From 13346fdf1824b3c26da85880739dde7240b71cbd Mon Sep 17 00:00:00 2001 From: Changming Sun Date: Tue, 19 Nov 2024 14:13:33 -0800 Subject: [PATCH] Cleanup code (#22827) ### Description 1. Delete TVM EP because it is out of maintain 2. Delete ortmodule related docker files and scripts. --- ThirdPartyNotices.txt | 255 ---- cgmanifests/cgmanifest.json | 1082 ++++++++--------- cmake/CMakeLists.txt | 54 - cmake/external/tvm.cmake | 24 - cmake/onnxruntime.cmake | 2 - cmake/onnxruntime_codegen_tvm.cmake | 25 - cmake/onnxruntime_csharp.cmake | 4 - cmake/onnxruntime_providers.cmake | 7 - cmake/onnxruntime_providers_cuda.cmake | 3 +- cmake/onnxruntime_providers_tvm.cmake | 64 - cmake/onnxruntime_python.cmake | 33 - cmake/onnxruntime_unittests.cmake | 35 - .../NativeMethods.shared.cs | 3 - .../SessionOptions.shared.cs | 35 - .../InferenceTest.cs | 4 - docs/TVM_EP.md | 319 ----- .../notebooks/onnxruntime-tvm-tutorial.ipynb | 657 ---------- onnxruntime/core/codegen/common/common.cc | 284 ----- onnxruntime/core/codegen/common/common.h | 153 --- onnxruntime/core/codegen/common/creator.h | 76 -- onnxruntime/core/codegen/common/dispatcher.h | 76 -- onnxruntime/core/codegen/common/dump_array.h | 62 - onnxruntime/core/codegen/common/handle.h | 22 - onnxruntime/core/codegen/common/op_macro.h | 101 -- onnxruntime/core/codegen/common/profile.h | 37 - onnxruntime/core/codegen/common/registry.h | 72 -- onnxruntime/core/codegen/common/settings.cc | 78 -- onnxruntime/core/codegen/common/settings.h | 40 - onnxruntime/core/codegen/common/target_info.h | 33 - onnxruntime/core/codegen/common/utils.cc | 99 -- onnxruntime/core/codegen/common/utils.h | 45 - onnxruntime/core/codegen/mti/common.h | 16 - .../core/codegen/mti/debug/tvm_print.cc | 83 -- .../core/codegen/mti/debug/tvm_print.h | 19 - .../core/codegen/mti/math/binary_ops.cc | 70 -- .../core/codegen/mti/math/binary_ops.h | 42 - onnxruntime/core/codegen/mti/math/gemm.cc | 30 - onnxruntime/core/codegen/mti/math/gemm.h | 16 - .../core/codegen/mti/math/logsoftmax.cc | 18 - .../core/codegen/mti/math/logsoftmax.h | 11 - .../core/codegen/mti/math/matmul_ops.cc | 161 --- .../core/codegen/mti/math/matmul_ops.h | 23 - .../core/codegen/mti/math/reduce_ops.cc | 90 -- .../core/codegen/mti/math/reduce_ops.h | 72 -- onnxruntime/core/codegen/mti/math/softmax.cc | 18 - onnxruntime/core/codegen/mti/math/softmax.h | 11 - .../core/codegen/mti/math/unary_ops.cc | 155 --- onnxruntime/core/codegen/mti/math/unary_ops.h | 36 - onnxruntime/core/codegen/mti/mti_tvm_utils.cc | 203 ---- onnxruntime/core/codegen/mti/mti_tvm_utils.h | 71 -- onnxruntime/core/codegen/mti/nn/conv_ops.cc | 193 --- onnxruntime/core/codegen/mti/nn/conv_ops.h | 39 - onnxruntime/core/codegen/mti/nn/lstm.cc | 140 --- onnxruntime/core/codegen/mti/nn/lstm.h | 35 - onnxruntime/core/codegen/mti/nn/pool_ops.cc | 63 - onnxruntime/core/codegen/mti/nn/pool_ops.h | 36 - .../core/codegen/mti/tensor/cast_ops.cc | 37 - .../core/codegen/mti/tensor/cast_ops.h | 15 - .../core/codegen/mti/tensor/concat_ops.cc | 83 -- .../core/codegen/mti/tensor/concat_ops.h | 15 - onnxruntime/core/codegen/mti/tensor/crop.cc | 58 - onnxruntime/core/codegen/mti/tensor/crop.h | 17 - onnxruntime/core/codegen/mti/tensor/expand.cc | 30 - onnxruntime/core/codegen/mti/tensor/expand.h | 14 - onnxruntime/core/codegen/mti/tensor/gather.cc | 55 - onnxruntime/core/codegen/mti/tensor/gather.h | 17 - .../codegen/mti/tensor/gather_elements.cc | 45 - .../core/codegen/mti/tensor/gather_elements.h | 17 - .../core/codegen/mti/tensor/pad_ops.cc | 121 -- onnxruntime/core/codegen/mti/tensor/pad_ops.h | 34 - .../core/codegen/mti/tensor/reshape_ops.cc | 48 - .../core/codegen/mti/tensor/reshape_ops.h | 16 - .../core/codegen/mti/tensor/shape_op.cc | 25 - .../core/codegen/mti/tensor/shape_op.h | 14 - onnxruntime/core/codegen/mti/tensor/slice.cc | 91 -- onnxruntime/core/codegen/mti/tensor/slice.h | 19 - onnxruntime/core/codegen/mti/tensor/split.cc | 72 -- onnxruntime/core/codegen/mti/tensor/split.h | 25 - onnxruntime/core/codegen/mti/tensor/tile.cc | 40 - onnxruntime/core/codegen/mti/tensor/tile.h | 16 - .../core/codegen/mti/tensor/transpose.cc | 16 - .../core/codegen/mti/tensor/transpose.h | 16 - onnxruntime/core/codegen/mti/tensor/where.cc | 36 - onnxruntime/core/codegen/mti/tensor/where.h | 17 - .../codegen/passes/op_ir_creator/all_ops.h | 47 - .../passes/op_ir_creator/math/binary_ops.cc | 46 - .../codegen/passes/op_ir_creator/math/clip.cc | 48 - .../codegen/passes/op_ir_creator/math/gemm.cc | 39 - .../passes/op_ir_creator/math/logsoftmax.cc | 32 - .../passes/op_ir_creator/math/matmul.cc | 23 - .../math/quantize/matmul_integer.cc | 37 - .../passes/op_ir_creator/math/reduce_ops.cc | 111 -- .../passes/op_ir_creator/math/softmax.cc | 32 - .../passes/op_ir_creator/math/unary_funcs.h | 51 - .../passes/op_ir_creator/math/unary_ops.cc | 93 -- .../passes/op_ir_creator/math/variadic_ops.cc | 36 - .../codegen/passes/op_ir_creator/nn/conv.cc | 131 -- .../codegen/passes/op_ir_creator/nn/lstm.cc | 64 - .../passes/op_ir_creator/nn/pool_ops.cc | 51 - .../passes/op_ir_creator/tensor/cast.cc | 40 - .../passes/op_ir_creator/tensor/concat.cc | 30 - .../passes/op_ir_creator/tensor/crop.cc | 46 - .../passes/op_ir_creator/tensor/expand.cc | 26 - .../passes/op_ir_creator/tensor/gather.cc | 30 - .../op_ir_creator/tensor/gather_elements.cc | 32 - .../passes/op_ir_creator/tensor/pad.cc | 49 - .../op_ir_creator/tensor/reshape_ops.cc | 99 -- .../passes/op_ir_creator/tensor/shape_op.cc | 26 - .../passes/op_ir_creator/tensor/slice.cc | 71 -- .../passes/op_ir_creator/tensor/split.cc | 66 - .../passes/op_ir_creator/tensor/transpose.cc | 48 - .../passes/op_ir_creator/tensor/where.cc | 28 - .../passes/op_ir_creator/tvm_ir_builder.cc | 125 -- .../passes/op_ir_creator/tvm_ir_builder.h | 64 - .../passes/op_ir_creator/tvm_op_creator.cc | 37 - .../passes/op_ir_creator/tvm_op_creator.h | 84 -- .../codegen/passes/scheduler/all_schedules.h | 20 - .../passes/scheduler/ort_type_schedule.cc | 22 - .../passes/scheduler/schedule_utils.cc | 178 --- .../codegen/passes/scheduler/schedule_utils.h | 65 - .../passes/scheduler/tvm_rule_schedule.cc | 41 - .../passes/scheduler/tvm_schedule_builder.cc | 104 -- .../passes/scheduler/tvm_schedule_builder.h | 46 - .../codegen/passes/scheduler/tvm_scheduler.cc | 79 -- .../codegen/passes/scheduler/tvm_scheduler.h | 128 -- .../codegen/passes/utils/codegen_context.cc | 27 - .../codegen/passes/utils/codegen_context.h | 44 - .../codegen/passes/utils/ort_tvm_utils.cc | 194 --- .../core/codegen/passes/utils/ort_tvm_utils.h | 31 - .../codegen/passes/weight_layout/tiling_2d.cc | 105 -- .../codegen/passes/weight_layout/tiling_2d.h | 43 - .../passes/weight_layout/transpose_2d.cc | 64 - .../passes/weight_layout/transpose_2d.h | 33 - .../weight_layout/vertical_stripes_2d.cc | 77 -- .../weight_layout/vertical_stripes_2d.h | 40 - .../passes/weight_layout/weight_layout.cc | 91 -- .../passes/weight_layout/weight_layout.h | 68 -- onnxruntime/core/framework/utils.cc | 1 - .../core/platform/windows/stacktrace.cc | 1 - .../core/providers/get_execution_providers.cc | 8 - .../providers/provider_factory_creators.h | 4 - .../core/providers/tvm/custom_logging.cc | 52 - .../core/providers/tvm/hash_alg/hasher.cc | 30 - .../core/providers/tvm/hash_alg/hasher.h | 32 - .../providers/tvm/hash_alg/hasher_impl.cc | 39 - .../core/providers/tvm/hash_alg/hasher_impl.h | 42 - onnxruntime/core/providers/tvm/symbols.txt | 1 - .../core/providers/tvm/tvm_allocator.cc | 29 - .../core/providers/tvm/tvm_allocator.h | 45 - onnxruntime/core/providers/tvm/tvm_api.cc | 303 ----- onnxruntime/core/providers/tvm/tvm_api.h | 38 - onnxruntime/core/providers/tvm/tvm_common.h | 22 - .../core/providers/tvm/tvm_compiler.cc | 48 - onnxruntime/core/providers/tvm/tvm_compiler.h | 63 - onnxruntime/core/providers/tvm/tvm_defaults.h | 35 - .../core/providers/tvm/tvm_ep_options.cc | 273 ----- .../core/providers/tvm/tvm_ep_options.h | 76 -- .../providers/tvm/tvm_execution_provider.cc | 304 ----- .../providers/tvm/tvm_execution_provider.h | 71 -- .../providers/tvm/tvm_provider_factory.cc | 53 - .../tvm/tvm_provider_factory_creator.h | 19 - onnxruntime/core/providers/tvm/tvm_runner.cc | 26 - onnxruntime/core/providers/tvm/tvm_runner.h | 34 - .../core/providers/tvm/tvm_runner_impl.cc | 175 --- .../core/providers/tvm/tvm_runner_impl.h | 126 -- .../tvm/tvm_so_execution_provider.cc | 284 ----- .../providers/tvm/tvm_so_execution_provider.h | 72 -- onnxruntime/core/providers/tvm/tvm_utils.cc | 31 - onnxruntime/core/providers/tvm/tvm_utils.h | 70 -- .../core/providers/tvm/xpu_data_transfer.cc | 84 -- .../core/providers/tvm/xpu_data_transfer.h | 38 - .../core/session/provider_registration.cc | 9 - .../python/onnxruntime_pybind_state.cc | 10 - .../python/onnxruntime_pybind_state_common.h | 15 +- onnxruntime/python/providers/tvm/__init__.py | 10 - .../providers/tvm/extend_python_file.py | 54 - onnxruntime/python/providers/tvm/ort.py | 140 --- onnxruntime/test/framework/function_test.cc | 6 - .../test/platform/windows/stacktrace_test.cc | 1 - .../python/onnxruntime_test_python_tvm.py | 242 ---- onnxruntime/test/util/default_providers.cc | 8 - .../test/util/include/default_providers.h | 1 - onnxruntime/test/util/include/providers.h | 3 - tools/ci_build/build.py | 62 +- ...orttraining-linux-gpu-test-ci-pipeline.yml | 41 - ...py-packaging-training-cuda-stage-steps.yml | 209 ---- .../linux/docker/scripts/install_os_deps.sh | 1 - .../docker/scripts/install_python_deps.sh | 16 - .../stage1/requirements_rocm/requirements.txt | 2 - .../requirements.txt | 8 - .../requirements.txt | 7 - .../requirements_torch_cpu/requirements.txt | 3 - .../requirements.txt | 5 - .../stage1/torch_eager_cpu/requirements.txt | 11 - .../ortmodule/stage2/requirements.txt | 15 - .../ci_build/github/linux/run_dockerbuild.sh | 26 - .../pai/pai_huggingface_bert_large_test.sh | 43 - tools/ci_build/set-trigger-rules.py | 5 - .../nuget/generate_nuspec_for_native_nuget.py | 51 +- tools/scripts/python_test.sh | 8 +- 200 files changed, 512 insertions(+), 12715 deletions(-) delete mode 100644 cmake/external/tvm.cmake 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We believe that it is important to create an inclusive community where everyone can use, -contribute to, and influence the direction of the project. We actively invite contributors who have earned the merit to be part of the development community. - -See the [community structure document](http://docs.tvm.ai/contribute/community.html) for the explanation of community structure and contribution guidelines. - -## Committers -- [Tianqi Chen](https://github.com/tqchen) (PMC) -- [Thierry Moreau](http://homes.cs.washington.edu/~moreau/) -- [Ziheng Jiang](https://github.com/ZihengJiang) -- [Haichen Shen](http://homes.cs.washington.edu/~haichen/) -- [Yizhi Liu](https://github.com/yzhliu) - -## Code Owners -- [Aditya Atluri](https://github.com/adityaatluri) ROCM -- [Leyuan Wang](https://github.com/Laurawly) TOPI -- [Yuwei Hu](https://github.com/Huyuwei) TOPI -- [Zhixun Tan](https://github.com/phisiart) OpenGL/WebGL backend -- [Nick Hynes](https://github.com/nhynes) SGX and secured computing -- [Lianmin Zheng](https://github.com/merrymercy) AutoTVM - -## Reviewers -- [Zhi Chen](https://github.com/zhiics) -- [Xiaoqiang Dan](https://github.com/xqdan) -- [Liangfu Chen](https://github.com/liangfu) -- [Masahiro Masuda](https://github.com/masahi) -- [Kazutaka Morita](https://github.com/kazum) -- [Tatsuya Nishiyama](https://github.com/nishi-t) -- [Pariksheet Pinjari](https://github.com/PariksheetPinjari909) -- [Jared Roesch](https://github.com/jroesch) -- [Siva](https://github.com/srkreddy1238) -- [Siju Samuel](https://github.com/siju-samuel) -- [Alex Weaver](https://github.com/alex-weaver) -- [Yao Wang](https://github.com/kevinthesun) -- [Jian Weng](https://github.com/were) -- [Eddie Yan](https://github.com/eqy) -- [Joshua Z. Zhang](https://github.com/zhreshold) - -## List of Contributors -- [Full List of Contributors](https://github.com/dmlc/tvm/graphs/contributors) - - To contributors: please add your name to the list. -- [Qiao Zhang](https://github.com/zhangqiaorjc) -- [Haolong Zhang](https://github.com/haolongzhangm) -- [Cody Hao Yu](https://github.com/comaniac) -- [Chris Nuernberger](https://github.com/cnuernber) - -_____ - FreeBSD: getopt.c file Copyright (c) 1987, 1993, 1994 diff --git a/cgmanifests/cgmanifest.json b/cgmanifests/cgmanifest.json index 1432193ac9080..46349f43923e2 100644 --- a/cgmanifests/cgmanifest.json +++ b/cgmanifests/cgmanifest.json @@ -1,578 +1,508 @@ { - "$schema": "https://json.schemastore.org/component-detection-manifest.json", - "Registrations": [ - { - "component": { - "type": "git", - "git": { - "commitHash": "215105818dfde3174fe799600bb0f3cae233d0bf", - "repositoryUrl": "https://github.com/abseil/abseil-cpp.git" - } - } - }, - { - "component": { - "Type": "maven", - "maven": { - 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Implementation logic for anti-aliasing copied by Resize CPU kernel." + } + }, + { + "component": { + "type": "git", + "git": { + "commitHash": "e7248b26a1ed53fa030c5c459f7ea095dfd276ac", + "repositoryUrl": "https://gitlab.com/libeigen/eigen.git" + } + } + } + ], + "Version": 1 } diff --git a/cmake/CMakeLists.txt b/cmake/CMakeLists.txt index 31ebf58b03152..af341aaead2d5 100644 --- a/cmake/CMakeLists.txt +++ b/cmake/CMakeLists.txt @@ -102,7 +102,6 @@ option(onnxruntime_BUILD_CSHARP "Build C# library" OFF) option(onnxruntime_BUILD_OBJC "Build Objective-C library" OFF) option(onnxruntime_USE_PREINSTALLED_EIGEN "Use pre-installed EIGEN. Need to provide eigen_SOURCE_PATH if turn this on." OFF) option(onnxruntime_BUILD_BENCHMARKS "Build ONNXRuntime micro-benchmarks" OFF) -option(onnxruntime_USE_LLVM "Build TVM with LLVM" OFF) option(onnxruntime_USE_VSINPU "Build with VSINPU support" OFF) cmake_dependent_option(onnxruntime_USE_FLASH_ATTENTION "Build flash attention kernel for scaled dot product attention" ON "onnxruntime_USE_CUDA" OFF) @@ -145,10 +144,6 @@ option(onnxruntime_USE_TELEMETRY "Build with Telemetry" OFF) cmake_dependent_option(onnxruntime_USE_MIMALLOC "Override new/delete and arena allocator with mimalloc" OFF "WIN32;NOT onnxruntime_USE_CUDA;NOT onnxruntime_USE_OPENVINO" OFF) option(onnxruntime_USE_CANN "Build with CANN support" OFF) option(onnxruntime_USE_ROCM "Build with AMD GPU support" OFF) -option(onnxruntime_USE_TVM "Build with TVM support" OFF) -option(onnxruntime_TVM_CUDA_RUNTIME "Build TVM with CUDA support" OFF) -option(onnxruntime_TVM_USE_LLVM "Build TVM with LLVM. Set customized path to llvm-config.exe here if need" OFF) -option(onnxruntime_TVM_USE_HASH "Build ipp-crypto library for support hash algorithm. It is defined for TVM only") option(onnxruntime_USE_XNNPACK "Build with XNNPACK support. Provides an alternative math library on ARM, WebAssembly and x86." OFF) option(onnxruntime_USE_WEBNN "Build with WebNN support. Enable hardware acceleration in web browsers." OFF) option(onnxruntime_USE_WEBGPU "Build with WebGPU support. Enable WebGPU via C/C++ interface." OFF) @@ -906,11 +901,6 @@ if (onnxruntime_USE_SNPE) list(APPEND ONNXRUNTIME_PROVIDER_NAMES snpe) list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_SNPE=1) endif() -if (onnxruntime_USE_TVM) - list(APPEND ORT_PROVIDER_FLAGS -DUSE_TVM=1) - list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_TVM=1) - list(APPEND ONNXRUNTIME_PROVIDER_NAMES tvm) -endif() if (onnxruntime_USE_WINML) list(APPEND ORT_PROVIDER_FLAGS -DUSE_WINML=1) list(APPEND ORT_PROVIDER_CMAKE_FLAGS -Donnxruntime_USE_WINML=1) @@ -1313,50 +1303,6 @@ if (onnxruntime_USE_DNNL) add_compile_definitions(DNNL_OPENMP) endif() -# TVM EP -if (onnxruntime_USE_TVM) - if (NOT TARGET tvm) - message(STATUS "Include TVM(*).") - include(tvm) - endif() - - # ipp-crypto - if (onnxruntime_TVM_USE_HASH) - message(STATUS "Include ipp-crypto(*).") - include(ipp-crypto) - endif() - - # TVM - if (onnxruntime_TVM_USE_LLVM) - set(USE_LLVM "${onnxruntime_TVM_USE_LLVM}" CACHE STRING "Path to LLVM for correct TVM build") - elseif(onnxruntime_USE_LLVM) - set(USE_LLVM ON CACHE BOOL "Only defined for TVM") - endif() - - if (onnxruntime_TVM_CUDA_RUNTIME) - set(USE_CUDA ON CACHE BOOL "Only defined for TVM" FORCE) - endif() - - # TODO(vvchernov): customized tvm logger is hidden due to the issue on TVM side (https://github.com/apache/tvm/issues/10139) - # add_compile_definitions(TVM_LOG_CUSTOMIZE=1) - # add_library(tvm_custom_logger STATIC ${ONNXRUNTIME_ROOT}/core/providers/tvm/custom_logging.cc) - - set(USE_OPENMP gnu CACHE STRING "Only defined for TVM") - add_subdirectory(${tvm_SOURCE_DIR} ${tvm_BINARY_DIR} EXCLUDE_FROM_ALL) - - set_target_properties(tvm PROPERTIES FOLDER ${tvm_SOURCE_DIR}) - # target_link_libraries(tvm PUBLIC tvm_custom_logger) - - set(TVM_INCLUDES ${tvm_SOURCE_DIR}/include - ${tvm_SOURCE_DIR}/3rdparty/dmlc-core/include - ${tvm_SOURCE_DIR}/3rdparty/dlpack/include - $) - - set(onnxruntime_tvm_libs onnxruntime_providers_tvm) - list(APPEND onnxruntime_EXTERNAL_LIBRARIES tvm) - list(APPEND onnxruntime_EXTERNAL_DEPENDENCIES tvm) -endif() - # onnxruntime-extensions if (onnxruntime_USE_EXTENSIONS) include(extensions) diff --git a/cmake/external/tvm.cmake b/cmake/external/tvm.cmake deleted file mode 100644 index 93049c8b85853..0000000000000 --- a/cmake/external/tvm.cmake +++ /dev/null @@ -1,24 +0,0 @@ -if (onnxruntime_USE_TVM) - message(STATUS "onnxruntime_USE_TVM: Fetch tvm for TVM EP") - - FetchContent_Declare( - tvm - GIT_REPOSITORY https://github.com/apache/tvm.git - GIT_TAG 2379917985919ed3918dc12cad47f469f245be7a - ) - - FetchContent_GetProperties(tvm) - if(NOT tvm_POPULATED) - FetchContent_Populate(tvm) - if (WIN32) - execute_process( - COMMAND ${CMAKE_COMMAND} -E create_symlink ${tvm_BINARY_DIR}/${CMAKE_BUILD_TYPE} ${tvm_SOURCE_DIR}/build - ) - else() - file(CREATE_LINK ${tvm_BINARY_DIR} ${tvm_SOURCE_DIR}/build SYMBOLIC) - endif() - endif() - - set(tvm_INCLUDE_DIRS ${tvm_SOURCE_DIR}/include) - -endif() diff --git a/cmake/onnxruntime.cmake b/cmake/onnxruntime.cmake index 003f7ad18286b..732c0511d400f 100644 --- a/cmake/onnxruntime.cmake +++ b/cmake/onnxruntime.cmake @@ -210,7 +210,6 @@ set(onnxruntime_INTERNAL_LIBRARIES ${PROVIDERS_NNAPI} ${PROVIDERS_QNN} ${PROVIDERS_SNPE} - ${PROVIDERS_TVM} ${PROVIDERS_RKNPU} ${PROVIDERS_VSINPU} ${PROVIDERS_XNNPACK} @@ -221,7 +220,6 @@ set(onnxruntime_INTERNAL_LIBRARIES ${onnxruntime_winml} onnxruntime_optimizer onnxruntime_providers - ${onnxruntime_tvm_libs} onnxruntime_lora onnxruntime_framework onnxruntime_graph diff --git a/cmake/onnxruntime_codegen_tvm.cmake b/cmake/onnxruntime_codegen_tvm.cmake deleted file mode 100644 index 7b50d8f8603ae..0000000000000 --- a/cmake/onnxruntime_codegen_tvm.cmake +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - -file(GLOB_RECURSE onnxruntime_codegen_common_srcs - "${ONNXRUNTIME_ROOT}/core/codegen/common/*.h" - "${ONNXRUNTIME_ROOT}/core/codegen/common/*.cc" -) - -file(GLOB_RECURSE onnxruntime_codegen_tvm_srcs CONFIGURE_DEPENDS - "${ONNXRUNTIME_ROOT}/core/codegen/mti/*.h" - "${ONNXRUNTIME_ROOT}/core/codegen/mti/*.cc" - "${ONNXRUNTIME_ROOT}/core/codegen/passes/*.h" - "${ONNXRUNTIME_ROOT}/core/codegen/passes/*.cc" -) - -source_group(TREE ${ONNXRUNTIME_ROOT}/core FILES ${onnxruntime_codegen_common_srcs} ${onnxruntime_codegen_tvm_srcs}) - -#onnxruntime_codegen_tvm depends on onnxruntime framework -onnxruntime_add_static_library(onnxruntime_codegen_tvm ${onnxruntime_codegen_common_srcs} ${onnxruntime_codegen_tvm_srcs}) -set_target_properties(onnxruntime_codegen_tvm PROPERTIES FOLDER "ONNXRuntime") -target_include_directories(onnxruntime_codegen_tvm PRIVATE ${ONNXRUNTIME_ROOT} ${TVM_INCLUDES} ${MKLML_INCLUDE_DIR} ${eigen_INCLUDE_DIRS}) -onnxruntime_add_include_to_target(onnxruntime_codegen_tvm onnxruntime_common onnxruntime_framework onnx onnx_proto ${PROTOBUF_LIB} flatbuffers::flatbuffers safeint_interface Boost::mp11) -target_compile_options(onnxruntime_codegen_tvm PRIVATE ${DISABLED_WARNINGS_FOR_TVM}) -# need onnx to build to create headers that this project includes -add_dependencies(onnxruntime_codegen_tvm ${onnxruntime_EXTERNAL_DEPENDENCIES}) diff --git a/cmake/onnxruntime_csharp.cmake b/cmake/onnxruntime_csharp.cmake index 22c993d07f7f9..39533429e181c 100644 --- a/cmake/onnxruntime_csharp.cmake +++ b/cmake/onnxruntime_csharp.cmake @@ -30,10 +30,6 @@ if (onnxruntime_USE_NNAPI_BUILTIN) STRING(APPEND CSHARP_PREPROCESSOR_DEFINES "USE_NNAPI;") endif() -if (onnxruntime_USE_TVM) - STRING(APPEND CSHARP_PREPROCESSOR_DEFINES "USE_TVM,") -endif() - if (onnxruntime_USE_OPENVINO) STRING(APPEND CSHARP_PREPROCESSOR_DEFINES "USE_OPENVINO;") endif() diff --git a/cmake/onnxruntime_providers.cmake b/cmake/onnxruntime_providers.cmake index 9666877cdc206..582491de9503d 100644 --- a/cmake/onnxruntime_providers.cmake +++ b/cmake/onnxruntime_providers.cmake @@ -101,9 +101,6 @@ endif() if(onnxruntime_USE_ROCM) set(PROVIDERS_ROCM onnxruntime_providers_rocm) endif() -if (onnxruntime_USE_TVM) - set(PROVIDERS_TVM onnxruntime_providers_tvm) -endif() if (onnxruntime_USE_XNNPACK) set(PROVIDERS_XNNPACK onnxruntime_providers_xnnpack) endif() @@ -194,10 +191,6 @@ if (onnxruntime_USE_ROCM) include(onnxruntime_providers_rocm.cmake) endif() -if (onnxruntime_USE_TVM) - include(onnxruntime_providers_tvm.cmake) -endif() - if (onnxruntime_USE_VSINPU) include(onnxruntime_providers_vsinpu.cmake) endif() diff --git a/cmake/onnxruntime_providers_cuda.cmake b/cmake/onnxruntime_providers_cuda.cmake index 39ad530146b33..4f86717026118 100644 --- a/cmake/onnxruntime_providers_cuda.cmake +++ b/cmake/onnxruntime_providers_cuda.cmake @@ -224,8 +224,7 @@ include(cutlass) target_include_directories(${target} PRIVATE ${cutlass_SOURCE_DIR}/include ${cutlass_SOURCE_DIR}/examples ${cutlass_SOURCE_DIR}/tools/util/include) - target_include_directories(${target} PRIVATE ${ONNXRUNTIME_ROOT} ${CMAKE_CURRENT_BINARY_DIR} ${eigen_INCLUDE_DIRS} ${TVM_INCLUDES} - PUBLIC ${CUDAToolkit_INCLUDE_DIRS}) + target_include_directories(${target} PRIVATE ${ONNXRUNTIME_ROOT} ${CMAKE_CURRENT_BINARY_DIR} ${eigen_INCLUDE_DIRS} PUBLIC ${CUDAToolkit_INCLUDE_DIRS}) # ${CMAKE_CURRENT_BINARY_DIR} is so that #include "onnxruntime_config.h" inside tensor_shape.h is found set_target_properties(${target} PROPERTIES LINKER_LANGUAGE CUDA) set_target_properties(${target} PROPERTIES FOLDER "ONNXRuntime") diff --git a/cmake/onnxruntime_providers_tvm.cmake b/cmake/onnxruntime_providers_tvm.cmake deleted file mode 100644 index 8fd50c70dd5d7..0000000000000 --- a/cmake/onnxruntime_providers_tvm.cmake +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. - - add_definitions(-DUSE_TVM=1) - if (onnxruntime_TVM_USE_HASH) - add_definitions(-DUSE_TVM_HASH=1) - endif() - - if (onnxruntime_TVM_USE_HASH) - file (GLOB_RECURSE onnxruntime_providers_tvm_cc_srcs CONFIGURE_DEPENDS - "${ONNXRUNTIME_ROOT}/core/providers/tvm/*.h" - "${ONNXRUNTIME_ROOT}/core/providers/tvm/*.cc" - ) - else() - file (GLOB onnxruntime_providers_tvm_cc_srcs CONFIGURE_DEPENDS - "${ONNXRUNTIME_ROOT}/core/providers/tvm/*.h" - "${ONNXRUNTIME_ROOT}/core/providers/tvm/*.cc" - ) - endif() - - source_group(TREE ${ONNXRUNTIME_ROOT}/core FILES ${onnxruntime_providers_tvm_cc_srcs}) - onnxruntime_add_static_library(onnxruntime_providers_tvm ${onnxruntime_providers_tvm_cc_srcs}) - - if ( CMAKE_COMPILER_IS_GNUCC ) - target_compile_options(onnxruntime_providers_tvm PRIVATE -Wno-unused-parameter -Wno-missing-field-initializers) - endif() - - target_include_directories(onnxruntime_providers_tvm PRIVATE - ${TVM_INCLUDES} - ${PYTHON_INCLUDE_DIRS}) - onnxruntime_add_include_to_target(onnxruntime_providers_tvm onnxruntime_common onnxruntime_framework onnx onnx_proto ${PROTOBUF_LIB} flatbuffers::flatbuffers Boost::mp11 safeint_interface) - - add_dependencies(onnxruntime_providers_tvm ${onnxruntime_EXTERNAL_DEPENDENCIES}) - - if (onnxruntime_TVM_USE_HASH) - add_dependencies(onnxruntime_providers_tvm ippcp_s) - target_include_directories(onnxruntime_providers_tvm PRIVATE ${IPP_CRYPTO_INCLUDE_DIR}) - target_link_libraries(onnxruntime_providers_tvm PRIVATE ippcp_s) - endif() - - set_target_properties(onnxruntime_providers_tvm PROPERTIES FOLDER "ONNXRuntime") - set_target_properties(onnxruntime_providers_tvm PROPERTIES LINKER_LANGUAGE CXX) - - if (WIN32 AND MSVC) - # wd4100: identifier' : unreferenced formal parameter - # wd4127: conditional expression is constant - # wd4244: conversion from 'int' to 'char', possible loss of data - # TODO: 4244 should not be disabled - target_compile_options(onnxruntime_providers_tvm PRIVATE "/wd4100" "/wd4127" "/wd4244") - else() - target_compile_options(onnxruntime_providers_tvm PRIVATE "-Wno-error=type-limits") - endif() - target_compile_definitions(onnxruntime_providers_tvm PUBLIC DMLC_USE_LOGGING_LIBRARY=) - - install(FILES ${PROJECT_SOURCE_DIR}/../include/onnxruntime/core/providers/tvm/tvm_provider_factory.h - DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime/) - - if (NOT onnxruntime_BUILD_SHARED_LIB) - install(TARGETS onnxruntime_providers_tvm - ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR} - LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR} - RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR} - FRAMEWORK DESTINATION ${CMAKE_INSTALL_BINDIR}) - endif() \ No newline at end of file diff --git a/cmake/onnxruntime_python.cmake b/cmake/onnxruntime_python.cmake index 7239b245a7245..d2c022e4e0269 100644 --- a/cmake/onnxruntime_python.cmake +++ b/cmake/onnxruntime_python.cmake @@ -169,7 +169,6 @@ endif() target_link_libraries(onnxruntime_pybind11_state PRIVATE onnxruntime_session ${onnxruntime_libs} - ${PROVIDERS_TVM} ${PROVIDERS_NNAPI} ${PROVIDERS_XNNPACK} ${PROVIDERS_COREML} @@ -184,7 +183,6 @@ target_link_libraries(onnxruntime_pybind11_state PRIVATE onnxruntime_optimizer onnxruntime_providers onnxruntime_util - ${onnxruntime_tvm_libs} onnxruntime_lora onnxruntime_framework onnxruntime_util @@ -965,37 +963,6 @@ if (onnxruntime_USE_ROCM) ) endif() -if (onnxruntime_USE_TVM) - file(GLOB onnxruntime_python_providers_tvm_srcs CONFIGURE_DEPENDS - "${ONNXRUNTIME_ROOT}/python/providers/tvm/*.py" - ) - add_custom_command( - TARGET onnxruntime_pybind11_state POST_BUILD - COMMAND ${CMAKE_COMMAND} -E make_directory $/onnxruntime/providers - COMMAND ${CMAKE_COMMAND} -E make_directory $/onnxruntime/providers/tvm - COMMAND ${CMAKE_COMMAND} -E copy - ${onnxruntime_python_providers_tvm_srcs} - $/onnxruntime/providers/tvm - COMMAND ${CMAKE_COMMAND} -E copy - $ - $/onnxruntime/capi/ - ) - - add_custom_command( - TARGET onnxruntime_pybind11_state POST_BUILD - WORKING_DIRECTORY ${tvm_SOURCE_DIR}/python - COMMAND ${Python_EXECUTABLE} setup.py bdist_wheel - ) - - add_custom_command( - TARGET onnxruntime_pybind11_state POST_BUILD - COMMAND ${Python_EXECUTABLE} - $/onnxruntime/providers/tvm/extend_python_file.py - --target_file $/onnxruntime/capi/_ld_preload.py - ) - -endif() - if (onnxruntime_USE_DML) if (NOT onnxruntime_USE_CUSTOM_DIRECTML) set(dml_shared_lib_path ${DML_PACKAGE_DIR}/bin/${onnxruntime_target_platform}-win/${DML_SHARED_LIB}) diff --git a/cmake/onnxruntime_unittests.cmake b/cmake/onnxruntime_unittests.cmake index 3b73933f3ff1d..e822f0a3655fc 100644 --- a/cmake/onnxruntime_unittests.cmake +++ b/cmake/onnxruntime_unittests.cmake @@ -9,9 +9,6 @@ set(TEST_INC_DIR ${ONNXRUNTIME_ROOT}) if (onnxruntime_ENABLE_TRAINING) list(APPEND TEST_INC_DIR ${ORTTRAINING_ROOT}) endif() -if (onnxruntime_USE_TVM) - list(APPEND TEST_INC_DIR ${TVM_INCLUDES}) -endif() set(disabled_warnings) function(AddTest) @@ -114,7 +111,6 @@ function(AddTest) endif() target_compile_options(${_UT_TARGET} PRIVATE ${disabled_warnings}) else() - target_compile_options(${_UT_TARGET} PRIVATE ${DISABLED_WARNINGS_FOR_TVM}) target_compile_options(${_UT_TARGET} PRIVATE "$<$:SHELL:--compiler-options -Wno-error=sign-compare>" "$<$>:-Wno-error=sign-compare>") if (${HAS_NOERROR}) @@ -644,13 +640,11 @@ set(ONNXRUNTIME_TEST_LIBS ${PROVIDERS_ACL} ${PROVIDERS_ARMNN} ${PROVIDERS_COREML} - # ${PROVIDERS_TVM} ${PROVIDERS_XNNPACK} ${PROVIDERS_AZURE} onnxruntime_optimizer onnxruntime_providers onnxruntime_util - ${onnxruntime_tvm_libs} onnxruntime_lora onnxruntime_framework onnxruntime_util @@ -752,12 +746,6 @@ if(onnxruntime_USE_AZURE) list(APPEND onnxruntime_test_providers_libs onnxruntime_providers_azure) endif() -if(WIN32) - if (onnxruntime_USE_TVM) - list(APPEND disabled_warnings ${DISABLED_WARNINGS_FOR_TVM}) - endif() -endif() - file(GLOB onnxruntime_test_framework_src CONFIGURE_DEPENDS ${onnxruntime_test_framework_src_patterns} ) @@ -858,9 +846,6 @@ if (onnxruntime_ENABLE_TRAINING_APIS) list(APPEND all_tests ${onnxruntime_test_training_api_src}) endif() -if (onnxruntime_USE_TVM) - list(APPEND all_tests ${onnxruntime_test_tvm_src}) -endif() if (onnxruntime_USE_OPENVINO) list(APPEND all_tests ${onnxruntime_test_openvino_src}) @@ -1092,15 +1077,6 @@ if (NOT onnxruntime_ENABLE_TRAINING_TORCH_INTEROP) COMMAND ${CMAKE_COMMAND} -E copy ${DNNL_DLL_PATH} $ ) endif() - if(WIN32) - if (onnxruntime_USE_TVM) - add_custom_command( - TARGET ${test_data_target} POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy $ $ - ) - endif() - endif() - if(WIN32) set(wide_get_opt_src_dir ${TEST_SRC_DIR}/win_getopt/wide) onnxruntime_add_static_library(win_getopt_wide ${wide_get_opt_src_dir}/getopt.cc ${wide_get_opt_src_dir}/include/getopt.h) @@ -1142,12 +1118,6 @@ if (NOT IOS) endif() set_target_properties(onnx_test_runner PROPERTIES FOLDER "ONNXRuntimeTest") - if (onnxruntime_USE_TVM) - if (WIN32) - target_link_options(onnx_test_runner PRIVATE "/STACK:4000000") - endif() - endif() - install(TARGETS onnx_test_runner ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR} LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR} @@ -1301,11 +1271,6 @@ if (NOT onnxruntime_ENABLE_TRAINING_TORCH_INTEROP) endif() set_target_properties(onnxruntime_perf_test PROPERTIES FOLDER "ONNXRuntimeTest") - if (onnxruntime_USE_TVM) - if (WIN32) - target_link_options(onnxruntime_perf_test PRIVATE "/STACK:4000000") - endif() - endif() endif() diff --git a/csharp/src/Microsoft.ML.OnnxRuntime/NativeMethods.shared.cs b/csharp/src/Microsoft.ML.OnnxRuntime/NativeMethods.shared.cs index be157a0419fc0..d6c46833f1514 100644 --- a/csharp/src/Microsoft.ML.OnnxRuntime/NativeMethods.shared.cs +++ b/csharp/src/Microsoft.ML.OnnxRuntime/NativeMethods.shared.cs @@ -1142,9 +1142,6 @@ IntPtr[] outputValues /* An array of output value pointers. Array must be alloca [DllImport(NativeLib.DllName, CharSet = CharSet.Ansi)] public static extern IntPtr /*(OrtStatus*)*/ OrtSessionOptionsAppendExecutionProvider_MIGraphX(IntPtr /*(OrtSessionOptions*)*/ options, int device_id); - - [DllImport(NativeLib.DllName, CharSet = CharSet.Ansi)] - public static extern IntPtr /*(OrtStatus*)*/ OrtSessionOptionsAppendExecutionProvider_Tvm(IntPtr /*(OrtSessionOptions*) */ options, byte[] /*(char char*)*/ settings); #endif /// /// Append a TensorRT EP instance (configured based on given provider options) to the native OrtSessionOptions instance diff --git a/csharp/src/Microsoft.ML.OnnxRuntime/SessionOptions.shared.cs b/csharp/src/Microsoft.ML.OnnxRuntime/SessionOptions.shared.cs index 3acd84b3016de..9841d972fa620 100644 --- a/csharp/src/Microsoft.ML.OnnxRuntime/SessionOptions.shared.cs +++ b/csharp/src/Microsoft.ML.OnnxRuntime/SessionOptions.shared.cs @@ -146,27 +146,6 @@ public static SessionOptions MakeSessionOptionWithTensorrtProvider(OrtTensorRTPr } } - /// - /// A helper method to construct a SessionOptions object for TVM execution. - /// Use only if you have the onnxruntime package specific to this Execution Provider. - /// - /// settings string, comprises of comma separated key:value pairs. default is empty - /// A SessionsOptions() object configured for execution with TVM - public static SessionOptions MakeSessionOptionWithTvmProvider(String settings = "") - { - SessionOptions options = new SessionOptions(); - try - { - options.AppendExecutionProvider_Tvm(settings); - return options; - } - catch (Exception) - { - options.Dispose(); - throw; - } - } - /// /// A helper method to construct a SessionOptions object for ROCM execution. /// Use only if ROCM is installed and you have the onnxruntime package specific to this Execution Provider. @@ -397,20 +376,6 @@ public void AppendExecutionProvider_CoreML(CoreMLFlags coremlFlags = CoreMLFlags #endif } - /// - /// Use only if you have the onnxruntime package specific to this Execution Provider. - /// - /// string with TVM specific settings - public void AppendExecutionProvider_Tvm(string settings = "") - { -#if __MOBILE__ - throw new NotSupportedException("The TVM Execution Provider is not supported in this build"); -#else - var utf8 = NativeOnnxValueHelper.StringToZeroTerminatedUtf8(settings); - NativeApiStatus.VerifySuccess(NativeMethods.OrtSessionOptionsAppendExecutionProvider_Tvm(handle, utf8)); -#endif - } - private class ExecutionProviderAppender { private byte[] _utf8ProviderName; diff --git a/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs b/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs index aa0e6ee62248a..b2a863a48e68a 100644 --- a/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs +++ b/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs @@ -146,10 +146,6 @@ public void TestSessionOptions() opt.AppendExecutionProvider_Nnapi(0); #endif -#if USE_TVM - opt.AppendExecutionProvider_Tvm("Vulkan -device=amd_apu"); -#endif - #if USE_OPENVINO opt.AppendExecutionProvider_OpenVINO(); #endif diff --git a/docs/TVM_EP.md b/docs/TVM_EP.md deleted file mode 100644 index df59d5c05855c..0000000000000 --- a/docs/TVM_EP.md +++ /dev/null @@ -1,319 +0,0 @@ -# TVM Execution Provider - -## Contents - -- [Introduction](#introduction) -- [Build](#build-onnx-runtime-with-the-tvm-execution-provider) - - [Linux](#linux) - - [Windows](#windows) -- [Configuration options](#configuration-options) -- [Performance Tuning](#performance-tuning) - - [Using precompiled model](#using-precompiled-model) -- [Samples](#samples) -- [Known issues](#known-issues) - - -## Introduction - -TVM is an execution provider for ONNX Runtime that is built on top of Apache TVM. It enables ONNX Runtime users to leverage Apache TVM model optimizations. -TVM EP is currently in "Preview". It's been tested to work on a handful of models on Linux or Windows, but not on MacOS. - -## Build ONNX Runtime with the TVM Execution Provider - -### **Linux** -Install the minimal pre-requisites on Ubuntu/Debian like linux operating systems: -```bash -apt-get install -y python3 python3-dev python3-pip python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev llvm-12 -pip3 install numpy decorator attrs nasm -``` -Note: since ONNX Runtime with TVM EP is built with Intel ipp-crypto library there are new requirements. Compiler gcc (and g++) version should be equal to or higher than 8.2. nasm version should be 2.14.02 or higher. Problem with small nasm version can be seen [here](https://github.com/intel/ipp-crypto/issues/9) or [here](https://bugzilla.nasm.us/show_bug.cgi?id=3392205). For ubuntu LTS 18 `apt-get install nasm` is not enough due to it has version 2.13.02, see how to install from sources instruction [here](https://stackoverflow.com/questions/36144930/steps-to-install-nasm-offline-on-ubuntu). - -Also, the current implementation has `NVidia GPU` support for TVM EP. For now, you can use only `NVidia GPU` with CUDA Toolkit support. -To do this, make sure you have installed the NVidia driver and CUDA Toolkit. -More detailed instructions can be found on the [official page](https://developer.nvidia.com/cuda-toolkit). - -Clone this repo. -In order to build ONNXRT you will need to have CMake 3.18 or higher. In Ubuntu 20.04 you can use the following commands to install the latest version of CMake: - -```bash -sudo apt-get update -sudo apt-get install gpg wget - -wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null - -echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ focal main' | sudo tee /etc/apt/sources.list.d/kitware.list >/dev/null -sudo apt-get update - -sudo rm /usr/share/keyrings/kitware-archive-keyring.gpg -sudo apt-get install kitware-archive-keyring - -sudo apt-get install cmake -``` - -Build ONNX Runtime (TVM x86): -```bash -./build.sh --config Release --enable_pybind --build_wheel --parallel --skip_tests --skip_onnx_tests --use_tvm -``` - -Build ONNX Runtime (TVM with CUDA support): -```bash -./build.sh --config Release --enable_pybind --build_wheel --parallel --skip_tests --skip_onnx_tests --use_tvm --tvm_cuda_runtime -``` - -This command builds both `TVM` and `onnxruntime-tvm`. It creates two wheel, one for each project. -Build the python API for ONNX Runtime instead of using the standard package. Instructions for this are given below. - -Package for TVM: -```bash -cd -python3 -m pip uninstall tvm -y -whl_path=$(find ./build//Release/_deps/tvm-src/python/dist -name "*.whl") -python3 -m pip install $whl_path -``` - -Package for TVM EP: -```bash -cd -python3 -m pip uninstall onnxruntime onnxruntime-tvm -y -whl_path=$(find ./build//Release/dist -name "*.whl") -python3 -m pip install $whl_path -``` - -Alternatively, you can set `PYTHONPATH` to tell python where to find the ONNXRT library and the TVM library. -```bash -export PYTHONPATH=/build//Release:${PYTHONPATH} -export PYTHONPATH=/build//Release/_deps/tvm-src/python:${PYTHONPATH} -``` - -### **Windows** -Install the minimal prerequisites on Windows: Git, CMake, Visual Studio, Python, LLVM -- Git: Download Git for Windows from [here](https://git-scm.com/download/win) and install it. Please make sure that the git.exe path is included in the environment variable. By default, it should be added. To check git after the installation use `git --version` in command line (cmd). -- CMake: use [the link](https://cmake.org/download/) to download and install CMake. msi-file is recommended for it. To verify CMake installation use `cmake --version` in cmd. -- Visual Studio: Download from [here](https://visualstudio.microsoft.com/ru/downloads/) and install Visual Studio 20** Community & Visual Studio Build Tools respectively. It is recommended not to change the default installation path. Chose "Desktop development with C++" workload and make sure that both options of “MSVC [contemporary version] C++ build tools” and “Windows 10 SDK” are selected. -- Python: Download Python 3.* from [here](https://www.python.org/downloads/windows/) and install it. Please have a check on the option of “Add Python to PATH”, so the installer will include the Python directory into the environment variable directly. To check python after the installation use `python` from cmd. The expected output is similar to the following: -```cmd -Python 3.10.5 (tags/v3.10.5:f377153, Jun 6 2022, 16:14:13) [MSC v.1929 64 bit (AMD64)] on win32 -Type "help", "copyright", "credits" or "license" for more information. ->>> -``` -Use `quit()` to exit from python interface. -- LLVM: the compiler is not necessary for pure ONNX Runtime installation but it is needed for TVM EP by default. -```cmd -git clone --depth 1 --branch release/11.x https://github.com/llvm/llvm-project.git -cmake -S llvm -B build -DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi" -DLLVM_TARGETS_TO_BUILD=X86 -Thost=x64 -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 17 2022" -cmake --build ./build --config Release -``` -- Dependencies of ipp-crypto:
-1. install asm compiler (nasm) on windows by line: -```cmd -winget install nasm -i -``` -          -Add it to PATH (instruction for Windows GUI can be seen [here](https://www.computerhope.com/issues/ch000549.htm#dospath)) or by cmd: -```cmd -set PATH="%PATH%;C:\Program Files\NASM" -``` -          -or -```cmd -setx PATH "%PATH%;C:\Program Files\NASM" -``` -          -Check by `nasm --version` in prompt command line.
-       -2. install openssl on windows by msi-file from [here](https://slproweb.com/products/Win32OpenSSL.html) -Add path to directory (e.g. "C:\Program Files\OpenSSL-Win64\bin") with executable file to PATH (see instructions above).
-          -Check by `openssl version` in prompt command line.
