max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = math.floor(height / beta / factor) * factor + w_bar = math.floor(width / beta / factor) * factor + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + def img_process(self, image): + from transformers.image_transforms import ( + convert_to_rgb, + resize, + rescale, + normalize + ) + from transformers.image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + PILImageResampling, + infer_channel_dimension_format, + to_numpy_array + ) + image = convert_to_rgb(image) + image = to_numpy_array(image) + resized_height, resized_width = self.smart_resize(self.image_height, self.image_width) + format = infer_channel_dimension_format(image) + resample = PILImageResampling.BICUBIC + image = resize(image, size=(resized_height, resized_width), resample=resample, input_data_format=format) + image = rescale(image, scale=1 / 255.0, input_data_format=format) + image = normalize(image=image, mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_data_format=format) + image = np.expand_dims(image, [0]) + image = image.transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + self.image_embeds = self.images_forward(image) + return self.image_embeds.shape[0] + + def embed(self, input_ids, images = None, videos = None): + input_embeds = self.embed_(input_ids) + if self.image_embeds is not None: + image_mask = (input_ids == self.image_pad_id).squeeze() + input_embeds[image_mask] = self.image_embeds + return input_embeds + + def export(self, onnx_path): + patch = torch.randn([900, 1176]) + posision_ids = torch.zeros([2, 900], dtype=torch.int32) + attention_mask = torch.zeros([1, 900, 900], dtype=torch.float) + onnx_model = f'{onnx_path}/visual.onnx' + torch.onnx.export(self, (patch, posision_ids, attention_mask), + onnx_model, + input_names=['patches', 'position_ids', 'attention_mask'], + output_names=['image_embeds'], + dynamic_axes={ + "patches": { 0: "size" }, + "position_ids": { 1: "size" }, + "attention_mask": { 1: "size", 2: "size" } + }, + do_constant_folding=True, + verbose=False, + opset_version=15) + return onnx_model + +class MllamaVision(Vision): + def __init__(self, visual, base): + super().__init__(visual, base) + self.multi_modal_projector = base.multi_modal_projector + self.image_objs = [] + + def load(self): + self.llm_config['is_visual'] = True + self.llm_config['image_size'] = self.config.vision_config.image_size + self.image_size = self.config.vision_config.image_size + + def str_to_ids(self, prompt): + if '' in prompt and '' in prompt: + import re + import requests + from PIL import Image + pattern = r'(.*?)' + parts = re.split(pattern, prompt) + txt_prompt = '' + for part in parts: + if re.match(pattern, part): + img_content = re.search(r'(.*?)', part).group(1) + if img_content.startswith('http://') or img_content.startswith('https://'): + self.image_objs.append(Image.open(requests.get(img_content, stream=True).raw)) + txt_prompt += '<|image|>' + else: + txt_prompt += part + else: + txt_prompt = prompt + input_ids = self.tokenizer(txt_prompt, return_tensors="pt")['input_ids'] + # image process + for img in self.image_objs: + self.img_process(img) + return input_ids + + def img_process(self, image): + self.image_size = 560 + resized_height = self.image_size + resized_width = self.image_size + from transformers.image_transforms import ( + convert_to_rgb, + resize, + rescale, + normalize + ) + from transformers.image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + PILImageResampling, + infer_channel_dimension_format, + to_numpy_array + ) + image = convert_to_rgb(image) + image = to_numpy_array(image) + format = infer_channel_dimension_format(image) + resample = PILImageResampling.BICUBIC + image = resize(image, size=(resized_height, resized_width), resample=resample, input_data_format=format) + image = rescale(image, scale=1 / 255.0, input_data_format=format) + image = normalize(image=image, mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_data_format=format) + image = image.transpose(2, 0, 1) + image = np.expand_dims(image, [0, 1, 2]) + pad_val = np.zeros_like(image) + image = np.concatenate([image, pad_val, pad_val, pad_val], axis=2) + image = torch.from_numpy(image) + self.cross_attention_states = self.forward(image) + + def forward(self, images): + aspect_ratio_ids = torch.tensor([[1]]) + aspect_ratio_mask = torch.tensor([[[1, 0, 0, 0]]]) + vision_outputs = self.visual(images, aspect_ratio_ids, aspect_ratio_mask) + cross_attention_states = vision_outputs[0] + cross_attention_states = cross_attention_states.type(self.multi_modal_projector.weight.dtype) + cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape( + -1, cross_attention_states.shape[-2], self.hidden_size) + return cross_attention_states + + def embed(self, input_ids, images = None, videos = None): + txt_embeds = self.embed_(input_ids) + return txt_embeds \ No newline at end of file