-       -3. Correct build of ipp-crytpo requires specific environment variables for supported MSVC compiler. Long way to adjust the environment is to follow to instructions [here](https://docs.microsoft.com/en-us/cpp/build/building-on-the-command-line?view=msvc-170&viewFallbackFrom=vs-2017). Quick way is to use VS Developer command prompt where the environment have been already adjusted or add some paths to standard Windows command prompt: -```cmd -set INCLUDE=C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.32.31326\include;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\ucrt -``` -          -Take into account that MSVC and Kit versions are specific for Visual Studio built on the machine, specified values here are used as example. -
-
- -For using NVIDIA GPU (optional) CUDA and cuDNN should be installed. -- CUDA: Install CUDA by the [link](https://developer.nvidia.com/cuda-11.0-download-archive). -- cuDNN: download cuDNN installer from [here](https://developer.nvidia.com/rdp/cudnn-archive). Choose v8.* for corresponding CUDA v11.*, unzip it, and move cuDNN files as following: -1. [unzipped dir]\bin\ → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin -2. [unzipped dir]\include\ → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include -3. [unzipped dir]\lib\ → C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\lib - -To verify the CUDA installation use `nvcc --version` in cmd. -
-
- -#### **Build ONNX Runtime with TVM Execution Provider from source (Python):** -- Use command line and clone sources from github: -```cmd -git clone --recursive https://github.com/Microsoft/onnxruntime -cd onnxruntime -``` -- CPU build: -``` -build.bat --config Release --enable_pybind --build_wheel --skip_tests --parallel --use_tvm --skip_onnx_tests --cmake_generator "Visual Studio 17 2022" --llvm_config /build/Release/bin/llvm-config.exe -``` -- GPU build: -``` -build.bat --config Release --enable_pybind --build_wheel --skip_tests --parallel --use_tvm --skip_onnx_tests --cmake_generator "Visual Studio 17 2022" --llvm_config /build/Release/bin/llvm-config.exe --use_cuda --cudnn_home “C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.*” --cuda_home “C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.*” -``` -In both cases (CPU, GPU) there are the following options for cmake generator: "Visual Studio 17 2022" and "Ninja". Also handshake mechanism can be switched on by `--use_tvm_hash` flag. At the latter case ipp-crypto library is built with dependencies, see details above. -- Install python wheel package for ONNX Runtime:
-Default path to the package is `/build/Windows/Release/Release/dist`. Note that it is different in comparison with path to the package on Linux. Before installation check names of wheel packages and use corresponding one. It can be looked like the following: -```cmd -python -m pip install .\onnxruntime\build\Windows\Release\Release\dist\onnxruntime_tvm-1.6.0-cp38-cp38-win_amd64.whl -``` -- Install python wheel package for TVM due to its python API is used inside TVM EP:
-It can be looked like the following: -```cmd -python -m pip install .\onnxruntime\build\Windows\Release\_deps\tvm-src\python\dist\tvm-0.9.dev1728+g3425ed846-cp39-cp39-win_amd64.whl -``` -- Verify result by python script. Note: python should not be launched from directory containing 'onnxruntime' directory for correct result: -```python -import onnxruntime -print(onnxruntime.__version__) -print(onnxruntime.get_device()) -print(onnxruntime.get_available_providers()) -``` -- Uninstall procedure: -```cmd -pip uninstall onnxruntime-tvm -``` - -#### **Build ONNX Runtime with TVM Execution Provider from source (C#):** -- Use command line and clone sources from github: -```cmd -git clone --recursive https://github.com/Microsoft/onnxruntime -cd onnxruntime -``` -- CPU build: - -Make sure you download [nuget.exe](https://docs.microsoft.com/en-us/nuget/install-nuget-client-tools#nugetexe-cli) and add path to it into `PATH` env. -``` -build.bat --config Release --build_nuget --skip_tests --parallel --use_tvm --skip_onnx_tests --cmake_generator "Visual Studio 17 2022" --llvm_config llvm-config.exe -``` -- Install C# nuget package for TVM EP. Default path to the package is `\build\Windows\Release\Release`. - - -## Configuration options -TVM Executor Provider can be configured with the following provider options: -1. Python -```python -po = [dict(executor=tvm_executor_type, - so_folder=folder_with_pretuned_files, - check_hash=check_hash, - hash_file_path=hash_file_path, - target=client_target, - target_host=client_target_host, - opt_level=client_opt_level, - freeze_weights=freeze, - to_nhwc=layout_transform, - tuning_type=tvm_optimizer_type, - tuning_file_path=client_tuning_logfile, - input_names = input_names_str, - input_shapes = input_shapes_str)] -tvm_session = onnxruntime.InferenceSession(model_path, providers=["TvmExecutionProvider"], provider_options=po) -``` - -2. C# - -Currently, only precompiled models are supported in C# (see the related section below). - -```CSharp -SessionOptions session_options = new SessionOptions{}; -string tvm_ep_options = - $"executor: {tvm_executor_type}, " + - $"so_folder: {folder_with_pretuned_files}, " + - $"check_hash: {check_hash}, " + - $"hash_file_path: {hash_file_path}, " + - $"target: {client_target}, " + - $"target_host: {client_target_host}, " + - $"opt_level: {client_opt_level}, " + - $"freeze_weights: {freeze}, " + - $"to_nhwc: {layout_transform}, " + - $"tuning_type: {tvm_optimizer_type}, " + - $"tuning_file_path: {client_tuning_logfile}, " + - $"input_names: {input_names_str}, " + - $"input_shapes: {input_shapes_str}"; - -session_options.AppendExecutionProvider_Tvm(tvm_ep_options); -using var tvm_session = new InferenceSession(modelFilePath, session_options); -``` -
- -- `executor` is executor type used by TVM. There is choice between two types: GraphExecutor and VirtualMachine which are corresponded to "graph" and "vm" tags. VirtualMachine is used by default. -- `so_folder` is path to folder with set of files (.ro-, .so/.dll-files and weights) obtained after model tuning. It uses these files for executor compilation instead of onnx-model. But the latter is still needed for ONNX Runtime. -- `check_hash` means that it is necessary to perform a HASH check for the model obtained in the `so_folder` parameter. It is `False` by default. -- `hash_file_path` is path to file that contains the pre-computed HASH for the ONNX model which result of tuning locates in the path passed by `so_folder` parameter. - If an empty string was passed as this value, then the file will be searched in the folder that was passed in the `so_folder` parameter. -- `target` and `target_host` are strings like in TVM (e.g. "llvm --mcpu=avx2"). When using accelerators, target may be something like `cuda` while target_host may be `llvm -mtriple=x86_64-linux-gnu` -- `opt_level` is TVM optimization level. It is 3 by default -- `freeze_weights` means that all model weights are kept on compilation stage otherwise they are downloaded each inference. True is recommended value for the best performance. It is true by default. -- `to_nhwc` switches on special model transformations, particularly data layout, which Octomizer is used. It allows to work correctly with tuning logs obtained from Octomizer. It is false by default. -- `tuning_type` defines the type of TVM tuning logs being used, and can be set to either `AutoTVM` (1st gen auto tuning logs) or `Ansor` (2nd gen auto tuning logs). By default this option is set to `AutoTVM`. -- `tuning_file_path` is path to AutoTVM or Ansor tuning file which gives specifications for given model and target for the best performance. (See below for more details). - -TVM supports models with fixed graph only. If your model has unknown dimensions in input shapes (excluding batch size) you must provide the shape using the `input_names` and `input_shapes` provider options. Below is an example of what must be passed to `provider_options`: -```python -input_names = "input_1 input_2" -input_shapes = "[1 3 224 224] [1 2]" -``` - -## Performance Tuning -TVM optimizes machine learning models through an automated tuning process that produces model variants specific to targeted hardware architectures. This process also generates 'tuning logs' that the TVM EP relies on to maximize model performance. These logs can be acquired for your model by either using TVM as described here: - -AutoTVM: -https://tvm.apache.org/docs/how_to/tune_with_autotvm/index.html - -Ansor (Autoscheduling): -https://tvm.apache.org/docs/how_to/tune_with_autoscheduler/index.html - -or by using logs generated through the OctoML platform (https://onnx.octoml.ai) using instructions [here](https://help.octoml.ai/en/articles/5814452-using-octoml-platform-logs-with-onnx-rt-tvm-ep) - -Using the TVM EP with TVM tuning logs also requires users to turn off ONNX Runtime preprocessing. To do this, the following `SessionOptions()` can be used: -``` -so = onnxruntime.SessionOptions() -so.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL - -tvm_session = onnxruntime.InferenceSession(model_path, sess_options=so, providers=["TvmExecutionProvider"], provider_options=po) -``` - -### **Using precompiled model** -It is also possible to use a precompiled model. - -The compiled model can be obtained using the [OctoML platform](https://onnx.octoml.ai) -or compiled directly (see **Support precompiled model** section in -[Sample notebook for ResNet50 inference with TVM EP](https://github.com/microsoft/onnxruntime/blob/main/docs/python/notebooks/onnxruntime-tvm-tutorial.ipynb) -for more information on model compilation). - -In order to use the precompiled model, only need to pass two options: -* **executor** - `vm` (`VirtualMachine`) must be used as a value -(this functionality is not supported for `GraphExecutor`); -* **so_folder** - as a value, you must pass the path to the directory where -the files of the precompiled model are located. -* **check_hash** - (optional) if you want to check hash, you must pass `True` as the value. -* **hash_file_path** - (optional) by default, the file containing the hash for the tuned model will be searched in the directory that is passed in the `so_folder` parameter. - If you want to specify different location, then you must pass the path to the file that contains the desired hash as a value. - -You can read more about these options in section [Configuration options](#configuration-options) above. - - -## Samples -- [Sample notebook for ResNet50 inference with TVM EP](https://github.com/microsoft/onnxruntime/blob/main/docs/python/notebooks/onnxruntime-tvm-tutorial.ipynb) - -## Known issues -- At this moment, the TVM EP has only been verified on UNIX/Linux and Windows systems. -- Some compatibility issues have been found between ONNX and Google protobuf. `AttributeError: module 'google.protobuf.internal.containers' has no attribute 'MutableMapping'`. This usually occurss during `import onnx` in any python scripts for protobuf version >= 3.19.0 and ONNX version <= 1.8.1. To resolve the issue Google protobuf and ONNX can be reinstalled separately or together using: -``` -pip3 uninstall onnx -y -pip3 install onnx==1.10.1 -pip3 uninstall protobuf -y -pip3 install protobuf==3.19.1 -``` - -The following pair of ONNX and protobuf versions have been found to be compatible: -- 3.17.3 and 1.8.0 -- 3.19.1 and 1.10.1 diff --git a/docs/python/notebooks/onnxruntime-tvm-tutorial.ipynb b/docs/python/notebooks/onnxruntime-tvm-tutorial.ipynb deleted file mode 100644 index 830495bdfb98d..0000000000000 --- a/docs/python/notebooks/onnxruntime-tvm-tutorial.ipynb +++ /dev/null @@ -1,657 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "72476497", - "metadata": {}, - "source": [ - "# ONNX Runtime: Tutorial for TVM execution provider\n", - "\n", - "This notebook shows a simple example for model inference with TVM EP.\n", - "\n", - "\n", - "#### Tutorial Roadmap:\n", - "1. Prerequistes\n", - "2. Accuracy check for TVM EP\n", - "3. Configuration options\n", - "4. Support precompiled model" - ] - }, - { - "cell_type": "markdown", - "id": "9345cbab", - "metadata": {}, - "source": [ - "## 1. Prerequistes\n", - "\n", - "Make sure that you have installed all the necessary dependencies described in the corresponding paragraph of the documentation.\n", - "\n", - "Also, make sure you have the `tvm` and `onnxruntime-tvm` packages in your pip environment. \n", - "\n", - "If you are using `PYTHONPATH` variable expansion, make sure it contains the following paths: `/onnxruntime/cmake/external/tvm_update/python` and `/onnxruntime/build/Linux/Release`." - ] - }, - { - "cell_type": "markdown", - "id": "da4ca21f", - "metadata": {}, - "source": [ - "### Common import\n", - "\n", - "These packages can be delivered from standard `pip`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0f072875", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import onnx\n", - "import tempfile\n", - "import numpy as np\n", - "from typing import List, AnyStr\n", - "from onnx import ModelProto, helper, checker, mapping" - ] - }, - { - "cell_type": "markdown", - "id": "118670aa", - "metadata": {}, - "source": [ - "### Specialized import\n", - "\n", - "It is better to collect these packages from source code in order to clearly understand what is available to you right now." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a5502966", - "metadata": {}, - "outputs": [], - "source": [ - "import onnxruntime\n", - "\n", - "import tvm\n", - "import tvm.relay\n", - "import tvm.testing\n", - "import tvm.runtime\n", - "import tvm.runtime.vm\n", - "import tvm.relay.backend.vm\n", - "import tvm.contrib.download" - ] - }, - { - "cell_type": "markdown", - "id": "b7313183", - "metadata": {}, - "source": [ - "### Helper functions for working with ONNX ModelProto\n", - "\n", - "This set of helper functions allows you to recognize the meta information of the models. This information is needed for more versatile processing of ONNX models." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7d0a36e8", - "metadata": {}, - "outputs": [], - "source": [ - "def get_onnx_input_names(model: ModelProto) -> List[AnyStr]:\n", - " inputs = [node.name for node in model.graph.input]\n", - " initializer = [node.name for node in model.graph.initializer]\n", - " inputs = list(set(inputs) - set(initializer))\n", - " return sorted(inputs)\n", - "\n", - "\n", - "def get_onnx_output_names(model: ModelProto) -> List[AnyStr]:\n", - " return [node.name for node in model.graph.output]\n", - "\n", - "\n", - "def get_onnx_input_types(model: ModelProto) -> List[np.dtype]:\n", - " input_names = get_onnx_input_names(model)\n", - " return [\n", - " mapping.TENSOR_TYPE_TO_NP_TYPE[node.type.tensor_type.elem_type]\n", - " for node in sorted(model.graph.input, key=lambda node: node.name) if node.name in input_names\n", - " ]\n", - "\n", - "\n", - "def get_onnx_input_shapes(model: ModelProto) -> List[List[int]]:\n", - " input_names = get_onnx_input_names(model)\n", - " return [\n", - " [dv.dim_value for dv in node.type.tensor_type.shape.dim]\n", - " for node in sorted(model.graph.input, key=lambda node: node.name) if node.name in input_names\n", - " ]\n", - "\n", - "\n", - "def get_random_model_inputs(model: ModelProto) -> List[np.ndarray]:\n", - " input_shapes = get_onnx_input_shapes(model)\n", - " input_types = get_onnx_input_types(model)\n", - " assert len(input_types) == len(input_shapes)\n", - " inputs = [np.random.uniform(size=shape).astype(dtype) for shape, dtype in zip(input_shapes, input_types)]\n", - " return inputs" - ] - }, - { - "cell_type": "markdown", - "id": "f0de1682", - "metadata": {}, - "source": [ - "### Wrapper helper functions for Inference\n", - "\n", - "Wrapper helper functions for running model inference using ONNX Runtime EP." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "258ce9e9", - "metadata": {}, - "outputs": [], - "source": [ - "def get_onnxruntime_output(model: ModelProto, inputs: List, provider_name: AnyStr) -> np.ndarray:\n", - " output_names = get_onnx_output_names(model)\n", - " input_names = get_onnx_input_names(model)\n", - " assert len(input_names) == len(inputs)\n", - " input_dict = {input_name: input_value for input_name, input_value in zip(input_names, inputs)}\n", - "\n", - " inference_session = onnxruntime.InferenceSession(model.SerializeToString(), providers=[provider_name])\n", - " output = inference_session.run(output_names, input_dict)\n", - "\n", - " # Unpack output if there's only a single value.\n", - " if len(output) == 1:\n", - " output = output[0]\n", - " return output\n", - "\n", - "\n", - "def get_cpu_onnxruntime_output(model: ModelProto, inputs: List) -> np.ndarray:\n", - " return get_onnxruntime_output(model, inputs, \"CPUExecutionProvider\")\n", - "\n", - "\n", - "def get_tvm_onnxruntime_output(model: ModelProto, inputs: List) -> np.ndarray:\n", - " return get_onnxruntime_output(model, inputs, \"TvmExecutionProvider\")" - ] - }, - { - "cell_type": "markdown", - "id": "cc17d3b2", - "metadata": {}, - "source": [ - "### Helper function for checking accuracy\n", - "\n", - "This function uses the TVM API to compare two output tensors. The tensor obtained using the `CPUExecutionProvider` is used as a reference.\n", - "\n", - "If a mismatch is found between tensors, an appropriate exception will be thrown." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4e598907", - "metadata": {}, - "outputs": [], - "source": [ - "def verify_outputs(\n", - " lhs: List[np.ndarray],\n", - " rhs: List[np.ndarray],\n", - " rtol: float = 5e-5,\n", - " atol: float = 5e-5\n", - ") -> None:\n", - " for lhs_tensor, rhs_tensor in zip(lhs, rhs):\n", - " tvm.testing.assert_allclose(lhs_tensor, rhs_tensor, rtol=rtol, atol=atol)\n", - " assert lhs_tensor.dtype == rhs_tensor.dtype\n", - " print(\"Same output, congratulations!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f33a372b", - "metadata": {}, - "outputs": [], - "source": [ - "def verify_with_ort_with_inputs(\n", - " model,\n", - " inputs,\n", - " out_shape=None,\n", - " opset=None,\n", - " freeze_params=False,\n", - " dtype=\"float32\",\n", - " rtol=1e-5,\n", - " atol=1e-5,\n", - " opt_level=1,\n", - "):\n", - " if opset is not None:\n", - " model.opset_import[0].version = opset\n", - "\n", - " ort_out = get_cpu_onnxruntime_output(model, inputs)\n", - " tvm_out = get_tvm_onnxruntime_output(model, inputs)\n", - " verify_outputs(ort_out, tvm_out, rtol, atol)" - ] - }, - { - "cell_type": "markdown", - "id": "8c62b01a", - "metadata": {}, - "source": [ - "### Helper functions for download models\n", - "\n", - "These functions use the TVM API to download models from the ONNX Model Zoo." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "324c00e7", - "metadata": {}, - "outputs": [], - "source": [ - "BASE_MODEL_URL = \"https://github.com/onnx/models/raw/master/\"\n", - "MODEL_URL_COLLECTION = {\n", - " \"ResNet50-v1\": \"vision/classification/resnet/model/resnet50-v1-7.onnx\",\n", - " \"ResNet50-v2\": \"vision/classification/resnet/model/resnet50-v2-7.onnx\",\n", - " \"SqueezeNet-v1.1\": \"vision/classification/squeezenet/model/squeezenet1.1-7.onnx\",\n", - " \"SqueezeNet-v1.0\": \"vision/classification/squeezenet/model/squeezenet1.0-7.onnx\",\n", - " \"Inception-v1\": \"vision/classification/inception_and_googlenet/inception_v1/model/inception-v1-7.onnx\",\n", - " \"Inception-v2\": \"vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-7.onnx\",\n", - "}\n", - "\n", - "\n", - "def get_model_url(model_name):\n", - " return BASE_MODEL_URL + MODEL_URL_COLLECTION[model_name]\n", - "\n", - "\n", - "def get_name_from_url(url):\n", - " return url[url.rfind(\"/\") + 1 :].strip()\n", - "\n", - "\n", - "def find_of_download(model_name):\n", - " model_url = get_model_url(model_name)\n", - " model_file_name = get_name_from_url(model_url)\n", - " return tvm.contrib.download.download_testdata(model_url, model_file_name, module=\"models\")" - ] - }, - { - "cell_type": "markdown", - "id": "90fb7c5c", - "metadata": {}, - "source": [ - "## 2. Accuracy check for TVM EP \n", - "\n", - "This section will check the accuracy. The check will be to compare the output tensors for `CPUExecutionProvider` and `TvmExecutionProvider`. See the description of `verify_with_ort_with_inputs` function used above.\n", - "\n", - "\n", - "### Check for simple architectures" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c739ed5c", - "metadata": {}, - "outputs": [], - "source": [ - "def get_two_input_model(op_name: AnyStr) -> ModelProto:\n", - " dtype = \"float32\"\n", - " in_shape = [1, 2, 3, 3]\n", - " in_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]\n", - " out_shape = in_shape\n", - " out_type = in_type\n", - "\n", - " layer = helper.make_node(op_name, [\"in1\", \"in2\"], [\"out\"])\n", - " graph = helper.make_graph(\n", - " [layer],\n", - " \"two_input_test\",\n", - " inputs=[\n", - " helper.make_tensor_value_info(\"in1\", in_type, in_shape),\n", - " helper.make_tensor_value_info(\"in2\", in_type, in_shape),\n", - " ],\n", - " outputs=[\n", - " helper.make_tensor_value_info(\n", - " \"out\", out_type, out_shape\n", - " )\n", - " ],\n", - " )\n", - " model = helper.make_model(graph, producer_name=\"two_input_test\")\n", - " checker.check_model(model, full_check=True)\n", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7048ee6d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Same output, congratulations!\n", - "****************** Success! ******************\n" - ] - } - ], - "source": [ - "onnx_model = get_two_input_model(\"Add\")\n", - "inputs = get_random_model_inputs(onnx_model)\n", - "verify_with_ort_with_inputs(onnx_model, inputs)\n", - "print(\"****************** Success! ******************\")" - ] - }, - { - "cell_type": "markdown", - "id": "52c880f4", - "metadata": {}, - "source": [ - "### Check for DNN architectures " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f5d465dc", - "metadata": {}, - "outputs": [], - "source": [ - "def get_onnx_model(model_name):\n", - " model_path = find_of_download(model_name)\n", - " onnx_model = onnx.load(model_path)\n", - " return onnx_model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "68daac7e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Same output, congratulations!\n", - "****************** Success! ******************\n" - ] - } - ], - "source": [ - "model_name = \"ResNet50-v1\"\n", - "\n", - "onnx_model = get_onnx_model(model_name)\n", - "inputs = get_random_model_inputs(onnx_model)\n", - "verify_with_ort_with_inputs(onnx_model, inputs)\n", - "print(\"****************** Success! ******************\")" - ] - }, - { - "cell_type": "markdown", - "id": "e27f64a2", - "metadata": {}, - "source": [ - "## 3. Configuration options\n", - "\n", - "This section shows how you can configure TVM EP using custom options. For more details on the options used, see the corresponding section of the documentation." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a053f59f", - "metadata": {}, - "outputs": [], - "source": [ - "provider_name = \"TvmExecutionProvider\"\n", - "provider_options = dict(\n", - " target=\"llvm -mtriple=x86_64-linux-gnu\",\n", - " target_host=\"llvm -mtriple=x86_64-linux-gnu\",\n", - " opt_level=3,\n", - " freeze_weights=True,\n", - " tuning_file_path=\"\",\n", - " tuning_type=\"Ansor\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3f6e6f01", - "metadata": {}, - "outputs": [], - "source": [ - "model_name = \"ResNet50-v1\"\n", - "onnx_model = get_onnx_model(model_name)\n", - "input_dict = {\n", - " input_name: input_value for input_name, input_value in zip(\n", - " get_onnx_input_names(onnx_model),\n", - " get_random_model_inputs(onnx_model),\n", - " )\n", - "}\n", - "output_names = get_onnx_output_names(onnx_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "85ab83f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "****************** Output shape: (1, 1000) ******************\n" - ] - } - ], - "source": [ - "tvm_session = onnxruntime.InferenceSession(\n", - " onnx_model.SerializeToString(),\n", - " providers=[provider_name],\n", - " provider_options=[provider_options],\n", - ")\n", - "output = tvm_session.run(output_names, input_dict)[0]\n", - "print(f\"****************** Output shape: {output.shape} ******************\")" - ] - }, - { - "cell_type": "markdown", - "id": "b704374b", - "metadata": {}, - "source": [ - "## 4. Support precompiled model\n", - "\n", - "Wrapper functions that allow you to compile the model and save it in the desired format." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8150942b", - "metadata": {}, - "outputs": [], - "source": [ - "def compile_virtual_machine(model: onnx.ModelProto, target_str: AnyStr) -> tvm.runtime.vm.Executable:\n", - " ir_mod, params = tvm.relay.frontend.from_onnx(\n", - " model,\n", - " opset=model.opset_import[0].version,\n", - " freeze_params=True,\n", - " )\n", - " target = tvm.target.Target(target=target_str, host=target_str)\n", - " return tvm.relay.backend.vm.compile(ir_mod, target)\n", - "\n", - "\n", - "def serialize_virtual_machine(vm_exec: tvm.runtime.vm.Executable) -> AnyStr:\n", - " temp_directory = tempfile.mkdtemp()\n", - " path_consts = os.path.join(temp_directory, \"consts\")\n", - " vm_exec.move_late_bound_consts(path_consts, byte_limit=256)\n", - " lib_path = os.path.join(temp_directory, f\"model.so\")\n", - " code_path = os.path.join(temp_directory, f\"model.ro\")\n", - " code, lib = vm_exec.save()\n", - " lib.export_library(lib_path)\n", - " with open(code_path, \"wb\") as fo:\n", - " fo.write(code)\n", - " return temp_directory" - ] - }, - { - "cell_type": "markdown", - "id": "9cbb987e", - "metadata": {}, - "source": [ - "Preparation of the ONNX model." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "febb9d72", - "metadata": {}, - "outputs": [], - "source": [ - "model_name = \"ResNet50-v1\"\n", - "onnx_model = get_onnx_model(model_name)\n", - "input_dict = {\n", - " input_name: input_value for input_name, input_value in zip(\n", - " get_onnx_input_names(onnx_model),\n", - " get_random_model_inputs(onnx_model),\n", - " )\n", - "}\n", - "output_names = get_onnx_output_names(onnx_model)" - ] - }, - { - "cell_type": "markdown", - "id": "b05b251a", - "metadata": {}, - "source": [ - "Compiling the ONNX model using `VirtualMachine` (TVM)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b4b999ee", - "metadata": {}, - "outputs": [], - "source": [ - "compiled_vm_exec = compile_virtual_machine(onnx_model, target_str=\"llvm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e3408c15", - "metadata": {}, - "outputs": [], - "source": [ - "so_folder = serialize_virtual_machine(compiled_vm_exec)" - ] - }, - { - "cell_type": "markdown", - "id": "311405e8", - "metadata": {}, - "source": [ - "Preparing `ProviderOptions` and launching `TVM EP` inference.\n", - "\n", - "In order to use the precompiled model, you only need to pass two options:\n", - "* **executor** - `vm` (`VirtualMachine`) must be used as a value (this functionality is not supported for `GraphExecutor`);\n", - "* **so_folder** - as a value, you must pass the path to the directory where the files of the precompiled model are located." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8927293c", - "metadata": {}, - "outputs": [], - "source": [ - "provider_name = \"TvmExecutionProvider\"\n", - "provider_options = dict(\n", - " executor=\"vm\",\n", - " so_folder=so_folder,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d7532863", - "metadata": {}, - "outputs": [], - "source": [ - "tvm_session = onnxruntime.InferenceSession(\n", - " onnx_model.SerializeToString(),\n", - " providers=[provider_name],\n", - " provider_options=[provider_options],\n", - ")\n", - "tvm_output = tvm_session.run(output_names, input_dict)" - ] - }, - { - "cell_type": "markdown", - "id": "1c0b983e", - "metadata": {}, - "source": [ - "Let's make sure that the output values match those that can be obtained through `CPUExecutionProvider`:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c3de2299", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Same output, congratulations!\n" - ] - } - ], - "source": [ - "verify_outputs(\n", - " tvm_output[0],\n", - " get_cpu_onnxruntime_output(\n", - " onnx_model,\n", - " input_dict.values()\n", - " ),\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/onnxruntime/core/codegen/common/common.cc b/onnxruntime/core/codegen/common/common.cc deleted file mode 100644 index 818b919e99ef2..0000000000000 --- a/onnxruntime/core/codegen/common/common.cc +++ /dev/null @@ -1,284 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/common/common.h" - -#include "core/framework/tensorprotoutils.h" -#include "core/common/inlined_containers.h" -#include "core/graph/graph.h" -#include "core/graph/schema_registry.h" -#include -#include - -namespace onnxruntime { - -NodeKey GetKey(const onnxruntime::Node* node) { - ORT_ENFORCE(nullptr != node); - ORT_ENFORCE(node->OutputDefs().size() > 0); - return node->OutputDefs()[0]->Name(); -} - -NodeKey GetKey(const onnxruntime::Node& node) { - ORT_ENFORCE(node.OutputDefs().size() > 0); - return node.OutputDefs()[0]->Name(); -} - -NodeKey GetKey(const onnxruntime::NodeArg* def) { - // NodeArg's name is unique. - ORT_ENFORCE(nullptr != def); - return def->Name(); -} - -bool IsRecurrentNode(const onnxruntime::Node& node) { - auto op_type = node.OpType(); - return (op_type == "LSTM" || op_type == "RNN" || op_type == "GRU" || - op_type == "Scan" || op_type == "Loop"); -} - -bool IsAliasNode(const onnxruntime::Node& node) { - auto op_type = node.OpType(); - if (op_type == "Transpose") { - // Treat Transpose (1,N) -> (N,1) as Alias - const auto shape = node.OutputDefs()[0]->Shape(); - if (shape != nullptr && shape->dim_size() == 2) { - for (int i = 0; i < 2; ++i) { - if (shape->dim(i).has_dim_value() && shape->dim(i).dim_value() == 1) { - return true; - } - } - } - return false; - } - - return (op_type == "Flatten" || op_type == "Identity" || op_type == "Reshape" || - op_type == "Squeeze" || op_type == "Unsqueeze"); -} - -std::string NormalizeCppName(const std::string& name) { - std::string normalized_name = name; - for (char c : {'.', ' ', '+', '-', '*', '/', '\\', '='}) - std::replace(normalized_name.begin(), normalized_name.end(), c, '_'); - return normalized_name; -} - -std::string NormalizeNodeArgName(const NodeArg* def) { - return NormalizeCppName(def->Name()); -} - -bool IsFusedNode(const Node& node) { - if (node.NodeType() == Node::Type::Fused) { - return true; - } - return false; -} - -// A unified API to get Subgraph -const Graph* GetSubgraph(const Node& node) { - if (node.NodeType() == Node::Type::Fused) { - return &(node.GetFunctionBody()->Body()); - } else if (node.OpType() == "Scan") { - return node.GetGraphAttribute("body"); - } - // return nullptr implying no subgraph - return nullptr; -} - -bool HasLoop(const Node& node) { - auto op_type = node.OpType(); - if (op_type == "LSTM" || - op_type == "GRU" || - op_type == "RNN" || - op_type == "Scan") { - return true; - } - return false; -} - -// Return the corresponding input node for the NodeArg of the given node -const onnxruntime::Node* GetInputNode(const Node& node, const NodeArg* def) { - const auto& input_name = def->Name(); - const onnxruntime::Node* input_node = nullptr; - // search input node set to see if input_name is in their outputs (weights are not from node) - for (auto iter = node.InputNodesBegin(); iter != node.InputNodesEnd(); ++iter) { - const onnxruntime::Node& p = *iter; - bool found = false; - ORT_THROW_IF_ERROR(p.ForEachWithIndex( - p.OutputDefs(), - [&found, &input_name](const onnxruntime::NodeArg& out_def, size_t) { - if (input_name == out_def.Name()) { - found = true; - } - return Status::OK(); - })); - if (found) - input_node = &p; - } - return input_node; -} - -// create capacity from subgraph -std::unique_ptr ToCapacity(const onnxruntime::GraphViewer& graph, - int fused_count, - std::unique_ptr& subgraph) { - auto meta_def = std::make_unique<::onnxruntime::IndexedSubGraph::MetaDef>(); - meta_def->name = "Fuse" + std::to_string(fused_count); - meta_def->domain = "Fuse"; - - std::set node_indices(subgraph->nodes.begin(), subgraph->nodes.end()); - - const auto& start_node_index = subgraph->nodes.front(); - const auto& start_node = *graph.GetNode(start_node_index); - const auto& end_node_index = subgraph->nodes.back(); - const auto& end_node = *graph.GetNode(end_node_index); - meta_def->name += start_node.OpType() + std::to_string(start_node_index); - meta_def->name += "_With" + std::to_string(subgraph->nodes.size()) + "Nodes_"; - meta_def->name += end_node.OpType() + std::to_string(end_node_index); - - InlinedHashSet real_output_names; - real_output_names.reserve(graph.GetOutputs().size()); - for (const auto* def : graph.GetOutputs()) { - real_output_names.insert(def->Name()); - } - - for (const auto& node_index : subgraph->nodes) { - const auto& node = *graph.GetNode(node_index); - auto process_input_fn = - [&meta_def, &node, &node_indices](const onnxruntime::NodeArg& def, size_t) { - const onnxruntime::Node* input_node = GetInputNode(node, &def); - bool input_from_subgraph = (input_node && node_indices.count(input_node->Index())); - if (!input_from_subgraph) { - // input is from weights or outside of graph - meta_def->inputs.push_back(def.Name()); - } - return Status::OK(); - }; - // handle current graph's inputs - ORT_THROW_IF_ERROR(node.ForEachWithIndex(node.InputDefs(), process_input_fn)); - // nodes' implicit inputs also need to be collected. They need to - // be promoted to being explicit inputs for everything to work. - ORT_THROW_IF_ERROR(node.ForEachWithIndex(node.ImplicitInputDefs(), process_input_fn)); - - // Handle outouts - // two cases are considered as outputs - // 1. Output NodeArg is not used by any Node - // 2. Output NodeArg is used by at least one Node out of this subgraph. - // Note a NodeArg can be used by Nodes in and out of the subgraph at the same time. - // 3. Output NodeArg is one of real outputs of an Ort graph. - - auto InsertOutputToSubgraph = [&meta_def](const NodeArg* def) { - if (std::find(meta_def->outputs.begin(), meta_def->outputs.end(), def->Name()) == - meta_def->outputs.end()) { - meta_def->outputs.push_back(def->Name()); - } - }; - - InlinedHashSet input_names_from_the_output_node; - - for (auto o_iter = node.OutputEdgesBegin(); o_iter != node.OutputEdgesEnd(); ++o_iter) { - const auto& p = *o_iter; - const Node& out_node = p.GetNode(); - - // preprocess for the case 1 - ORT_THROW_IF_ERROR(out_node.ForEachWithIndex( - out_node.InputDefs(), - [&input_names_from_the_output_node](const onnxruntime::NodeArg& in_def, size_t) { - input_names_from_the_output_node.insert(in_def.Name()); - return Status::OK(); - })); - - // handle the case 2 - if (node_indices.count(out_node.Index()) == 0) { - const NodeArg* def = node.OutputDefs()[p.GetSrcArgIndex()]; - InsertOutputToSubgraph(def); - } - } - - // handle case 1 and 3 - ORT_THROW_IF_ERROR(node.ForEachWithIndex( - node.OutputDefs(), - [&](const onnxruntime::NodeArg& def, size_t) { - if (input_names_from_the_output_node.count(def.Name()) == 0 || - real_output_names.count(def.Name()) > 0) { - InsertOutputToSubgraph(&def); - } - return Status::OK(); - })); - } - - // Handle subgraph's initializers - const auto& all_initializers = graph.GetAllInitializedTensors(); - for (const auto& node_index : subgraph->nodes) { - const auto& node = *graph.GetNode(node_index); - // check whether it is an immediate nested subgraph - auto immediate_nested_subgraph = GetSubgraph(node); - // If so, copy the immediate nested subgraph's initializers to meta_def->inputs. - // Note we don't need recursion here, since Ort did recursion for us by handling subgraph early than the current graph. - // Therefore, the all inner nested subgraph's initializers should be already in the immediate nested subgraph's inputs. - if (nullptr != immediate_nested_subgraph) { - for (auto& n : immediate_nested_subgraph->Nodes()) { - auto add_input_fn = - [&meta_def, &all_initializers](const onnxruntime::NodeArg& def, size_t) { - auto iter = all_initializers.find(def.Name()); - if (iter != all_initializers.end()) { - meta_def->inputs.push_back(def.Name()); - } - return Status::OK(); - }; - ORT_THROW_IF_ERROR(n.ForEachWithIndex(n.InputDefs(), add_input_fn)); - ORT_THROW_IF_ERROR(n.ForEachWithIndex(n.ImplicitInputDefs(), add_input_fn)); - } - } - } - - meta_def->since_version = 1; - meta_def->status = ONNX_NAMESPACE::EXPERIMENTAL; - std::unique_ptr finished_subgraph(subgraph.release()); - finished_subgraph->SetMetaDef(std::move(meta_def)); - return std::make_unique(std::move(finished_subgraph)); -} - -int64_t ShapeRank(const NodeArg* def) { - ORT_ENFORCE_DEBUG(nullptr != def); - return gsl::narrow_cast(def->Shape()->dim_size()); -} - -bool ShapeHasValue(const NodeArg* def, int i) { - ORT_ENFORCE_DEBUG(nullptr != def); - ORT_ENFORCE_DEBUG(i >= 0); - ORT_ENFORCE_DEBUG(i < def->Shape()->dim_size()); - return utils::HasDimValue(def->Shape()->dim(i)); -} - -bool ShapeHasSymbol(const NodeArg* def, int i) { - ORT_ENFORCE_DEBUG(nullptr != def); - ORT_ENFORCE_DEBUG(i >= 0); - ORT_ENFORCE_DEBUG(i < def->Shape()->dim_size()); - return utils::HasDimParam(def->Shape()->dim(i)); -} - -int64_t ShapeValue(const NodeArg* def, int i) { - ORT_ENFORCE_DEBUG(ShapeHasValue(def, i)); - return def->Shape()->dim(i).dim_value(); -} - -const std::string& ShapeSymbol(const NodeArg* def, int i) { - ORT_ENFORCE_DEBUG(ShapeHasSymbol(def, i)); - return def->Shape()->dim(i).dim_param(); -} - -ONNX_NAMESPACE::TensorProto_DataType TensorProtoDataType(const NodeArg* def) { - ORT_ENFORCE_DEBUG(nullptr != def); - return static_cast(def->TypeAsProto()->tensor_type().elem_type()); -} - -// Convert GraphNodes to internal NodePtrs without check lifetime. -// Please use it only locally when GraphNodes still exist -InlinedVector ConvertGraphNodesToNodePtrs(const ConstGraphNodes& graph_nodes) { - InlinedVector nodes; - for (auto& node : graph_nodes) { - nodes.push_back(&node); - } - return nodes; -} - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/common.h b/onnxruntime/core/codegen/common/common.h deleted file mode 100644 index 81b74daf6f711..0000000000000 --- a/onnxruntime/core/codegen/common/common.h +++ /dev/null @@ -1,153 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/common/inlined_containers.h" -#include "core/framework/compute_capability.h" -#include "core/framework/tensor.h" -#include "core/graph/graph_nodes.h" -#include "core/graph/graph_viewer.h" - -#ifndef NDEBUG -#define ORT_ENFORCE_DEBUG(...) ORT_ENFORCE(__VA_ARGS__) -#else -#define ORT_ENFORCE_DEBUG(...) -#endif // !NDEBUG - -// DYN_PROMOTE is a simplified llvm::dyn_cast, which does not need RTTI -// DYN_PROMOTE is faster than dynamic_cast and also has smaller binary size -// Please use DYN_PROMOTE in a critical path. -#define DYN_PROMOTE(BASE) \ - template \ - inline const ToType* Promote(const BASE* base) { \ - if (ToType::IsType(base)) \ - return static_cast(base); \ - return nullptr; \ - } \ - \ - template \ - inline ToType* Promote(BASE* base) { \ - if (ToType::IsType(base)) \ - return static_cast(base); \ - return nullptr; \ - } \ - \ - template \ - inline ToType* Promote(const std::unique_ptr& base) { \ - if (ToType::IsType(base.get())) \ - return static_cast(base); \ - return nullptr; \ - } \ - \ - template \ - inline ToType* Promote(const std::shared_ptr& base) { \ - if (ToType::IsType(base.get())) \ - return static_cast(base); \ - return nullptr; \ - } - -// DYN_PROMOTE_BASE is a macro inserted in the base class to support DYN_PROMOTE -// TYPE_ID is required for DYN_PROMOTE and TYPE_ID is a enum class -// TYPE_ID_VAR is a corresponding variable name for in the base class -#define DYN_PROMOTE_BASE(BASE, TYPE_ID, TYPE_ID_VAR) \ - inline const TYPE_ID TypeID() const { \ - return TYPE_ID_VAR; \ - } \ - \ - static inline bool IsType(const BASE*) { \ - return true; \ - } - -// DYN_PROMOTE_DERIVED is a macro inserted in a derived class to support DYN_PROMOTE -// TYPE_ID is required for DYN_PROMOTE and TYPE_ID is a enum class -// TYPE_ID_VALUE is corresponding TYPE_ID::value of a derived class. -#define DYN_PROMOTE_DERIVED(BASE, TYPE_ID, TYPE_ID_VALUE) \ - static inline bool IsType(const BASE* base) { \ - ORT_ENFORCE_DEBUG(nullptr != base); \ - return base->TypeID() == TYPE_ID::TYPE_ID_VALUE; \ - } - -// DYNAMIC_PROMOTE is a dynamic_cast needing RTTI -// DYNAMIC_PROMOTE is usually slower than than DYN_PROMOTE. -// Please use DYNAMIC_PROMOTE in a non-critical path. -#define DYNAMIC_PROMOTE(BASE) \ - template \ - inline const X* Promote(const BASE* base) { \ - auto derived = dynamic_cast(base); \ - ORT_ENFORCE(nullptr != derived); \ - return derived; \ - } \ - \ - template \ - inline X* Promote(BASE* base) { \ - auto derived = dynamic_cast(base); \ - ORT_ENFORCE(nullptr != derived); \ - return derived; \ - } \ - \ - template \ - inline X* Promote(const std::unique_ptr& base) { \ - auto derived = dynamic_cast(base.get()); \ - ORT_ENFORCE(nullptr != derived); \ - return derived; \ - } \ - \ - template \ - inline X* Promote(const std::shared_ptr& base) { \ - auto derived = dynamic_cast(base.get()); \ - ORT_ENFORCE(nullptr != derived); \ - return derived; \ - } - -namespace onnxruntime { - -// Nodekey is used as a key for maps -using NodeKey = std::string; - -NodeKey GetKey(const onnxruntime::Node* node); -NodeKey GetKey(const onnxruntime::Node& node); -NodeKey GetKey(const onnxruntime::NodeArg* def); - -bool IsRecurrentNode(const onnxruntime::Node& node); - -bool IsAliasNode(const onnxruntime::Node& node); - -// Helper function that creates ComputeCapability for subgraphs -std::unique_ptr ToCapacity(const onnxruntime::GraphViewer& graph, - int fused_count, - std::unique_ptr& subgraph); - -bool IsFusedNode(const Node& node); - -bool HasLoop(const Node& node); - -const Graph* GetSubgraph(const Node& node); - -std::string NormalizeCppName(const std::string& name); - -std::string NormalizeNodeArgName(const NodeArg* def); - -// Return the corresponding input node for the NodeArg of the given node -const onnxruntime::Node* GetInputNode(const Node& node, const NodeArg* def); - -int64_t ShapeRank(const NodeArg* def); - -bool ShapeHasValue(const NodeArg* def, int i); - -bool ShapeHasSymbol(const NodeArg* def, int i); - -int64_t ShapeValue(const NodeArg* def, int i); - -const std::string& ShapeSymbol(const NodeArg* def, int i); - -ONNX_NAMESPACE::TensorProto_DataType TensorProtoDataType(const NodeArg* def); - -// Convert ConstGraphNodes to internal NodePtrs without check lifetime. -// Please use it only locally when GraphNodes still exist -InlinedVector ConvertGraphNodesToNodePtrs(const ConstGraphNodes& graph_nodes); - -enum : int { - Dimension_Unknown = -1, -}; - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/creator.h b/onnxruntime/core/codegen/common/creator.h deleted file mode 100644 index b31a12db4875b..0000000000000 --- a/onnxruntime/core/codegen/common/creator.h +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/dispatcher.h" - -// TODO rename this file to creator_base -namespace onnxruntime { -namespace codegen { - -// It is a base class for TVM Op IR builder, weight layout builder, TVM scheduler -// CreatorBase is a template class of compiler pass -// for 1) TVM IR builder -// 2) Weight layout transformer -// 3) TVM Scheduler, etc. -// CreatorBase is similor to OpXXCreate in llvm IR builder - -template -class CreatorBase { - public: - CreatorBase(const std::string& name) - : name_(name) {} - - virtual ~CreatorBase() = default; - - virtual RETURN_TYPE Evaluate(INPUT_TYPE, - NODE_TYPE, - CONTEXT_TYPE, - OUTPUT_TYPE) = 0; - - const std::string& Name() const { - return name_; - } - - protected: - std::string name_; - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(CreatorBase); -}; - -// macro to stringize -#define STRINGIZE_NX(OP) #OP -#define STRINGIZE(OP) STRINGIZE_NX(OP) - -// macro returns class name -#define CREATOR_CLASS(OP, POSTFIX) \ - OP##POSTFIX - -// macro returns class name as string -#define CREATOR_STRING(OP, POSTFIX) \ - STRINGIZE(CREATOR_CLASS(OP, POSTFIX)) - -// macro returns class constructor name -#define CREATOR_CLASS_FUNC(OP, POSTFIX) \ - OP##POSTFIX() - -// macro declares a creator class inheriting the template class CreatorBase -// with corresponding template parameters -#define DECLARE_CREATOR_CLASS(OP, POSTFIX, INPUT, NODE, CONTEXT, OUTPUT, RETURN) \ - class CREATOR_CLASS(OP, POSTFIX) : public onnxruntime::codegen::CreatorBase { \ - public: \ - CREATOR_CLASS_FUNC(OP, POSTFIX) : CreatorBase(CREATOR_STRING(OP, POSTFIX)) {} \ - RETURN Evaluate(INPUT, \ - NODE, \ - CONTEXT, \ - OUTPUT) override; \ - \ - private: \ - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(CREATOR_CLASS(OP, POSTFIX)); \ - }; - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/dispatcher.h b/onnxruntime/core/codegen/common/dispatcher.h deleted file mode 100644 index 80a854a06977c..0000000000000 --- a/onnxruntime/core/codegen/common/dispatcher.h +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/common/common.h" -#include -#include -#include - -namespace onnxruntime { -namespace codegen { - -// DispatcherBase is a customized unordered_map -// that provides all codegen-related functionality -// including 1) dispatching a pass -// 2) dump corresponding name -// DispatcherBase may or may not keep ownership, -// depending on the template parameter, CONTENT_TYPE. -// Note DispatcherBase has a protected destructor - -template -class DispatcherBase { - public: - DispatcherBase(const std::string& name) - : name_(name) {} - - const std::string& Name() const { - return name_; - } - - bool Contains(const std::string& name) const { - return contents_.count(name) > 0; - } - - void ForEach(std::function - func) { - for (auto& p : contents_) { - func(p.first, p.second); - } - } - - bool Register(const std::string& name, - CONTENT_TYPE op) { - if (!Contains(name)) { - contents_.emplace(name, op); - return true; - } - return false; - } - - CONTENT_TYPE Get(const std::string& key) const { - auto iter = contents_.find(key); - if (iter != contents_.end()) { - return iter->second; - } - return nullptr; - } - - const std::unordered_map GetContents() const { - return contents_; - } - - std::unordered_map GetMutableContents() { - return contents_; - } - - protected: - std::string name_; - std::unordered_map contents_; - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(DispatcherBase); - ~DispatcherBase() = default; -}; - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/dump_array.h b/onnxruntime/core/codegen/common/dump_array.h deleted file mode 100644 index 8e51cd36d0087..0000000000000 --- a/onnxruntime/core/codegen/common/dump_array.h +++ /dev/null @@ -1,62 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include -#include -#include -#include - -namespace onnxruntime { - -template -void DumpArrayRecursive(const T1* data, int64_t& data_offset, const std::vector& shape, int idx) { - int dim = static_cast(shape.size()); - if (dim == 0) { - std::cout << "[]\n"; - return; - } - - assert(idx < dim); - int sz = shape[idx]; - - std::cout << "["; - if (idx < dim - 1) { - for (auto i = 0; i < sz; ++i) { - DumpArrayRecursive(data, data_offset, shape, idx + 1); - if (i < sz - 1) { - std::cout << ","; - // print multiple newlines after ',' when necessary - for (int j = idx + 1; j < dim; j++) - std::cout << "\n"; - // print leading spaces before "[" when necessary - for (int j = 0; j < idx + 1; ++j) - std::cout << " "; - } - } - } else { - for (auto i = 0; i < sz; ++i) { - if (std::is_same::value || std::is_same::value) - std::cout << std::setw(3) << static_cast(*(data + data_offset)); - else - std::cout << std::setw(12) << std::setprecision(8) << *(data + data_offset); - data_offset++; - if (i < sz - 1) - std::cout << ","; - } - } - std::cout << "]"; -} - -// A helper function to dump multidimensional arrays in a way similar to numpy -template -void DumpArray(const std::string& tag, const T1* data, const std::vector& shape) { - std::cout << tag << "\n"; - int64_t data_offset = 0; - DumpArrayRecursive(data, data_offset, shape, 0); - assert(data_offset == TotalSize(shape)); - std::cout << std::endl; -} - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/handle.h b/onnxruntime/core/codegen/common/handle.h deleted file mode 100644 index 7caad27dcbe01..0000000000000 --- a/onnxruntime/core/codegen/common/handle.h +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/target_info.h" -#include -#include - -namespace onnxruntime { -namespace codegen { - -using DomainVersionLookupFunc = std::function; - -struct CodeGenHandle { - CodeGenTarget* codegen_target; - DomainVersionLookupFunc domain_version_lookup_func = - // by default, always uses the latest opset implemented - [](const std::string&) { return INT_MAX; }; -}; - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/op_macro.h b/onnxruntime/core/codegen/common/op_macro.h deleted file mode 100644 index 04305c4aa47b0..0000000000000 --- a/onnxruntime/core/codegen/common/op_macro.h +++ /dev/null @@ -1,101 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -namespace onnxruntime { - -#define LIST_BINARY_OPS() \ - BINARY_OP(Add) \ - BINARY_OP(Div) \ - BINARY_OP(Mul) \ - BINARY_OP(PRelu) \ - BINARY_OP(Sub) - -#define LIST_BINARY_CMP_OPS() \ - BINARY_CMP_OP(Equal) \ - BINARY_CMP_OP(Greater) \ - BINARY_CMP_OP(Less) - -#define LIST_POOL_OPS() \ - POOL_OP(MaxPool) \ - POOL_OP(AveragePool) \ - POOL_OP(GlobalMaxPool) \ - POOL_OP(GlobalAveragePool) - -#define LIST_REDUCE_OPS() \ - REDUCE_INDEXED_OP(ArgMax) \ - REDUCE_INDEXED_OP(ArgMin) \ - REDUCE_OP(ReduceL1) \ - REDUCE_OP(ReduceL2) \ - REDUCE_OP(ReduceLogSum) \ - REDUCE_OP(ReduceLogSumExp) \ - REDUCE_OP(ReduceMax) \ - REDUCE_OP(ReduceMean) \ - REDUCE_OP(ReduceMin) \ - REDUCE_OP(ReduceProd) \ - REDUCE_OP(ReduceSum) \ - REDUCE_OP(ReduceSumSquare) - -#define LIST_UNARY_OPS() \ - UNARY_OP(Abs) \ - UNARY_OP(Affine) \ - UNARY_OP(Ceil) \ - UNARY_OP(Elu) \ - UNARY_OP(Exp) \ - UNARY_OP(Floor) \ - UNARY_OP(HardSigmoid) \ - UNARY_OP(LeakyRelu) \ - UNARY_OP(Log) \ - UNARY_OP(Neg) \ - UNARY_OP(ParametricSoftplus) \ - UNARY_OP(Reciprocal) \ - UNARY_OP(Relu) \ - UNARY_OP(ScaledTanh) \ - UNARY_OP(Selu) \ - UNARY_OP(Sigmoid) \ - UNARY_OP(Softplus) \ - UNARY_OP(Softsign) \ - UNARY_OP(Sqrt) \ - UNARY_OP(Tanh) \ - UNARY_OP(ThresholdedRelu) - -#define LIST_VARIADIC_OPS() \ - VARIADIC_OP(Max) \ - VARIADIC_OP(Min) \ - VARIADIC_OP(Sum) - -#define LIST_ALL_GENERIC_OPS() \ - LIST_BINARY_OPS() \ - LIST_BINARY_CMP_OPS() \ - LIST_REDUCE_OPS() \ - LIST_POOL_OPS() \ - LIST_UNARY_OPS() \ - LIST_VARIADIC_OPS() \ - ADD_OP_ITEM(Cast) \ - ADD_OP_ITEM(Clip) \ - ADD_OP_ITEM(Concat) \ - ADD_OP_ITEM(Conv) \ - ADD_OP_ITEM(Crop) \ - ADD_OP_ITEM(Dropout) \ - ADD_OP_ITEM(Expand) \ - ADD_OP_ITEM(Flatten) \ - ADD_OP_ITEM(Gather) \ - ADD_OP_ITEM(GatherElements) \ - ADD_OP_ITEM(Gemm) \ - ADD_OP_ITEM(Identity) \ - ADD_OP_ITEM(LogSoftmax) \ - ADD_OP_ITEM(LSTM) \ - ADD_OP_ITEM(MatMul) \ - ADD_OP_ITEM(MatMulInteger) \ - ADD_OP_ITEM(Pad) \ - ADD_OP_ITEM(Reshape) \ - ADD_OP_ITEM(Shape) \ - ADD_OP_ITEM(Slice) \ - ADD_OP_ITEM(Softmax) \ - ADD_OP_ITEM(Split) \ - ADD_OP_ITEM(Squeeze) \ - ADD_OP_ITEM(Transpose) \ - ADD_OP_ITEM(Unsqueeze) \ - ADD_OP_ITEM(Where) - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/profile.h b/onnxruntime/core/codegen/common/profile.h deleted file mode 100644 index 31c9e764320d0..0000000000000 --- a/onnxruntime/core/codegen/common/profile.h +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -// uncomment this line or use -DCODEGEN_ENABLE_PROFILER in compiler options to enable profiler events in codegen -// #define CODEGEN_ENABLE_PROFILER - -#ifdef CODEGEN_ENABLE_PROFILER -#include "core/common/profiler.h" - -namespace onnxruntime { - -class ProfilerEvent { - public: - ProfilerEvent(const std::string& name) : name_(name) { - ts_ = profiling::Profiler::Instance().StartTime(); - } - - ~ProfilerEvent() { - profiling::Profiler::Instance().EndTimeAndRecordEvent(profiling::EventCategory::NODE_EVENT, name_, ts_); - } - - private: - TimePoint ts_; - const std::string name_; -}; - -} // namespace onnxruntime - -#define CODEGEN_PROFILER_EVENT(name) onnxruntime::ProfilerEvent profiler_event(name) - -#else - -#define CODEGEN_PROFILER_EVENT(name) - -#endif diff --git a/onnxruntime/core/codegen/common/registry.h b/onnxruntime/core/codegen/common/registry.h deleted file mode 100644 index c1642e76e2120..0000000000000 --- a/onnxruntime/core/codegen/common/registry.h +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/common/common.h" -#include -#include -#include - -namespace onnxruntime { -namespace codegen { - -// RegistryBase is a customized unordered_map -// that keep ownership of passes, -// including 1) IR builder passes -// 2) Weight layout transformer passes -// 3) Scheduler passses, etc. - -template -class RegistryBase { - public: - RegistryBase() = default; - - virtual ~RegistryBase() = default; - - bool Contains(const std::string& name) const { - return contents_.count(name) > 0; - } - - CONTENT_TYPE* Get(const std::string& name) const { - if (contents_.find(name) != contents_.end()) - return contents_.at(name).get(); - return nullptr; - } - - CONTENT_TYPE* RegisterOrGet( - const std::string& name, - std::unique_ptr&& ptr) { - if (!Contains(name)) - contents_.emplace(name, std::move(ptr)); - return Get(name); - } - - CONTENT_TYPE* RegisterOrGet( - std::unique_ptr&& ptr) { - return RegisterOrGet(ptr->Name(), std::move(ptr)); - } - - bool Register( - const std::string& name, - std::unique_ptr&& ptr) { - if (!Contains(name)) { - contents_.emplace(name, std::move(ptr)); - return true; - } - return false; - } - - bool Register( - std::unique_ptr&& ptr) { - return Register(ptr->Name(), std::move(ptr)); - } - - protected: - std::unordered_map> contents_; - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(RegistryBase); -}; - -// Put common Registry Management utilities if these is any - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/settings.cc b/onnxruntime/core/codegen/common/settings.cc deleted file mode 100644 index 529cb654f922c..0000000000000 --- a/onnxruntime/core/codegen/common/settings.cc +++ /dev/null @@ -1,78 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/common/settings.h" - -#include "core/common/logging/logging.h" -#include -#include - -namespace onnxruntime { -namespace codegen { - -CodeGenSettings& CodeGenSettings::Instance() { - static CodeGenSettings settings; - return settings; -} - -CodeGenSettings::CodeGenSettings() {} - -void CodeGenSettings::InsertOptions(const std::map& options) { - for (const auto& option : options) { - const auto& key = option.first; - const auto& value = option.second; - - auto iter = options_.find(key); - // found existing ones - if (iter != options_.end()) { - if (iter->second != value) { - LOGS_DEFAULT(CODEGEN_SETTINGS_LOG_LEVEL) << "CodeGenSettings: option" - << key << " is overridded from: " - << iter->second << " to: " << value; - iter->second = value; - } - } else { - options_.insert(std::make_pair(key, value)); - } - } -} - -void CodeGenSettings::DumpOptions() const { - std::ostringstream stream; - stream << "CodeGenSettings: dump all options" << std::endl; - for (const auto& option : options_) { - stream << " " << option.first << " = " << option.second << std::endl; - } - LOGS_DEFAULT(CODEGEN_SETTINGS_LOG_LEVEL) << stream.str(); -} - -std::string CodeGenSettings::GetOptionValue(const std::string& key) const { - const auto& iter = options_.find(key); - if (iter == options_.end()) { - LOGS_DEFAULT(CODEGEN_SETTINGS_LOG_LEVEL) << "CodeGenSettings::GetOptionValue: unrecognized option" << key; - return ""; - } - return iter->second; -} - -bool CodeGenSettings::HasOption(const std::string& key) const { - return options_.count(key) > 0; -} - -bool CodeGenSettings::OptionMatches(const std::string& key, const std::string& value) const { - if (!HasOption(key)) - return false; - -#ifdef _WIN32 - return 0 == _stricmp(options_.at(key).c_str(), value.c_str()); -#else - return 0 == strcasecmp(options_.at(key).c_str(), value.c_str()); -#endif -} - -void CodeGenSettings::Clear() { - options_.clear(); -} - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/settings.h b/onnxruntime/core/codegen/common/settings.h deleted file mode 100644 index e327b0e207cc2..0000000000000 --- a/onnxruntime/core/codegen/common/settings.h +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace codegen { - -// use log level warning as default to make sure logs are outputted -#define CODEGEN_SETTINGS_LOG_LEVEL WARNING - -// This stores codegen settings to control dumps, execution preference, etc. -// CodeGenSettings could come from command line options or environment variables -// Or could come from a static variables in source code -class CodeGenSettings { - public: - // generic built-in options - constexpr static const char* kDumpAllOptions = "dump_all_options"; - constexpr static const char* kCodeGenDumpModule = "codegen_dump_module"; // dump tvm module - constexpr static const char* kCodeGenDumpLower = "codegen_dump_lower"; // dump lowered func - constexpr static const char* kCodeGenDumpSchedule = "codegen_dump_schedule"; // dump scheduler - - void InsertOptions(const std::map& options); - void DumpOptions() const; - std::string GetOptionValue(const std::string& key) const; - bool HasOption(const std::string& key) const; - bool OptionMatches(const std::string& key, const std::string& value) const; - void Clear(); - static CodeGenSettings& Instance(); - - private: - CodeGenSettings(); - - std::map options_; -}; - -} // namespace codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/target_info.h b/onnxruntime/core/codegen/common/target_info.h deleted file mode 100644 index da063545f0a1e..0000000000000 --- a/onnxruntime/core/codegen/common/target_info.h +++ /dev/null @@ -1,33 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { - -// CodeGenTarget holds meta info for backend code generation -// and will be lowered to a target of corresponding backend -// code generation, e.g. TVM's Target. -class CodeGenTarget { - public: - CodeGenTarget() {} - CodeGenTarget(const std::string& target_name) - : target_name_(target_name) {} - - virtual int NaturalVectorWidth(int /*bits*/) const { - return 1; - } - - const std::string& GetTargetName() const { - return target_name_; - } - - virtual ~CodeGenTarget() = default; - - private: - std::string target_name_{"unknown"}; // default name is unknown -}; - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/utils.cc b/onnxruntime/core/codegen/common/utils.cc deleted file mode 100644 index f4140a411bddf..0000000000000 --- a/onnxruntime/core/codegen/common/utils.cc +++ /dev/null @@ -1,99 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/common/utils.h" -#include "core/common/cpuid_info.h" -#include "core/common/safeint.h" - -#include -#include - -namespace onnxruntime { - -std::unique_ptr GetEnv(const char* var) { - char* val = nullptr; -#if _MSC_VER - size_t len; - - if (_dupenv_s(&val, &len, var)) { - // Something went wrong, just return nullptr. - return nullptr; - } -#else - val = getenv(var); -#endif // _MSC_VER - - if (val == nullptr) { - return nullptr; - } - - // On windows, we will have to explicitly free val. Instead of returning val - // to its caller and make distinguish between windows and linux, we return - // a unique_ptr, and it will be destroyed automatically after the caller - // completes. - size_t len_val = strnlen(val, onnxruntime::kMaxStrLen) + 1; - auto p = std::make_unique(len_val); - // use explicit loop to get ride of VC's warning on unsafe copy - for (size_t i = 0; i < len_val; ++i) { - p[i] = val[i]; - } - return p; -} - -bool IsEnvVarDefined(const char* var) { - auto val = GetEnv(var); - return val != nullptr; -} - -int64_t TotalSize(const std::vector& shape) { - SafeInt total = 1; - for (auto s : shape) { - total *= s; - } - return total; -} - -// Return the strides for the input shape, i.e. the number of -// elements contained by a single element of current dimension. -// For example, for shape[3][4][5][6], strides will be -// [4*5*6, 5*6, 6, 1], i.e. [120, 30, 6, 1] -void GetStrides(const int64_t* shape, int ndim, std::vector& strides) { - strides.resize(ndim); - strides[ndim - 1] = 1; - for (int64_t i = ndim - 2; i >= 0; i--) { - strides[i] = strides[i + 1] * shape[i + 1]; - } -} - -// Common utils to get target option -TargetFeature GetTargetInfo(const codegen::CodeGenSettings& settings) { - TargetFeature feature; - - std::string target_str = ""; - - bool isAVX = false; - bool isAVX2 = false; - bool isAVX512 = false; - if (target_str == "avx") { - isAVX = true; - } else if (target_str == "avx2") { - isAVX = true; - isAVX2 = true; - } else if (target_str == "avx512") { - isAVX = true; - isAVX2 = true; - isAVX512 = true; - } else { - isAVX = CPUIDInfo::GetCPUIDInfo().HasAVX(); - isAVX2 = CPUIDInfo::GetCPUIDInfo().HasAVX2(); - isAVX512 = CPUIDInfo::GetCPUIDInfo().HasAVX512Skylake(); - } - - feature.hasAVX = isAVX; - feature.hasAVX2 = isAVX2; - feature.hasAVX512 = isAVX512; - - return feature; -} - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/common/utils.h b/onnxruntime/core/codegen/common/utils.h deleted file mode 100644 index ef06b5b72dc2c..0000000000000 --- a/onnxruntime/core/codegen/common/utils.h +++ /dev/null @@ -1,45 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/common/common.h" -#include -#include -#include - -namespace onnxruntime { - -// Holding utility functions that are not tied to TVM and ORT - -std::unique_ptr GetEnv(const char* var); - -// Check if an environment variable is set -bool IsEnvVarDefined(const char* var); - -int64_t TotalSize(const std::vector& shape); - -void GetStrides(const int64_t* shape, int ndim, std::vector& strides); - -struct TargetFeature { - bool hasAVX; - bool hasAVX2; - bool hasAVX512; -}; - -TargetFeature GetTargetInfo(const codegen::CodeGenSettings& setttings); - -// GCD (Greatest Common Divisor) -template -T GCD(T a, T b) { - ORT_ENFORCE(a >= 0); - ORT_ENFORCE(b >= 0); - if (a < b) std::swap(a, b); - if (b == 0) return a; - while (a % b != 0) { - a = a % b; - std::swap(a, b); - } - return b; -} - -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/common.h b/onnxruntime/core/codegen/mti/common.h deleted file mode 100644 index d71e740b9284a..0000000000000 --- a/onnxruntime/core/codegen/mti/common.h +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -#include -#include - -#define MTI_ASSERT(condition) \ - if (!(condition)) { \ - std::string error_msg = "Not satisfied: " #condition \ - ": line " + \ - std::to_string(__LINE__) + \ - " in file " + std::string(__FILE__) + "\n"; \ - throw std::runtime_error(error_msg); \ - } diff --git a/onnxruntime/core/codegen/mti/debug/tvm_print.cc b/onnxruntime/core/codegen/mti/debug/tvm_print.cc deleted file mode 100644 index 0491636032b47..0000000000000 --- a/onnxruntime/core/codegen/mti/debug/tvm_print.cc +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/debug/tvm_print.h" - -#include "core/codegen/common/utils.h" -#include "core/codegen/common/dump_array.h" -#include "core/codegen/mti/common.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -TVM_REGISTER_GLOBAL("tvm.contrib.onnxruntime.print") - .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* /*ret*/) { - DLTensor* X = args[0]; - DLTensor* Y = args[1]; - - DLDataType dtype = X->dtype; - std::vector shape; - int64_t total_size = 1; - for (int i = 0; i < X->ndim; ++i) { - shape.push_back(X->shape[i]); - total_size *= X->shape[i]; - } - - // pass X to Y - memcpy(static_cast(Y->data) + Y->byte_offset, - static_cast(X->data) + X->byte_offset, - total_size * dtype.bits / 8); - - if (tvm::runtime::TypeMatch(dtype, kDLFloat, 32)) { - float* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("float tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLInt, 8)) { - int8_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("int8 tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLInt, 16)) { - int16_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("int16 tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLInt, 32)) { - int32_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("int32 tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLUInt, 8)) { - uint8_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("uint8 tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLUInt, 16)) { - uint16_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("uint16 tensor:", data, shape); - } else if (tvm::runtime::TypeMatch(dtype, kDLUInt, 32)) { - uint32_t* data = reinterpret_cast(static_cast(X->data) + X->byte_offset); - DumpArray("uint32 tensor:", data, shape); - } else { - MTI_ASSERT(0 && "not implemented!"); - } - }); - -tvm::Array -PrintTVMTensorExtern(const tvm::Tensor& X, - const std::string& name) { - return topi::detail::make_extern( - {X->shape}, - {X->dtype}, - {X}, - [&](tvm::Array ins, tvm::Array outs) { - return topi::detail::call_packed({tvm::Expr("tvm.contrib.onnxruntime.print"), - topi::detail::pack_buffer(ins[0]), - topi::detail::pack_buffer(outs[0])}); - }, - name + "_print", "", {}); -} - -tvm::Tensor PrintImmutable(const tvm::Tensor& X) { - auto outputs = PrintTVMTensorExtern(X, X->op->name + "_print"); - return outputs[0]; -} - -void Print(tvm::Tensor& X) { - X = PrintImmutable(X); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/debug/tvm_print.h b/onnxruntime/core/codegen/mti/debug/tvm_print.h deleted file mode 100644 index 91a334785a2a4..0000000000000 --- a/onnxruntime/core/codegen/mti/debug/tvm_print.h +++ /dev/null @@ -1,19 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Array PrintTVMTensorExtern( - const tvm::Tensor& X, - const std::string& name = "PrintTVM2DTensorExtern"); - -tvm::Tensor PrintImmutable(const tvm::Tensor& X); - -void Print(tvm::Tensor& X); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/binary_ops.cc b/onnxruntime/core/codegen/mti/math/binary_ops.cc deleted file mode 100644 index f3048799458f4..0000000000000 --- a/onnxruntime/core/codegen/mti/math/binary_ops.cc +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/binary_ops.h" - -#include "core/codegen/mti/math/unary_ops.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/cast_ops.h" -#include - -// Using namespace topi for override operator +-*/ -using namespace topi; - -namespace onnxruntime { -namespace tvm_codegen { - -#define TVM_BINARY_OP1(op, expr) \ - tvm::Tensor op(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name) { \ - return Rename(expr, name); \ - } \ - tvm::Tensor op(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name) { \ - return Rename(expr, name); \ - } - -#define TVM_BINARY_OP(op, expr) \ - TVM_BINARY_OP1(op, expr) \ - tvm::Tensor op(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name) { \ - return Rename(expr, name); \ - } - -TVM_BINARY_OP(Add, lhs + rhs); -TVM_BINARY_OP(Div, lhs / rhs); -TVM_BINARY_OP(Max, maximum(lhs, rhs)); -TVM_BINARY_OP(Min, minimum(lhs, rhs)); -TVM_BINARY_OP(Mul, lhs* rhs); -TVM_BINARY_OP1(PRelu, Relu(lhs) - rhs * Relu(0 - lhs)); -TVM_BINARY_OP(Sub, lhs - rhs); - -tvm::Tensor Equal(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::equal(lhs, rhs, name); -} -tvm::Tensor Equal(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name) { - return topi::equal(lhs, rhs, name); -} -tvm::Tensor Equal(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::equal(lhs, rhs, name); -} - -tvm::Tensor Greater(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::greater(lhs, rhs, name); -} -tvm::Tensor Greater(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name) { - return topi::greater(lhs, rhs, name); -} -tvm::Tensor Greater(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::greater(lhs, rhs, name); -} - -tvm::Tensor Less(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::less(lhs, rhs, name); -} -tvm::Tensor Less(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name) { - return topi::less(lhs, rhs, name); -} -tvm::Tensor Less(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name) { - return topi::less(lhs, rhs, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/binary_ops.h b/onnxruntime/core/codegen/mti/math/binary_ops.h deleted file mode 100644 index dd51ce5e7917d..0000000000000 --- a/onnxruntime/core/codegen/mti/math/binary_ops.h +++ /dev/null @@ -1,42 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Add(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "add"); -tvm::Tensor Add(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "add"); -tvm::Tensor Add(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "add"); -tvm::Tensor Div(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "div"); -tvm::Tensor Div(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "div"); -tvm::Tensor Div(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "div"); -tvm::Tensor Equal(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "equal"); -tvm::Tensor Equal(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "equal"); -tvm::Tensor Equal(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "equal"); -tvm::Tensor Greater(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "greater"); -tvm::Tensor Greater(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "greater"); -tvm::Tensor Greater(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "greater"); -tvm::Tensor Less(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "less"); -tvm::Tensor Less(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "less"); -tvm::Tensor Less(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "less"); -tvm::Tensor Max(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "max"); -tvm::Tensor Max(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "max"); -tvm::Tensor Max(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "max"); -tvm::Tensor Min(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "min"); -tvm::Tensor Min(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "min"); -tvm::Tensor Min(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "min"); -tvm::Tensor Mul(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "mul"); -tvm::Tensor Mul(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "mul"); -tvm::Tensor Mul(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "mul"); -tvm::Tensor PRelu(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "prelu"); -tvm::Tensor PRelu(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "prelu"); -tvm::Tensor Sub(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name = "sub"); -tvm::Tensor Sub(const tvm::Tensor& lhs, const tvm::Expr& rhs, const std::string& name = "sub"); -tvm::Tensor Sub(const tvm::Expr& lhs, const tvm::Tensor& rhs, const std::string& name = "sub"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/gemm.cc b/onnxruntime/core/codegen/mti/math/gemm.cc deleted file mode 100644 index 7a79513ccaa97..0000000000000 --- a/onnxruntime/core/codegen/mti/math/gemm.cc +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/gemm.h" - -#include "core/codegen/mti/math/matmul_ops.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -// Using namespace topi for override operator +-*/ -using namespace topi; - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Gemm(const tvm::Tensor& A, const tvm::Tensor& B, const tvm::Tensor& C, - bool trans_A, bool trans_B, float alpha, float beta, - const std::string& name) { - auto A_dot_B = MatMul2D(A, B, trans_A, trans_B, name + "_matmul2d"); - tvm::Expr alphaExpr = tvm::make_const(A->dtype, alpha); - if (beta != 0) { - tvm::Expr betaExpr = tvm::make_const(A->dtype, beta); - return Rename(alphaExpr * A_dot_B + (betaExpr * C), name); - } else { - return Rename(alphaExpr * A_dot_B, name); - } -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/gemm.h b/onnxruntime/core/codegen/mti/math/gemm.h deleted file mode 100644 index 3bb205c13fdc9..0000000000000 --- a/onnxruntime/core/codegen/mti/math/gemm.h +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Gemm(const tvm::Tensor& p_A, const tvm::Tensor& p_B, const tvm::Tensor& p_C, - bool trans_A, bool trans_B, float alpha, float beta, - const std::string& name = "gemm"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/logsoftmax.cc b/onnxruntime/core/codegen/mti/math/logsoftmax.cc deleted file mode 100644 index cd8c2edae6959..0000000000000 --- a/onnxruntime/core/codegen/mti/math/logsoftmax.cc +++ /dev/null @@ -1,18 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/logsoftmax.h" - -#include "core/codegen/mti/tensor/reshape_ops.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor LogSoftmax(const tvm::Tensor& input, int64_t axis, const std::string& name) { - tvm::Tensor flatten_t = Flatten(input, axis, "logsoftmax_flatten"); - return Reshape(topi::nn::log_softmax(flatten_t, name), input->shape, "logsoftmax_reshape"); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/logsoftmax.h b/onnxruntime/core/codegen/mti/math/logsoftmax.h deleted file mode 100644 index 606a32806434b..0000000000000 --- a/onnxruntime/core/codegen/mti/math/logsoftmax.h +++ /dev/null @@ -1,11 +0,0 @@ -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor LogSoftmax(const tvm::Tensor& input, int64_t axis, const std::string& name = "logsoftmax"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/matmul_ops.cc b/onnxruntime/core/codegen/mti/math/matmul_ops.cc deleted file mode 100644 index 6ecf2f69a9c25..0000000000000 --- a/onnxruntime/core/codegen/mti/math/matmul_ops.cc +++ /dev/null @@ -1,161 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/matmul_ops.h" - -#include "core/codegen/mti/common.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor MatMul2D(const tvm::Tensor& A, const tvm::Tensor& B, bool trans_a, bool trans_b, const std::string& name) { - return topi::matmul(A, B, trans_a, trans_b, name); -} - -/* - * Generic Matrix Multiplication - * - * If both arguments are 2-D, they are multiplied like conventional matrices. - * - * If either argument is N-D and N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly. - * - * If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. - * After matrix multiplication the prepended 1 is removed. - * - * If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. - * After matrix multiplication the appended 1 is removed. - */ -tvm::Tensor MatMul(const tvm::Tensor& A, const tvm::Tensor& B, const std::string& name) { - int64_t a_rank = static_cast(A->shape.size()); - int64_t b_rank = static_cast(B->shape.size()); - const auto& A_shape = A->shape; - const auto& B_shape = B->shape; - if (a_rank == 2 && b_rank == 2) { - // 2-D X 2-D - return MatMul2D(A, B); - } else if (a_rank == 1 && b_rank == 1) { - // 1-D X 1-D - auto k = tvm::reduce_axis(tvm::Range(0, A_shape[0]), "k"); - - return tvm::compute( - {}, - [&](const tvm::Array& /*indices*/) { - return tvm::sum(A[k] * B[k], {k}); - }, - name); - } else if (a_rank == 1) { - // 1-D X n-D - auto k = tvm::reduce_axis(tvm::Range(0, A_shape[0]), "k"); - - auto l = [&](const tvm::Array& indices) { - auto ndims = indices.size(); - MTI_ASSERT(ndims >= 1); - tvm::Array b_indices; - for (size_t bi = 0; bi < ndims - 1; ++bi) { - b_indices.push_back(indices[bi]); - } - b_indices.push_back(k); - b_indices.push_back(indices[ndims - 1]); - return tvm::sum(A({k}) * B(b_indices), {k}); - }; - return tvm::compute(ConcatShapes(SliceShapeToDimension(B_shape, -2), SliceShapeFromDimension(B_shape, -1)), l, name); - } else if (b_rank == 1) { - // n-D X 1-D - auto k = tvm::reduce_axis(tvm::Range(0, B_shape[0]), "k"); - - auto l = [&](const tvm::Array& indices) { - tvm::Array a_indices(indices.begin(), indices.end()); - a_indices.push_back(k); - return tvm::sum(A(a_indices) * B({k}), {k}); - }; - return tvm::compute(SliceShapeToDimension(A->shape, -1), l, name); - } else { - // n-D X m-D - MTI_ASSERT(a_rank >= 2 && b_rank >= 2); - auto k = tvm::reduce_axis(tvm::Range(0, A_shape[a_rank - 1]), "k"); - - auto l = [&](const tvm::Array& indices) { - auto ndims = static_cast(indices.size()); - MTI_ASSERT(ndims > 2); - tvm::Array a_indices, b_indices; - - // handle broadcasting - int i = 0, a_idx = 0, b_idx = 0; - bool a_greater = a_rank > b_rank; - for (; i < std::abs(a_rank - b_rank); ++i) { - if (a_greater) { - a_indices.push_back(indices[i]); - a_idx++; - } else { - b_indices.push_back(indices[i]); - b_idx++; - } - } - for (; i < ndims - 2; ++i, ++a_idx, ++b_idx) { - auto tp = indices[i].type(); - if (IsOne(A_shape, a_idx)) { - a_indices.push_back(tvm::make_zero(tp)); - b_indices.push_back(indices[i]); - } else if (IsOne(B_shape, b_idx)) { - b_indices.push_back(tvm::make_zero(tp)); - a_indices.push_back(indices[i]); - } else { - a_indices.push_back(indices[i]); - b_indices.push_back(indices[i]); - } - } - - MTI_ASSERT(a_idx == a_rank - 2 && b_idx == b_rank - 2); - a_indices.push_back(indices[ndims - 2]); - a_indices.push_back(k); - - b_indices.push_back(k); - b_indices.push_back(indices[ndims - 1]); - - return tvm::sum(A(a_indices) * B(b_indices), {k}); - }; - - return tvm::compute(ComputeMatMulShape(A_shape, B_shape), l, name); - } -} - -tvm::Array -ComputeMatMulShape( - const tvm::Array& A_shape, - const tvm::Array& B_shape, - bool trans_a, - bool trans_b) { - auto a_rank = A_shape.size(); - auto b_rank = B_shape.size(); - tvm::Array output_shape; - int64_t output_rank = std::max(a_rank, b_rank); - MTI_ASSERT(a_rank > 0 && b_rank > 0); - if (a_rank == 1 && b_rank == 1) { - MTI_ASSERT(!trans_a && !trans_b); - // reduction, output shape is empty - } else if (a_rank == 1) { - MTI_ASSERT(!trans_a && !trans_b); - output_shape = SliceShapeToDimension(B_shape, b_rank - 2); - output_shape.push_back(B_shape[b_rank - 1]); - } else if (b_rank == 1) { - MTI_ASSERT(!trans_a && !trans_b); - output_shape = SliceShapeToDimension(A_shape, a_rank - 1); - } else { - for (int64_t i = 0; i < output_rank - 2; i++) { - tvm::Expr broadcasted_dim = tvm::make_const(HalideIR::Int(32), 1); - bool broadcasted = - BroadcastDim(A_shape, i, output_rank, broadcasted_dim) && - BroadcastDim(B_shape, i, output_rank, broadcasted_dim); - MTI_ASSERT(broadcasted); - output_shape.push_back(broadcasted_dim); - } - output_shape.push_back(A_shape[a_rank - (trans_a ? 1 : 2)]); - output_shape.push_back(B_shape[b_rank - (trans_b ? 2 : 1)]); - } - return output_shape; -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/matmul_ops.h b/onnxruntime/core/codegen/mti/math/matmul_ops.h deleted file mode 100644 index ab9986132d34a..0000000000000 --- a/onnxruntime/core/codegen/mti/math/matmul_ops.h +++ /dev/null @@ -1,23 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Array -ComputeMatMulShape( - const tvm::Array& A_shape, - const tvm::Array& B_shape, - bool trans_a = false, - bool trans_b = false); - -tvm::Tensor MatMul2D(const tvm::Tensor& A, const tvm::Tensor& B, bool trans_a = false, bool trans_b = false, const std::string& name = "matmul2d"); - -tvm::Tensor MatMul(const tvm::Tensor& A, const tvm::Tensor& B, const std::string& name = "matmul"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/reduce_ops.cc b/onnxruntime/core/codegen/mti/math/reduce_ops.cc deleted file mode 100644 index 7d179e2b04316..0000000000000 --- a/onnxruntime/core/codegen/mti/math/reduce_ops.cc +++ /dev/null @@ -1,90 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/reduce_ops.h" - -#include "core/codegen/mti/math/binary_ops.h" -#include "core/codegen/mti/math/unary_ops.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor ArgMax(const tvm::Tensor& X, int64_t axis, bool keep_dims, const std::string& name) { - return Rename(topi::argmax(X, ToTvmArrayInt({axis}), keep_dims), name); -} - -tvm::Tensor ArgMin(const tvm::Tensor& X, int64_t axis, bool keep_dims, const std::string& name) { - return Rename(topi::argmin(X, ToTvmArrayInt({axis}), keep_dims), name); -} - -tvm::Tensor ReduceL1(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return ReduceSum(Abs(X), axes, keep_dims, name); -} - -tvm::Tensor ReduceL2(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Sqrt(ReduceSumSquare(X, axes, keep_dims), name); -} - -tvm::Tensor ReduceLogSum(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Log(ReduceSum(X, axes, keep_dims), name); -} - -tvm::Tensor ReduceLogSumExp(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - tvm::Tensor reduce_max = ReduceMax(X, axes, true); - tvm::Tensor exp_delta = Exp(Sub(X, reduce_max)); - tvm::Tensor reduce_max_keep_dims = ReduceMax(X, axes, keep_dims); - return Add(ReduceLogSum(exp_delta, axes, keep_dims), reduce_max_keep_dims, name); -} - -tvm::Tensor ReduceMax(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Rename(topi::max(X, ToTvmArrayInt(axes), keep_dims), name); -} - -tvm::Tensor ReduceMean(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - tvm::Tensor reduce_sum = ReduceSum(X, axes, keep_dims); - tvm::Expr count = tvm::make_const(reduce_sum->dtype, 1.0f); - if (axes.empty()) { - for (const auto& dim : X->shape) - count = count * dim; - } else { - for (int64_t axis : axes) { - int64_t i = HandleNegativeAxis(axis, X->shape.size()); - count = count * X->shape[i]; - } - } - return tvm::compute( - reduce_sum->shape, - [&](const tvm::Array& i) { - return reduce_sum(i) / count; - }, - name); -} - -tvm::Tensor ReduceMin(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Rename(topi::min(X, ToTvmArrayInt(axes), keep_dims), name); -} - -tvm::Tensor ReduceProd(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - auto prod = [](tvm::Expr source, tvm::Array rdom) { - tvm::Var x("x", source.type()), y("y", source.type()); - tvm::Expr Rename_element = tvm::make_const(source.type(), 1.0f); - tvm::ir::CommReducer combiner = - tvm::ir::CommReducerNode::make({x}, {y}, {x * y}, {Rename_element}); - return tvm::ir::Reduce::make(combiner, {source}, rdom, tvm::make_const(tvm::Bool(1), true), 0); - }; - - return Rename(topi::CommReduce(X, ToTvmArrayInt(axes), prod, keep_dims, true), name); -} - -tvm::Tensor ReduceSum(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Rename(topi::sum(X, ToTvmArrayInt(axes), keep_dims), name); -} - -tvm::Tensor ReduceSumSquare(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name) { - return Rename(topi::sum(Mul(X, X), ToTvmArrayInt(axes), keep_dims), name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/reduce_ops.h b/onnxruntime/core/codegen/mti/math/reduce_ops.h deleted file mode 100644 index f782df5e6515f..0000000000000 --- a/onnxruntime/core/codegen/mti/math/reduce_ops.h +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor ArgMax(const tvm::Tensor& X, - int64_t axis, - bool keep_dims, - const std::string& name = "argmax"); - -tvm::Tensor ArgMin(const tvm::Tensor& X, - int64_t axis, - bool keep_dims, - const std::string& name = "argmin"); - -tvm::Tensor ReduceL1(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_l1"); - -tvm::Tensor ReduceL2(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_l2"); - -tvm::Tensor ReduceLogSum(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_log_sum"); - -tvm::Tensor ReduceLogSumExp(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "argmareduce_log_sum_exp"); - -tvm::Tensor ReduceMax(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_max"); - -tvm::Tensor ReduceMean(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_mean"); - -tvm::Tensor ReduceMin(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_min"); - -tvm::Tensor ReduceProd(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_prod"); - -tvm::Tensor ReduceSum(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_sum"); - -tvm::Tensor ReduceSumSquare(const tvm::Tensor& X, - const std::vector& axes, - bool keep_dims, - const std::string& name = "reduce_sum_square"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/softmax.cc b/onnxruntime/core/codegen/mti/math/softmax.cc deleted file mode 100644 index d7404137bb873..0000000000000 --- a/onnxruntime/core/codegen/mti/math/softmax.cc +++ /dev/null @@ -1,18 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/softmax.h" - -#include "core/codegen/mti/tensor/reshape_ops.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Softmax(const tvm::Tensor& input, int64_t axis, const std::string& name) { - tvm::Tensor flatten_t = Flatten(input, axis, "softmax_flatten"); - return Reshape(topi::nn::softmax(flatten_t, 1, name), input->shape, "softmax_reshape"); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/softmax.h b/onnxruntime/core/codegen/mti/math/softmax.h deleted file mode 100644 index fb16fbaeb56a2..0000000000000 --- a/onnxruntime/core/codegen/mti/math/softmax.h +++ /dev/null @@ -1,11 +0,0 @@ -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Softmax(const tvm::Tensor& input, int64_t axis, const std::string& name = "softmax"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/unary_ops.cc b/onnxruntime/core/codegen/mti/math/unary_ops.cc deleted file mode 100644 index ae732ea33e670..0000000000000 --- a/onnxruntime/core/codegen/mti/math/unary_ops.cc +++ /dev/null @@ -1,155 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/math/unary_ops.h" - -#include "core/codegen/common/settings.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include -#include -#include - -// Using namespace topi for override operator +-*/ -using namespace topi; - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Abs(const tvm::Tensor& X, const std::string& name) { - return abs(X, name); -} - -tvm::Tensor Affine(const tvm::Tensor& X, float alpha, float beta, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - tvm::Expr betaExpr = tvm::make_const(X->dtype, beta); - return Rename(alphaExpr * X + betaExpr, name); -} - -tvm::Tensor Ceil(const tvm::Tensor& X, const std::string& name) { - return topi::ceil(X, name); -} - -tvm::Tensor Clip(const tvm::Tensor& X, tvm::Expr min_value, tvm::Expr max_value, const std::string& name) { - auto Y = tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - return tvm::min(tvm::max(X(indices), min_value), max_value); - }, - name); - return Y; -} - -tvm::Tensor Elu(const tvm::Tensor& X, float alpha, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - return Rename(Relu(X) - alphaExpr * Relu(1 - Exp(X)), name); -} - -tvm::Tensor Exp(const tvm::Tensor& X, const std::string& name) { - return tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - return tvm::exp(X(indices)); - }, - name); -} - -tvm::Tensor Floor(const tvm::Tensor& X, const std::string& name) { - return topi::floor(X, name); -} - -tvm::Tensor HardSigmoid(const tvm::Tensor& X, float alpha, float beta, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - tvm::Expr betaExpr = tvm::make_const(X->dtype, beta); - return maximum(0, minimum(1, alphaExpr * X + betaExpr), name); -} - -tvm::Tensor LeakyRelu(const tvm::Tensor& X, float alpha, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - return Rename(Relu(X) - alphaExpr * Relu(0 - X), name); -} - -tvm::Tensor Log(const tvm::Tensor& X, const std::string& name) { - return tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - return tvm::log(X(indices)); - }, - name); -} - -tvm::Tensor Neg(const tvm::Tensor& X, const std::string& name) { - return negative(X, name); -} - -tvm::Tensor ParametricSoftplus(const tvm::Tensor& X, float alpha, float beta, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - tvm::Expr betaExpr = tvm::make_const(X->dtype, beta); - return Rename(alphaExpr * Softplus(betaExpr * X), name); -} - -tvm::Tensor Reciprocal(const tvm::Tensor& X, const std::string& name) { - return Rename(1 / X, name); -} - -tvm::Tensor Relu(const tvm::Tensor& X, const std::string& name) { - return maximum(X, 0, name); -} - -tvm::Tensor ScaledTanh(const tvm::Tensor& X, float alpha, float beta, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - tvm::Expr betaExpr = tvm::make_const(X->dtype, beta); - return Rename(alphaExpr * Tanh(betaExpr * X), name); -} - -tvm::Tensor Selu(const tvm::Tensor& X, float alpha, float gamma, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - tvm::Expr gammaExpr = tvm::make_const(X->dtype, gamma); - return Rename(gammaExpr * (-alphaExpr * Relu(1 - Exp(X)) + Relu(X)), name); -} - -tvm::Tensor Sigmoid(const tvm::Tensor& X, const std::string& name) { - return tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - return tvm::ir::Select::make(X(indices) > 0, - 1 / (1 + tvm::exp(-X(indices))), - tvm::exp(X(indices)) / (tvm::exp(X(indices)) + 1)); - }, - name); -} - -tvm::Tensor SignNoZero(const tvm::Tensor& X, const std::string& name) { - return Rename(greater_equal(X, 0) * 2 - 1, name); -} - -tvm::Tensor Softplus(const tvm::Tensor& X, const std::string& name) { - return Rename(Log(1 + Exp(Neg(Abs(X)))) + Relu(X), name); -} - -tvm::Tensor Softsign(const tvm::Tensor& X, const std::string& name) { - return Rename(X / (1 + Abs(X)), name); -} - -tvm::Tensor Sqrt(const tvm::Tensor& X, const std::string& name) { - return sqrt(X, name); -} - -tvm::Tensor Tanh(const tvm::Tensor& X, const std::string& name) { - return tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - return tvm::ir::Select::make(X(indices) < 0, - (tvm::exp(2 * X(indices)) - 1) / (tvm::exp(2 * X(indices)) + 1), - (1 - tvm::exp(-2 * X(indices))) / (1 + tvm::exp(-2 * X(indices)))); - }, - name); -} - -tvm::Tensor ThresholdedRelu(const tvm::Tensor& X, float alpha, const std::string& name) { - tvm::Expr alphaExpr = tvm::make_const(X->dtype, alpha); - return topi::where(greater(X, alphaExpr), X, topi::full_like(X, tvm::make_zero(X->dtype)), name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/math/unary_ops.h b/onnxruntime/core/codegen/mti/math/unary_ops.h deleted file mode 100644 index aeb336262e547..0000000000000 --- a/onnxruntime/core/codegen/mti/math/unary_ops.h +++ /dev/null @@ -1,36 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Abs(const tvm::Tensor& X, const std::string& name = "abs"); -tvm::Tensor Affine(const tvm::Tensor& X, float alpha, float beta, const std::string& name = "affine"); -tvm::Tensor Ceil(const tvm::Tensor& X, const std::string& name = "ceil"); -tvm::Tensor Clip(const tvm::Tensor& X, tvm::Expr min_value, tvm::Expr max_value, const std::string& name = "clip"); -tvm::Tensor Elu(const tvm::Tensor& X, float alpha, const std::string& name = "elu"); -tvm::Tensor Exp(const tvm::Tensor& X, const std::string& name = "exp"); -tvm::Tensor Floor(const tvm::Tensor& X, const std::string& name = "floor"); -tvm::Tensor HardSigmoid(const tvm::Tensor& X, float alpha, float beta, const std::string& name = "hard_sigmoid"); -tvm::Tensor LeakyRelu(const tvm::Tensor& X, float alpha, const std::string& name = "leaky_relu"); -tvm::Tensor Log(const tvm::Tensor& X, const std::string& name = "log"); -tvm::Tensor Neg(const tvm::Tensor& X, const std::string& name = "neg"); -tvm::Tensor ParametricSoftplus(const tvm::Tensor& X, float alpha, float beta, const std::string& name = "parametric_softplus"); -tvm::Tensor Reciprocal(const tvm::Tensor& X, const std::string& name = "reciprocal"); -tvm::Tensor Relu(const tvm::Tensor& X, const std::string& name = "relu"); -tvm::Tensor ScaledTanh(const tvm::Tensor& X, float alpha, float beta, const std::string& name = "scaled_tanh"); -tvm::Tensor Selu(const tvm::Tensor& X, float alpha, float gamma, const std::string& name = "selu"); -tvm::Tensor Sigmoid(const tvm::Tensor& X, const std::string& name = "sigmoid"); -tvm::Tensor SignNoZero(const tvm::Tensor& X, const std::string& name = "sign_no_zero"); -tvm::Tensor Softplus(const tvm::Tensor& X, const std::string& name = "softplus"); -tvm::Tensor Softsign(const tvm::Tensor& X, const std::string& name = "softsign"); -tvm::Tensor Sqrt(const tvm::Tensor& X, const std::string& name = "sqrt"); -tvm::Tensor Tanh(const tvm::Tensor& X, const std::string& name = "tanh"); -tvm::Tensor ThresholdedRelu(const tvm::Tensor& X, float alpha, const std::string& name = "thresholded_relu"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/mti_tvm_utils.cc b/onnxruntime/core/codegen/mti/mti_tvm_utils.cc deleted file mode 100644 index 8e73629c05614..0000000000000 --- a/onnxruntime/core/codegen/mti/mti_tvm_utils.cc +++ /dev/null @@ -1,203 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/mti_tvm_utils.h" - -#include "core/codegen/common/settings.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Array ToTvmArray(gsl::span shape) { - tvm::Array arr; - for (size_t i = 0; i < shape.size(); ++i) { - arr.push_back(tvm::Expr(static_cast(shape[i]))); - } - return arr; -} - -tvm::Array ToTvmArrayInt(gsl::span shape) { - tvm::Array arr; - for (size_t i = 0; i < shape.size(); ++i) { - arr.push_back(shape[i]); - } - return arr; -} - -tvm::Expr SizeToDimension(const tvm::Array& shape, int64_t axis) { - tvm::Expr size(1); - auto rank = shape.size(); - if (static_cast(axis) != rank) { - axis = HandleNegativeAxis(axis, rank); - } - for (size_t d = 0; d < std::min(rank, static_cast(axis)); ++d) - size = tvm::ir::Simplify(size * shape[d]); - return size; -} - -tvm::Expr SizeFromDimension(const tvm::Array& shape, int64_t axis) { - tvm::Expr size(1); - auto rank = shape.size(); - if (static_cast(axis) != rank) { - axis = HandleNegativeAxis(axis, rank); - } - for (size_t d = static_cast(axis); d < rank; ++d) - size = tvm::ir::Simplify(size * shape[d]); - return size; -} - -tvm::Expr RoundUp(tvm::Expr value, tvm::Expr alignment) { - return tvm::ir::Simplify((value + alignment - 1) / alignment * alignment); -} - -tvm::Array ConcatShapes( - const tvm::Array& shape1, - const tvm::Array& shape2) { - tvm::Array result; - for (size_t i = 0; i < shape1.size(); i++) - result.push_back(shape1[i]); - for (size_t i = 0; i < shape2.size(); i++) - result.push_back(shape2[i]); - return result; -} - -tvm::Tensor Rename(tvm::Tensor X, const std::string& name) { - const_cast(X->op->name) = name; - return X; -} - -tvm::Array SliceShape(const tvm::Array& shape, const std::vector& axes) { - tvm::Array new_shape; - for (auto axis : axes) { - CHECK(axis < static_cast(shape.size())); - new_shape.push_back(shape[axis]); - } - return new_shape; -} - -tvm::Array SliceShapeFromDimension(const tvm::Array& shape, int64_t axis) { - int64_t rank = static_cast(shape.size()); - axis = HandleNegativeAxis(axis, rank); - std::vector axes; - for (auto i = axis; i < rank; ++i) - axes.push_back(i); - return SliceShape(shape, axes); -} - -tvm::Array SliceShapeToDimension(const tvm::Array& shape, int64_t axis) { - int64_t rank = static_cast(shape.size()); - axis = HandleNegativeAxis(axis, rank); - std::vector axes; - for (auto i = 0; i < axis; ++i) - axes.push_back(i); - return SliceShape(shape, axes); -} - -bool IsOne(const tvm::Array& shape, int64_t axis) { - int64_t rank = static_cast(shape.size()); - axis = HandleNegativeAxis(axis, rank); - const auto& dim = shape[axis]; - auto* p = tvm::as_const_int(dim); - return p != nullptr && *p == 1; -} - -tvm::Tensor Promote(const tvm::Expr& expr, const tvm::Array& shape, const std::string& name) { - return tvm::compute( - shape, - [&](const tvm::Array&) { - return expr; - }, - name); -} - -void DumpTVMModuleToFile(const std::string& filename, tvm::runtime::Module& module) { - const codegen::CodeGenSettings& settings = codegen::CodeGenSettings::Instance(); - if (!settings.HasOption(codegen::CodeGenSettings::kCodeGenDumpModule)) - return; - - // ISSUE: note that all option values are converted to lower case. It doesn't cause - // any issue currently, because all supported formats (i.e. file exts) are of lower case. - // Just keep in mind that we might have issue if somehow we started to support dump - // formats with upper case, although it's quite unlikely. - std::string format = settings.GetOptionValue(codegen::CodeGenSettings::kCodeGenDumpModule); - std::string module_filename = filename + "." + format; - module->SaveToFile(module_filename, format); -} - -tvm::Tensor MakeZeroTensor(const tvm::Array& shape, - HalideIR::Type type, - const std::string& name) { - auto l = [&](const tvm::Array& /*indices*/) { - return tvm::make_zero(type); - }; - return tvm::compute(shape, l, name); -} - -bool BroadcastDim(const tvm::Array& shape, size_t i, size_t output_rank, tvm::Expr& dim) { - if (i >= output_rank - shape.size()) { - auto new_dim = shape[shape.size() - output_rank + i]; - if (tvm::ir::Equal(new_dim, dim)) - return true; - - const int64_t* p_new = tvm::as_const_int(new_dim); - if (p_new != nullptr && *p_new == 1) { - return true; - } else { - const int64_t* p_old = tvm::as_const_int(dim); - if (p_old != nullptr && *p_old == 1) { - dim = new_dim; - return true; - } - } - return false; - } - // auto broadcast to outer dims - return true; -} - -tvm::Array MakeInputsForExtern(const tvm::Array& inputs, const std::string& name) { - // note that currently TVM StorageFlatten creates strides like max(symbolic_dim, 1) - // which is not zero when checking symbolic_dim - max(symbolic_dim, 1) - // then triggers error like: Trying to bind compact buffer to strided one - // here's a workaround to reshape inputs to avoid that - tvm::Array fixed_inputs; - for (size_t idx_input = 0; idx_input < inputs.size(); ++idx_input) { - const auto& input = inputs[idx_input]; - tvm::Array fixed_shape; - if (input->shape.size() > 0) { - // stride compute does not use dim 0, so directly push to fixed_shape - fixed_shape.push_back(input->shape[0]); - bool need_fix = false; - for (size_t idx_dim = 1; idx_dim < input->shape.size(); ++idx_dim) { - const auto& dim = input->shape[idx_dim]; - if (tvm::as_const_int(dim) == nullptr) { - fixed_shape.push_back(tvm::max(dim, tvm::make_const(HalideIR::Int(32), 1))); - need_fix = true; - } else { - fixed_shape.push_back(dim); - } - } - if (need_fix) { - fixed_inputs.push_back(tvm_codegen::Reshape(input, fixed_shape, name + "_" + std::to_string(idx_input))); - continue; - } - } - // no fix needed - fixed_inputs.push_back(input); - } - return fixed_inputs; -} - -// Make sure idx is clamped in the range of [-bound, bound - 1] -tvm::Expr ClampIndex(const tvm::Expr& idx, const tvm::Expr& bound) { - // when idx >= 0, we take tvm::max(..., 0), because (idx < 0) is 0 - // when idx < 0, we take bound + tvm::max(...), because tvm::max(idx, 0) is 0 - return tvm::max(tvm::min(idx, bound - 1), 0) + - (idx < 0) * (bound + tvm::max(idx, -bound)); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/mti_tvm_utils.h b/onnxruntime/core/codegen/mti/mti_tvm_utils.h deleted file mode 100644 index c2a14106c1686..0000000000000 --- a/onnxruntime/core/codegen/mti/mti_tvm_utils.h +++ /dev/null @@ -1,71 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -#include -#include -#include -#include -#include "core/codegen/mti/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Array ToTvmArray(gsl::span shape); - -tvm::Array ToTvmArrayInt(gsl::span shape); - -// Helper function to compute sub shape size to axis (not included) -tvm::Expr SizeToDimension(const tvm::Array& shape, int64_t axis); - -// Helper function to compute sub shape size from axis (included) -tvm::Expr SizeFromDimension(const tvm::Array& shape, int64_t axis); - -// Helper function to align -tvm::Expr RoundUp(tvm::Expr value, tvm::Expr alignment); - -tvm::Array ConcatShapes( - const tvm::Array& shape1, - const tvm::Array& shape2); - -// Helper function to rename tvm::Tensor -tvm::Tensor Rename(tvm::Tensor X, const std::string& name); - -// Helper function to slice TVM shape -tvm::Array SliceShape(const tvm::Array& shape, const std::vector& axes); - -// Helper function to slice TVM shape from axis (inclusive). -// Basically, this function returns the shape of [axis, shape.size()-1] -tvm::Array SliceShapeFromDimension(const tvm::Array& shape, int64_t axis); - -// this function returns the shape of [0, axis-1] -tvm::Array SliceShapeToDimension(const tvm::Array& shape, int64_t axis); - -// check if dimension is 1 -bool IsOne(const tvm::Array& shape, int64_t axis); - -// Helper function to convert tvm::Expr to tvm::Tensor -tvm::Tensor Promote(const tvm::Expr& expr, - const tvm::Array& shape, - const std::string& name = "PromoteExpr"); - -tvm::Tensor MakeZeroTensor(const tvm::Array& shape, HalideIR::Type type, const std::string& name); - -void DumpTVMModuleToFile(const std::string& filename, tvm::runtime::Module& module); - -bool BroadcastDim(const tvm::Array& shape, size_t i, size_t output_rank, tvm::Expr& dim); - -inline int64_t HandleNegativeAxis(int64_t axis, int64_t rank) { - MTI_ASSERT(axis >= -rank && axis <= rank - 1); - return axis = axis < 0 ? (axis + rank) : axis; -} - -// Make sure idx is clamped in the range of [-bound, bound - 1] -tvm::Expr ClampIndex(const tvm::Expr& idx, const tvm::Expr& bound); - -// Helper function to workaround tvm ExternOp issue when input has symbolic dimensions -tvm::Array MakeInputsForExtern(const tvm::Array& inputs, const std::string& name = "make_inputs_for_extern"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/conv_ops.cc b/onnxruntime/core/codegen/mti/nn/conv_ops.cc deleted file mode 100644 index e2d4acc8843ad..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/conv_ops.cc +++ /dev/null @@ -1,193 +0,0 @@ -#include "core/codegen/mti/nn/conv_ops.h" - -#include "core/codegen/mti/math/matmul_ops.h" -#include "core/codegen/mti/tensor/pad_ops.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include "core/codegen/mti/tensor/transpose.h" - -namespace onnxruntime { -namespace tvm_codegen { - -static tvm::Tensor PadTensor1D(const tvm::Tensor& input, - const tvm::Array& padding, - size_t width_axis, - const std::string& name) { - auto pad_left = padding[0]; - auto pad_right = padding[1]; - - tvm::Array pad_before(std::vector(input->shape.size(), 0)); - pad_before.Set(width_axis, pad_left); - tvm::Array pad_after(std::vector(input->shape.size(), 0)); - pad_after.Set(width_axis, pad_right); - - const int64_t* padding_w0 = tvm::as_const_int(pad_left); - const int64_t* padding_w1 = tvm::as_const_int(pad_right); - - const bool do_pad = ((padding_w0 != nullptr && *padding_w0) || - (padding_w1 != nullptr && *padding_w1)); - - return do_pad ? Pad(input, pad_before, pad_after, - 0, "constant", name + "_input_padded") - : input; -} - -tvm::Tensor Conv1D(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& out_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name) { - size_t channel_axis = 1; - size_t width_axis = 2; - - auto stride_width = stride[width_axis - 2]; - - auto input_padded = PadTensor1D(input, padding, width_axis, name); - auto rc = tvm::reduce_axis((tvm::Range(0, filter->shape[1])), "rc"); - auto rx = tvm::reduce_axis((tvm::Range(0, filter->shape[2])), "rx"); - - return tvm::compute( - out_shape, - [&](const tvm::Array& output) { - tvm::Array indices; - for (const tvm::Var& var : output) { - indices.push_back(var); - } - indices.Set(channel_axis, rc); - indices.Set(width_axis, output[width_axis] * stride_width + rx); - - return tvm::sum(input_padded(indices) * filter({output[1], rc, rx}), - {rc, rx}); - }, - name); -} - -tvm::Tensor Conv2D(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& output_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name) { - return Conv2D_native(input, filter, output_shape, stride, padding); -} - -static tvm::Tensor PadTensor2D(const tvm::Tensor& input, - const tvm::Array& padding, - size_t height_axis, - size_t width_axis, - const std::string& name) { - auto pad_top = padding[0]; - auto pad_left = padding[1]; - auto pad_bottom = padding[2]; - auto pad_right = padding[3]; - - tvm::Array pad_before(std::vector(input->shape.size(), 0)); - pad_before.Set(height_axis, pad_top); - pad_before.Set(width_axis, pad_left); - - tvm::Array pad_after(std::vector(input->shape.size(), 0)); - pad_after.Set(height_axis, pad_bottom); - pad_after.Set(width_axis, pad_right); - - const int64_t* padding_h0 = tvm::as_const_int(pad_top); - const int64_t* padding_w0 = tvm::as_const_int(pad_left); - const int64_t* padding_h1 = tvm::as_const_int(pad_bottom); - const int64_t* padding_w1 = tvm::as_const_int(pad_right); - - const bool do_pad = ((padding_h0 != nullptr && *padding_h0) || - (padding_w0 != nullptr && *padding_w0)) || - ((padding_h1 != nullptr && *padding_h1) || - (padding_w1 != nullptr && *padding_w1)); - - return do_pad ? Pad(input, pad_before, pad_after, - 0, "constant", name + "_input_padded") - : input; -} - -tvm::Tensor Conv2D_native(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& out_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name) { - size_t channel_axis = 1; - size_t height_axis = 2; - size_t width_axis = 3; - - auto stride_height = stride[height_axis - 2]; - auto stride_width = stride[width_axis - 2]; - - auto input_padded = PadTensor2D(input, padding, height_axis, width_axis, name); - - auto rc = tvm::reduce_axis((tvm::Range(0, filter->shape[1])), "rc"); - auto ry = tvm::reduce_axis((tvm::Range(0, filter->shape[2])), "ry"); - auto rx = tvm::reduce_axis((tvm::Range(0, filter->shape[3])), "rx"); - - return tvm::compute( - out_shape, - [&](const tvm::Array& output) { - tvm::Array indices; - for (const tvm::Var& var : output) { - indices.push_back(var); - } - indices.Set(channel_axis, rc); - indices.Set(height_axis, output[height_axis] * stride_height + ry); - indices.Set(width_axis, output[width_axis] * stride_width + rx); - - return tvm::sum(input_padded(indices) * filter({output[1], rc, ry, rx}), - {rc, ry, rx}); - }, - name); -} - -tvm::Tensor Conv2D_gemm(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& out_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name) { - size_t height_axis = 2; - size_t width_axis = 3; - - auto stride_height = stride[height_axis - 2]; - auto stride_width = stride[width_axis - 2]; - - auto input_padded = PadTensor2D(input, padding, height_axis, width_axis, name); - - tvm::Array img_col_tmp(std::vector(6, 0)); - img_col_tmp.Set(0, out_shape[0]); - img_col_tmp.Set(1, out_shape[2]); - img_col_tmp.Set(2, out_shape[3]); - img_col_tmp.Set(3, filter->shape[1]); - img_col_tmp.Set(4, filter->shape[2]); - img_col_tmp.Set(5, filter->shape[3]); - - auto img_col = tvm::compute( - img_col_tmp, - [&](const tvm::Array& output) { - tvm::Array indices; - indices.push_back(output[0]); - indices.push_back(output[3]); - indices.push_back(output[1] * stride_height + output[4]); - indices.push_back(output[2] * stride_width + output[5]); - return input_padded(indices); - }, - name); - - tvm::Array input_col_shape(std::vector(2, 0)); - input_col_shape.Set(0, img_col_tmp[1] * img_col_tmp[2]); - input_col_shape.Set(1, img_col_tmp[3] * img_col_tmp[4] * img_col_tmp[5]); - auto input_col = Reshape(img_col, input_col_shape); - - tvm::Array filter_row_shape(std::vector(2, 0)); - filter_row_shape.Set(0, filter->shape[0]); - filter_row_shape.Set(1, filter->shape[1] * filter->shape[2] * filter->shape[3]); - auto filter_row = Reshape(filter, filter_row_shape, name); - - auto Y = MatMul2D(input_col, filter_row, false, true, name); - auto Y_T = Transpose(Y, /*axes=*/{}, name); - return Reshape(Y_T, out_shape, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/conv_ops.h b/onnxruntime/core/codegen/mti/nn/conv_ops.h deleted file mode 100644 index 1396c216865a7..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/conv_ops.h +++ /dev/null @@ -1,39 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Conv1D(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& output_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name = "conv1d"); - -tvm::Tensor Conv2D(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& output_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name = "conv2d"); - -tvm::Tensor Conv2D_native(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& output_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name = "conv2d_native"); - -tvm::Tensor Conv2D_gemm(const tvm::Tensor& input, - const tvm::Tensor& filter, - const tvm::Array& output_shape, - const tvm::Array& stride, - const tvm::Array& padding, - const std::string& name = "conv2d_gemm"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/lstm.cc b/onnxruntime/core/codegen/mti/nn/lstm.cc deleted file mode 100644 index 1148b0924e869..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/lstm.cc +++ /dev/null @@ -1,140 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/nn/lstm.h" - -#include "core/codegen/mti/math/binary_ops.h" -#include "core/codegen/mti/math/unary_ops.h" -#include "core/codegen/mti/math/matmul_ops.h" -#include "core/codegen/mti/math/reduce_ops.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include "core/codegen/mti/tensor/split.h" - -namespace onnxruntime { -namespace tvm_codegen { - -/* -`X` - input tensor -`i` - input gate -`o` - output gate -`f` - forget gate -`c` - cell gate -`t` - time step (t-1 means previous time step) - -`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates -`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates -`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates -`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates -`P[iof]` - P peephole weight vector for input, output, and forget gates -`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates -`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates -`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates -`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates -`PB[iof]` - P peephole weight vector for backward input, output, and forget gates - -`H` - Hidden state -`num_directions` - 2 if direction == bidirectional else 1 - -Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) -*/ - -void LSTM_cell( - const LSTMAttributes& lstm_attrs, - const tvm::Tensor& X, - const tvm::Tensor& W, - const tvm::Tensor& R, - const tvm::Tensor& B, - bool has_B, - const tvm::Tensor& prev_H, - const tvm::Tensor& prev_C, - const tvm::Tensor& P, - bool has_P, - tvm::Tensor& Y_h, - tvm::Tensor& Y_c) { - // Input projection: Xt*(W[iofc]^T) for forward direction or Xt*(WB[iofc]^T) for reverse direction - // (batch_size, input_size) * trans(4 * hidden_size, input_size) => (batch_size, 4 * hidden_size) - tvm::Tensor input_proj = MatMul2D(X, W, /*trans_a*/ false, /*trans_b*/ true); - - // Hidden projection: Ht-1*(R[iofc]^T) for forward direction or Ht-1*(RB[iofc]^T) for reverse direction - // (batch_size, hidden_size) * trans(4 * hidden_size, hidden_size) => (batch_size, 4 * hidden_size) - tvm::Tensor hidden_proj = MatMul2D(prev_H, R, /*trans_a*/ false, /*trans_b*/ true); - - // (batch_size, 4 * hidden_size) - tvm::Tensor sum_proj = Add(input_proj, hidden_proj); - - // Concatenation of [Wb[iofc], Rb[iofc]] or [WBb[iofc], RBb[iofc]] - if (has_B) { - // (8 * hidden_size) -> (2, 4 * hidden_size) -> (1, 4 * hidden_size), should be done in const folding - tvm::Tensor reduce_B = - ReduceSum(Reshape(B, {2, 4 * static_cast(lstm_attrs.hidden_size)}), {0}, /*keep_dims*/ true); - // (batch_size, 4 * hidden_size) via broadcasting reduce_B - sum_proj = Add(sum_proj, reduce_B); - } - - std::vector iofc_sum_split_sizes(4, lstm_attrs.hidden_size); - // Split sum_proj into iofc, where each gate proj is of (batch_size, hidden_size) - tvm::Array iofc_sum_projs = Split(sum_proj, ToTvmArray(iofc_sum_split_sizes), /*axis*/ 1); - MTI_ASSERT(iofc_sum_projs.size() == 4); - tvm::Tensor i_proj = iofc_sum_projs[0], - o_proj = iofc_sum_projs[1], - f_proj = iofc_sum_projs[2], - c_proj = iofc_sum_projs[3]; - - tvm::Tensor P_i, P_o, P_f; - if (has_P) { - std::vector iof_p_split_sizes(3, lstm_attrs.hidden_size); - // Split P into P_i, P_o, P_f, in const pre-processing (P_i, P_f might be merged?) - // where each P_[iof] has the shape of (hidden_size) - tvm::Array iof_P_projs = Split(P, ToTvmArray(iof_p_split_sizes), /*axis*/ 0); - MTI_ASSERT(iof_P_projs.size() == 3); - P_i = iof_P_projs[0], - P_o = iof_P_projs[1], - P_f = iof_P_projs[2]; - - // (batch_size, hidden_size) via broadcasting P_[if] - i_proj = Add(i_proj, Mul(P_i, prev_C)); - f_proj = Add(f_proj, Mul(P_f, prev_C)); - } - - // TODO: handle more general cases for activations f, h, g and activation_alpha and - // activation_beta. We may consider to move some code such as ActivationInfo from deep_cpu_lstm - // into a common header file, because the code can be used here. - - // Note that by default f = Sigmoid, g = Tanh, h = Tanh - - // it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - // shape: (batch_size, hidden_size) - tvm::Tensor i_t = Sigmoid(i_proj); - // ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - // shape: (batch_size, hidden_size) - tvm::Tensor f_t = Sigmoid(f_proj); - // ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - // shape: (batch_size, hidden_size) - tvm::Tensor c_t = Tanh(c_proj); - - // Ct = ft (.) Ct-1 + it (.) ct - // shape: (batch_size, hidden_size) - Y_c = Add(Mul(f_t, prev_C), Mul(i_t, c_t), Y_c->op->name); - - // ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - // shape: (batch_size, hidden_size) - if (has_P) { - o_proj = Add(o_proj, Mul(P_o, Y_c)); - } - // ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - // shape: (batch_size, hidden_size) - o_proj = Sigmoid(o_proj); - // Ht = ot (.) h(Ct) - // shape: (batch_size, hidden_size) - Y_h = Mul(o_proj, Tanh(Y_c), Y_h->op->name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/lstm.h b/onnxruntime/core/codegen/mti/nn/lstm.h deleted file mode 100644 index 851fa880c4427..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/lstm.h +++ /dev/null @@ -1,35 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -// A bubble now. But don't remove it -// TODO: refactor the LSTMcell building to a tvm function -// and move it here - -namespace onnxruntime { -namespace tvm_codegen { - -struct LSTMAttributes { - LSTMAttributes(int64_t hidden_size_p) : hidden_size(hidden_size_p) {} - int64_t hidden_size; -}; - -void LSTM_cell( - const LSTMAttributes& lstm_attrs, - const tvm::Tensor& X, - const tvm::Tensor& W, - const tvm::Tensor& R, - const tvm::Tensor& B, - bool has_B, - const tvm::Tensor& prev_H, - const tvm::Tensor& prev_C, - const tvm::Tensor& P, - bool has_P, - tvm::Tensor& Y_h, - tvm::Tensor& Y_c); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/pool_ops.cc b/onnxruntime/core/codegen/mti/nn/pool_ops.cc deleted file mode 100644 index 868a14748cabc..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/pool_ops.cc +++ /dev/null @@ -1,63 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/nn/pool_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/mlas/inc/mlas.h" -#include "core/providers/cpu/nn/pool_attributes.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// TODO: topi only support 2d-pool, MaxPool1d and MaxPool3d will need to be added if necessary. -// only support version < 8 for topi doesn't come with implementation to output index tensor -tvm::Tensor MaxPool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& /*output_shape*/, - const std::string& /*name*/) { - return topi::nn::pool(input, - ToTvmArray(pool_attrs.kernel_shape), - ToTvmArray(pool_attrs.strides), - ToTvmArray(pool_attrs.pads), - /*pool_type*/ topi::nn::kMaxPool, - /*ceil_mode*/ false, - /*layout*/ pool_attrs.storage_order == 0 ? "NCWH" : "NCHW", - pool_attrs.count_include_pad); -} - -tvm::Tensor AveragePool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& /*output_shape*/, - const std::string& /*name*/) { - return topi::nn::pool(input, - ToTvmArray(pool_attrs.kernel_shape), - ToTvmArray(pool_attrs.strides), - ToTvmArray(pool_attrs.pads), - /*pool_type*/ topi::nn::kAvgPool, - /*ceil_mode*/ false, - /*layout*/ "NCHW", - pool_attrs.count_include_pad); -} - -tvm::Tensor GlobalMaxPool(const tvm::Tensor& input, - const PoolAttributes& /*pool_attrs*/, - const tvm::Array& /*output_shape*/, - const std::string& /*name*/) { - return topi::nn::global_pool(input, - /*pool_type*/ topi::nn::kMaxPool, - /*layout*/ "NCHW"); -} - -tvm::Tensor GlobalAveragePool(const tvm::Tensor& input, - const PoolAttributes& /*pool_attrs*/, - const tvm::Array& /*output_shape*/, - const std::string& /*name*/) { - return topi::nn::global_pool(input, - /*pool_type*/ topi::nn::kAvgPool, - /*layout*/ "NCHW"); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/nn/pool_ops.h b/onnxruntime/core/codegen/mti/nn/pool_ops.h deleted file mode 100644 index d381f9ddff859..0000000000000 --- a/onnxruntime/core/codegen/mti/nn/pool_ops.h +++ /dev/null @@ -1,36 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { - -// Forward declaration -struct PoolAttributes; - -namespace tvm_codegen { - -tvm::Tensor MaxPool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& output_shape, - const std::string& name = "max_pool"); - -tvm::Tensor AveragePool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& output_shape, - const std::string& name = "average_pool"); - -tvm::Tensor GlobalMaxPool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& output_shape, - const std::string& name = "global_max_pool"); - -tvm::Tensor GlobalAveragePool(const tvm::Tensor& input, - const PoolAttributes& pool_attrs, - const tvm::Array& output_shape, - const std::string& name = "global_average_pool"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/cast_ops.cc b/onnxruntime/core/codegen/mti/tensor/cast_ops.cc deleted file mode 100644 index a8fc86488d82b..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/cast_ops.cc +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/cast_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Cast(const tvm::Tensor& X, tvm::Type type, const std::string& name) { - return topi::cast(X, type, name); -} - -// handle cases where bool is reprented as uint8 (e.g. in ONNX). -tvm::Tensor CastToUInt8Bool(const tvm::Tensor& X, const std::string& name) { - return tvm::compute( - X->shape, - [&](const tvm::Array& indices) { - auto val = X(indices); - // A special cast from float16 to bool, first cast up to float32, - // to workaround a float16 bug in many TVM backends. - // Intel Skylake is one of them. https://github.com/dmlc/tvm/issues/2959 - // TODO: remove it, after TVM is fixed - if (X->dtype == HalideIR::Float(16)) - val = tvm::cast(HalideIR::Float(32), val); - return tvm::ir::Select::make(topi::equal(val, tvm::make_zero(val.type())), - tvm::make_zero(HalideIR::UInt(8)), - tvm::make_const(HalideIR::UInt(8), 1)); - }, - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/cast_ops.h b/onnxruntime/core/codegen/mti/tensor/cast_ops.h deleted file mode 100644 index 02f6f9cb1fde7..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/cast_ops.h +++ /dev/null @@ -1,15 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Cast(const tvm::Tensor& X, tvm::Type type, const std::string& name = "cast"); -tvm::Tensor CastToUInt8Bool(const tvm::Tensor& X, const std::string& name = "cast_uint8_bool"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/concat_ops.cc b/onnxruntime/core/codegen/mti/tensor/concat_ops.cc deleted file mode 100644 index 3394d5b7e00a2..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/concat_ops.cc +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/concat_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Concat(const tvm::Array& inputs, - int64_t axis, - const std::string& name) { - return ConcatSafe(inputs, axis, name); -} - -// Note topi's implementation requires control flow within iterations to avoid out-of-bound access. -// Therefore, MTI implements a ConcatSafe that does not have out-of-bound access, -// and does not requires control or predicate. -tvm::Tensor ConcatSafe(const tvm::Array& inputs, - int64_t axis, - const std::string& name) { - axis = HandleNegativeAxis(axis, gsl::narrow(inputs[0]->shape.size())); - MTI_ASSERT(axis < gsl::narrow(inputs[0]->shape.size()) && "axis out of bounds"); - - tvm::Array axis_sizes; - for (auto t : inputs) { - axis_sizes.push_back(t->shape[axis]); - } - - tvm::Expr join_size = axis_sizes[0]; - for (size_t i = 1; i < axis_sizes.size(); ++i) { - join_size += axis_sizes[i]; - } - join_size = tvm::ir::Simplify(join_size); - tvm::Array out_shape; - for (size_t i = 0; i < inputs[0]->shape.size(); ++i) { - out_shape.push_back(i == gsl::narrow(axis) ? join_size : inputs[0]->shape[i]); - } - - return tvm::compute( - out_shape, [&](const tvm::Array& ovars) { - tvm::Array indices; - - // preset - tvm::Expr min = 0; - tvm::Expr extent = axis_sizes[0]; - tvm::Expr offset = 0; - tvm::Expr ret; - - // input i = 0 - for (size_t j = 0; j < ovars.size(); ++j) { - if (j == gsl::narrow(axis)) { - tvm::Expr ivar = ovars[j]; - indices.push_back(tvm::max(tvm::min(ivar, min + extent - 1), min)); - } else { - indices.push_back(ovars[j]); - } - } - ret = inputs[0](indices); - - for (size_t i = 1; i < inputs.size(); ++i) { - offset += extent; - tvm::Expr min = 0; - extent = axis_sizes[i]; - auto j = gsl::narrow(axis); - tvm::Expr ivar = ovars[j] - offset; - indices.Set(j, tvm::max(tvm::min(ivar, min + extent - 1), min)); - - ret = tvm::ir::Select::make(ivar >= 0, - inputs[i](indices), - ret); - } - - return ret; - }, - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/concat_ops.h b/onnxruntime/core/codegen/mti/tensor/concat_ops.h deleted file mode 100644 index 153afebb44615..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/concat_ops.h +++ /dev/null @@ -1,15 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Concat(const tvm::Array& inputs, int64_t axis, const std::string& name = "concat"); -tvm::Tensor ConcatSafe(const tvm::Array& inputs, int64_t axis, const std::string& name = "concat_safe"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/crop.cc b/onnxruntime/core/codegen/mti/tensor/crop.cc deleted file mode 100644 index 3fe569100df12..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/crop.cc +++ /dev/null @@ -1,58 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/crop.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Crop(const tvm::Tensor& t, - const tvm::Array& border, - const tvm::Array& scale, - const std::string& name) { - MTI_ASSERT(t->shape.size() == 4); - tvm::Expr N = t->shape[0]; - tvm::Expr C = t->shape[1]; - tvm::Expr H = t->shape[2]; - tvm::Expr W = t->shape[3]; - - MTI_ASSERT(border.size() == 4); - tvm::Expr leftBorder = border[0]; - tvm::Expr topBorder = border[1]; - tvm::Expr rightBorder = border[2]; - tvm::Expr bottomBorder = border[3]; - - tvm::Expr bottomLimit = H - bottomBorder; - tvm::Expr rightLimit = W - rightBorder; - - if (!scale.empty()) { - CHECK_EQ(scale.size(), 2); - bottomLimit = topBorder + scale[0]; - rightLimit = leftBorder + scale[1]; - } - - tvm::Array output_shape; - output_shape.push_back(tvm::ir::Simplify(N)); - output_shape.push_back(tvm::ir::Simplify(C)); - output_shape.push_back(tvm::ir::Simplify(bottomLimit - topBorder)); - output_shape.push_back(tvm::ir::Simplify(rightLimit - leftBorder)); - - auto l = [&](const tvm::Array& ovars) { - tvm::Array indices; - - indices.push_back(tvm::min(ovars[0], output_shape[0] - 1)); - indices.push_back(tvm::min(ovars[1], output_shape[1] - 1)); - indices.push_back(tvm::min(topBorder + ovars[2], topBorder + output_shape[2] - 1)); - indices.push_back(tvm::min(leftBorder + ovars[3], leftBorder + output_shape[3] - 1)); - - return t(indices); - }; - - return tvm::compute(output_shape, l, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/crop.h b/onnxruntime/core/codegen/mti/tensor/crop.h deleted file mode 100644 index ffb6a05c70504..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/crop.h +++ /dev/null @@ -1,17 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Crop(const tvm::Tensor& t, - const tvm::Array& border, - const tvm::Array& scale = {}, - const std::string& name = "crop"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/expand.cc b/onnxruntime/core/codegen/mti/tensor/expand.cc deleted file mode 100644 index cdac4f56e1f9f..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/expand.cc +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/expand.h" -#include "core/codegen/mti/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Expand(const tvm::Tensor& X, const tvm::Array& new_shape, const std::string& name) { - MTI_ASSERT(new_shape.size() >= X->shape.size()); - return tvm::compute( - new_shape, - [&](const tvm::Array& out_indices) { - tvm::Array indices; - size_t broadcasted_rank = new_shape.size() - X->shape.size(); - for (size_t d = broadcasted_rank; d < new_shape.size(); ++d) { - if (tvm::is_const_int(X->shape[d - broadcasted_rank], 1)) { - indices.push_back(tvm::make_zero(HalideIR::Int(32))); - } else { - indices.push_back(out_indices[d]); - } - } - return X(indices); - }, - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/expand.h b/onnxruntime/core/codegen/mti/tensor/expand.h deleted file mode 100644 index d66d41aeb0194..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/expand.h +++ /dev/null @@ -1,14 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Expand(const tvm::Tensor& X, const tvm::Array& new_shape, const std::string& name = "expand"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/gather.cc b/onnxruntime/core/codegen/mti/tensor/gather.cc deleted file mode 100644 index 152b3981f1623..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/gather.cc +++ /dev/null @@ -1,55 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/gather.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Gather(const tvm::Tensor& t, - int64_t axis, - const tvm::Tensor& indices, - const std::string& name) { - // handle negative axis - axis = HandleNegativeAxis(axis, gsl::narrow(t->shape.size())); - size_t axis_t = gsl::narrow(axis); - - tvm::Array output_shape; - for (size_t i = 0; i < axis_t; ++i) - output_shape.push_back(t->shape[i]); - - for (size_t i = 0; i < indices->shape.size(); ++i) - output_shape.push_back(indices->shape[i]); - - for (size_t i = axis_t + 1; i < t->shape.size(); ++i) - output_shape.push_back(t->shape[i]); - - tvm::Expr idx_upper_bound = t->shape[axis_t]; - auto l = [&](const tvm::Array& ovars) { - tvm::Array ivars; - for (size_t i = 0; i < t->shape.size(); ++i) { - if (i < axis_t) { - ivars.push_back(ovars[i]); - } else if (i == axis_t) { - tvm::Array idx_vars; - for (size_t d = 0; d < indices->shape.size(); ++d) - idx_vars.push_back(ovars[axis_t + d]); - // make sure idx is clamped in the range of [-idx_upper_bound, idx_upper_bound - 1] - tvm::Expr real_idx = tvm_codegen::ClampIndex(indices(idx_vars), idx_upper_bound); - ivars.push_back(tvm::cast(tvm::Int(32), real_idx)); // tvm indices must be Int32 - } else { - ivars.push_back(ovars[i - 1 + indices->shape.size()]); - } - } - return t(ivars); - }; - - return tvm::compute(output_shape, l, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/gather.h b/onnxruntime/core/codegen/mti/tensor/gather.h deleted file mode 100644 index a44bf3e4127d5..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/gather.h +++ /dev/null @@ -1,17 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Gather(const tvm::Tensor& t, - int64_t axis, - const tvm::Tensor& indices, - const std::string& name = "gather"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/gather_elements.cc b/onnxruntime/core/codegen/mti/tensor/gather_elements.cc deleted file mode 100644 index 12d2983335890..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/gather_elements.cc +++ /dev/null @@ -1,45 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/gather_elements.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor GatherElements(const tvm::Tensor& t, - int64_t axis, - const tvm::Tensor& indices, - const std::string& name) { - tvm::Array output_shape; - int64_t indices_rank = static_cast(indices->shape.size()); - // output shape is the same as indices - for (int64_t i = 0; i < indices_rank; ++i) - output_shape.push_back(indices->shape[i]); - - tvm::Expr idx_upper_bound = t->shape[axis]; - auto l = [&](const tvm::Array& ovars) { - tvm::Array ivars; - for (int64_t i = 0; i < indices_rank; i++) { - if (i == axis) { - tvm::Array idx_vars; - for (int64_t j = 0; j < indices_rank; j++) - idx_vars.push_back(ovars[j]); - // make sure idx is clamped in the range of [-idx_upper_bound, idx_upper_bound - 1] - tvm::Expr real_idx = tvm_codegen::ClampIndex(indices(idx_vars), idx_upper_bound); - // tvm idx must be of Int(32) - ivars.push_back(tvm::cast(tvm::Int(32), real_idx)); - } else { - ivars.push_back(ovars[i]); - } - } - return t(ivars); - }; - - return tvm::compute(output_shape, l, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/gather_elements.h b/onnxruntime/core/codegen/mti/tensor/gather_elements.h deleted file mode 100644 index 650086f0f2e87..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/gather_elements.h +++ /dev/null @@ -1,17 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor GatherElements(const tvm::Tensor& t, - int64_t axis, - const tvm::Tensor& indices, - const std::string& name = "gather_elements"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/pad_ops.cc b/onnxruntime/core/codegen/mti/tensor/pad_ops.cc deleted file mode 100644 index 2f688290d109e..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/pad_ops.cc +++ /dev/null @@ -1,121 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/pad_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// Note topi::pad does not support modes {edge, reflect} -// Therefore, MTI implements a generic Pad -tvm::Tensor Pad(const tvm::Tensor& t, - const tvm::Array& pad_before, - const tvm::Array& pad_after, - float pad_value, - const std::string& mode, - const std::string& name) { - MTI_ASSERT(pad_before.size() >= 1); - MTI_ASSERT(pad_before.size() == pad_after.size()); - MTI_ASSERT(pad_before.size() == t->shape.size()); - - tvm::Array output_shape; - for (size_t i = 0; i < t->shape.size(); ++i) { - output_shape.push_back( - tvm::ir::Simplify(t->shape[i] + pad_before[i] + pad_after[i])); - } - - auto l = [&](const tvm::Array& ovars) { - tvm::Array conds; - tvm::Array indices; - tvm::Array coords; - - for (size_t i = 0; i < t->shape.size(); ++i) { - tvm::Expr ivar = ovars[i] - pad_before[i]; - tvm::Expr min = 0; - tvm::Expr extent = t->shape[i]; - - conds.push_back(ivar < min); - conds.push_back(ivar >= min + extent); - indices.push_back(tvm::max(tvm::min(ivar, min + extent - 1), min)); - - if (mode == "reflect") { - // calculate indices for reflect mode - tvm::Expr limit = extent - 1; - tvm::Expr coord = ivar - min; - // Avoid mod zero when tensor shape has 1, - // e.g. input shape is [1, 3, 3] instead of [3, 3] - auto* p_limit = tvm::as_const_int(limit); - if (p_limit != nullptr && *p_limit != 0) - coord = (coord + 2 * limit) % (2 * limit); // avoid negative value - coord = coord - limit; - coord = tvm::abs(coord); - coord = limit - coord; - coord = coord + min; - coords.push_back(coord); - } - } - - if (mode == "reflect") { - return tvm::ir::Select::make(topi::detail::Map(conds, tvm::ir::Or::make), - t(coords), t(indices)); - } else if (mode == "constant") { - return tvm::ir::Select::make(topi::detail::Map(conds, tvm::ir::Or::make), - tvm::make_const(t->dtype, pad_value), t(indices)); - } - - // default mode is edge - return t(indices); - }; - - return tvm::compute(output_shape, l, name); -} - -tvm::Tensor Pad(const tvm::Tensor& t, - const tvm::Array& output_shape, - const tvm::Expr& pad_value, - const std::string& name) { - MTI_ASSERT(t->dtype == pad_value.type()); - - auto l = [&](const tvm::Array& ovars) { - tvm::Array conds; - tvm::Array indices; - - for (size_t i = 0; i < t->shape.size(); ++i) { - tvm::Expr ivar = ovars[i]; - tvm::Expr min = 0; - tvm::Expr extent = t->shape[i]; - - conds.push_back(ivar < min); - conds.push_back(ivar >= min + extent); - indices.push_back(tvm::max(tvm::min(ivar, min + extent - 1), min)); - } - - return tvm::ir::Select::make(topi::detail::Map(conds, tvm::ir::Or::make), - pad_value, t(indices)); - }; - - return tvm::compute(output_shape, l, name); -} - -tvm::Tensor PadLastDim(const tvm::Tensor& t, - const int32_t align_size, - const tvm::Expr& pad_value, - const std::string& name) { - auto input_shape = t->shape; - tvm::Array out_shape; - size_t input_shape_rank = input_shape.size(); - for (size_t i = 0; i < input_shape_rank - 1; ++i) { - out_shape.push_back(input_shape[i]); - } - out_shape.push_back( - (input_shape[input_shape_rank - 1] + align_size - 1) / - align_size * align_size); - - return Pad(t, out_shape, pad_value, name + "_pad"); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/pad_ops.h b/onnxruntime/core/codegen/mti/tensor/pad_ops.h deleted file mode 100644 index 6e8e350d71e97..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/pad_ops.h +++ /dev/null @@ -1,34 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// ONNX Pad semantics -tvm::Tensor Pad(const tvm::Tensor& t, - const tvm::Array& pad_before, - const tvm::Array& pad_after, - float pad_value = 0.0f, - const std::string& mode = "constant", - const std::string& name = "pad"); - -// Other common Pad interfaces -// Pad for a given shape -tvm::Tensor Pad(const tvm::Tensor& t, - const tvm::Array& output_shape, - const tvm::Expr& pad_value, - const std::string& name = "pad"); - -// Pad for the last dim only. -// This is widely used for weight layout to guard alignment -tvm::Tensor PadLastDim(const tvm::Tensor& t, - const int32_t align_size, - const tvm::Expr& pad_value, - const std::string& name = "pad_last_dim"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/reshape_ops.cc b/onnxruntime/core/codegen/mti/tensor/reshape_ops.cc deleted file mode 100644 index 817fb32c2837a..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/reshape_ops.cc +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/reshape_ops.h" - -#include "core/codegen/mti/common.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Flatten(const tvm::Tensor& X, int64_t axis, const std::string& name) { - const auto& input_shape = X->shape; - return Reshape(X, {SizeToDimension(input_shape, axis), SizeFromDimension(input_shape, axis)}, name); -} - -tvm::Tensor Identity(const tvm::Tensor& X, const std::string& name) { - return Reshape(X, X->shape, name); -} - -tvm::Tensor Reshape(const tvm::Tensor& X, const tvm::Array& new_shape, const std::string& name) { - if (new_shape.size() > 0) { - auto X_dim = SizeToDimension(X->shape, X->shape.size()); - auto new_dim = SizeToDimension(new_shape, new_shape.size()); - auto* pX_dim = tvm::as_const_int(X_dim); - auto* pNew_dim = tvm::as_const_int(new_dim); - - if (pX_dim != nullptr && pNew_dim != nullptr) { - MTI_ASSERT(*pX_dim == *pNew_dim); - } - return topi::reshape(X, new_shape, name); - } else { - // generate empty dim tensor with origial input data value - tvm::Array tmp_shape; - tmp_shape.push_back(1); - auto tmp_tensor = topi::reshape(X, tmp_shape); - return tvm::compute( - new_shape, - [&](const tvm::Array&) { - return tmp_tensor[0]; - }, - name); - } -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/reshape_ops.h b/onnxruntime/core/codegen/mti/tensor/reshape_ops.h deleted file mode 100644 index e23d62e4c57b0..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/reshape_ops.h +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Flatten(const tvm::Tensor& X, int64_t axis, const std::string& name = "flatten"); -tvm::Tensor Identity(const tvm::Tensor& X, const std::string& name = "identity"); -tvm::Tensor Reshape(const tvm::Tensor& X, const tvm::Array& new_shape, const std::string& name = "reshape"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/shape_op.cc b/onnxruntime/core/codegen/mti/tensor/shape_op.cc deleted file mode 100644 index b51bd67a8b2dc..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/shape_op.cc +++ /dev/null @@ -1,25 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/shape_op.h" - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Shape(const tvm::Tensor& X, const std::string& name) { - int ndim = static_cast(X->shape.size()); - tvm::Array out_shape{ndim}; - return tvm::compute( - out_shape, [&](const tvm::Array& indices) { - auto idx = indices[0]; - tvm::Expr ret = 0; - for (int i = 0; i < ndim; ++i) { - ret = tvm::ir::Select::make(idx == i, X->shape[i], ret); - } - return tvm::cast(HalideIR::Int(64), ret); - }, - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/shape_op.h b/onnxruntime/core/codegen/mti/tensor/shape_op.h deleted file mode 100644 index 67ee2de50eca9..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/shape_op.h +++ /dev/null @@ -1,14 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Shape(const tvm::Tensor& X, const std::string& name = "shape"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/slice.cc b/onnxruntime/core/codegen/mti/tensor/slice.cc deleted file mode 100644 index 6cbab43584d4b..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/slice.cc +++ /dev/null @@ -1,91 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/slice.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// local constexpr for INT_MAX -constexpr int64_t max_range = INT_MAX; - -tvm::Expr position(const tvm::Expr& dim, const tvm::Integer& offset, bool allow_out_of_bound = false) { - if (offset->value >= max_range) { - return allow_out_of_bound ? dim : dim - 1; - } else if (offset->value <= -max_range) { - return tvm::make_const(HalideIR::Int(32), allow_out_of_bound ? -1 : 0); - } else { - if (offset->value >= 0) { - return tvm::ir::Simplify(tvm::ir::Min::make(offset, dim + (allow_out_of_bound ? 0 : -1))); - } else { - return tvm::ir::Simplify(dim + tvm::ir::Max::make(offset, -dim + (allow_out_of_bound ? -1 : 0))); - } - } -} - -tvm::Tensor Slice(const tvm::Tensor& X, - const std::vector& starts, - const std::vector& ends, - const std::vector& axes1, - const std::vector& steps, - const std::string& name) { - MTI_ASSERT(starts.size() == ends.size()); - MTI_ASSERT(starts.size() == axes1.size()); - MTI_ASSERT(starts.size() == steps.size()); - - std::vector axes; - for (const auto& i : axes1) { - axes.push_back(HandleNegativeAxis(i, X->shape.size())); - } - - tvm::Array output_shape; - bool empty = false; - for (int64_t i = 0; i < gsl::narrow(X->shape.size()); ++i) { - auto axes_iter = std::find(axes.begin(), axes.end(), i); - if (axes_iter != axes.end()) { - auto axis = axes_iter - axes.begin(); - tvm::Expr start = position(X->shape[i], starts[axis]); - tvm::Expr end = position(X->shape[i], ends[axis], /*allow_out_of_bound*/ true); - auto dim = tvm::ir::Simplify((end - start + tvm::Integer(steps[axis] + (steps[axis] < 0 ? 1 : -1))) / tvm::Integer(steps[axis])); - auto int_dim = tvm::as_const_int(dim); - if (int_dim && *int_dim <= 0) { - output_shape.push_back(0); - empty = true; - } else { - output_shape.push_back(dim); - } - } else { - output_shape.push_back(X->shape[i]); - } - } - - if (empty) { - return MakeZeroTensor(output_shape, X->dtype, name); - } - - return tvm::compute( - output_shape, - [&](const tvm::Array& ovars) { - tvm::Array ivars; - for (size_t i = 0; i < X->shape.size(); ++i) { - auto axes_iter = std::find(axes.begin(), axes.end(), i); - if (axes_iter != axes.end()) { - auto axis = axes_iter - axes.begin(); - ivars.push_back(tvm::ir::Simplify(ovars[i] * tvm::Integer(steps[axis]) + position(X->shape[i], starts[axis]))); - } else { - ivars.push_back(ovars[i]); - } - } - return X(ivars); - }, - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/slice.h b/onnxruntime/core/codegen/mti/tensor/slice.h deleted file mode 100644 index ac5c9437791f6..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/slice.h +++ /dev/null @@ -1,19 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Slice(const tvm::Tensor& X, - const std::vector& starts, - const std::vector& ends, - const std::vector& axes, - const std::vector& steps, - const std::string& name = "slice"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/split.cc b/onnxruntime/core/codegen/mti/tensor/split.cc deleted file mode 100644 index 6ee366314858f..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/split.cc +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/split.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// Similar to numpy, topi::split takes split indices rather than the -// sizes of the splits. Thus we implement our own. -tvm::Array Split(const tvm::Tensor& X, - const tvm::Array& split_sizes, - int64_t axis, - const std::string& name) { - MTI_ASSERT(axis < gsl::narrow(X->shape.size())); - size_t axis_t = gsl::narrow(axis); - - tvm::Array> output_shapes; - int num_splits = gsl::narrow(split_sizes.size()); - for (auto& s : split_sizes) { - tvm::Array shape; - for (size_t i = 0; i < axis_t; i++) { - shape.push_back(X->shape[i]); - } - shape.push_back(s); - for (size_t i = axis_t + 1; i < X->shape.size(); i++) { - shape.push_back(X->shape[i]); - } - output_shapes.push_back(shape); - } - - tvm::Array res; - int idx = 0; - for (int i_split = 0; i_split < num_splits; ++i_split) { - tvm::Expr s = split_sizes[i_split]; - auto l = [&](const tvm::Array& indices) { - tvm::Array new_indices; - for (size_t i = 0; i < axis_t; i++) { - new_indices.push_back(indices[i]); - } - new_indices.push_back(indices[axis_t] + idx); - for (size_t i = axis_t + 1; i < X->shape.size(); i++) { - new_indices.push_back(indices[i]); - } - MTI_ASSERT(topi::detail::IsConstInt(s)); - MTI_ASSERT(new_indices.size() == X->shape.size()); - int size = topi::detail::GetConstInt(s); - idx += size; - return X(new_indices); - }; - res.push_back(tvm::compute(output_shapes[i_split], l, name)); - } - - MTI_ASSERT(topi::detail::IsConstInt(X->shape[axis_t])); - int size_of_splitted_axis = static_cast(topi::detail::GetConstInt(X->shape[axis_t])); - MTI_ASSERT(idx == size_of_splitted_axis); - return res; -} - -tvm::Array SplitWithIndices(const tvm::Tensor& X, - const tvm::Array& split_sizes, - int64_t axis, - const std::string& name) { - return topi::split(X, split_sizes, gsl::narrow(axis), name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/split.h b/onnxruntime/core/codegen/mti/tensor/split.h deleted file mode 100644 index bcb9c47d936dd..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/split.h +++ /dev/null @@ -1,25 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// ONNX Split semantics -tvm::Array Split(const tvm::Tensor& X, - const tvm::Array& split_sizes, - int64_t axis, - const std::string& name = "split"); - -// Another common Split interface -// Split with chunck indices -tvm::Array SplitWithIndices(const tvm::Tensor& X, - const tvm::Array& split_sizes, - int64_t axis, - const std::string& name = "split_with_indices"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/tile.cc b/onnxruntime/core/codegen/mti/tensor/tile.cc deleted file mode 100644 index 2fef86adcbaea..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/tile.cc +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/tile.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Tile(const tvm::Tensor& t, - const std::vector& repeats, - const std::string& name) { - MTI_ASSERT(repeats.size() == t->shape.size()); - tvm::Array output_shape; - - bool repeats_zero = false; - for (size_t i = 0; i < t->shape.size(); ++i) { - if (repeats[i] == 0) - repeats_zero = true; - output_shape.push_back(t->shape[i] * gsl::narrow(repeats[i])); - } - - auto l = [&](const tvm::Array& ovars) { - if (repeats_zero) - return tvm::make_zero(t->dtype); - - tvm::Array ivars; - for (size_t i = 0; i < t->shape.size(); ++i) { - tvm::Expr ovar = ovars[i]; - ivars.push_back(ovar % t->shape[i]); - } - return t(ivars); - }; - - return tvm::compute(output_shape, l, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/tile.h b/onnxruntime/core/codegen/mti/tensor/tile.h deleted file mode 100644 index 7ce331fb5ea95..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/tile.h +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Tile(const tvm::Tensor& t, - const std::vector& repeats, - const std::string& name = "tile"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/transpose.cc b/onnxruntime/core/codegen/mti/tensor/transpose.cc deleted file mode 100644 index 873ff8d7f1708..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/transpose.cc +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/tensor/transpose.h" - -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Transpose(const tvm::Tensor& X, const tvm::Array& axes, const std::string& name) { - return topi::transpose(X, axes, name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/transpose.h b/onnxruntime/core/codegen/mti/tensor/transpose.h deleted file mode 100644 index a2a98fedf1e79..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/transpose.h +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Transpose(const tvm::Tensor& X, - const tvm::Array& axes, - const std::string& name = "transpose"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/where.cc b/onnxruntime/core/codegen/mti/tensor/where.cc deleted file mode 100644 index 2bdac3cae7ef5..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/where.cc +++ /dev/null @@ -1,36 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/where.h" - -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Where(const tvm::Tensor& B, - const tvm::Tensor& X, - const tvm::Tensor& Y, - const std::string& name) { - size_t rank = std::max(std::max(B->shape.size(), X->shape.size()), Y->shape.size()); - tvm::Array output_shape; - for (size_t i = 0; i < rank; ++i) { - tvm::Expr dim = tvm::make_const(HalideIR::Int(32), 1); - bool broadcasted = - BroadcastDim(B->shape, i, rank, dim) && - BroadcastDim(X->shape, i, rank, dim) && - BroadcastDim(Y->shape, i, rank, dim); - MTI_ASSERT(broadcasted); - output_shape.push_back(dim); - } - - return topi::where(topi::broadcast_to(B, output_shape), - topi::broadcast_to(X, output_shape), - topi::broadcast_to(Y, output_shape), - name); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/mti/tensor/where.h b/onnxruntime/core/codegen/mti/tensor/where.h deleted file mode 100644 index 68c5288eb3580..0000000000000 --- a/onnxruntime/core/codegen/mti/tensor/where.h +++ /dev/null @@ -1,17 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Where(const tvm::Tensor& B, - const tvm::Tensor& X, - const tvm::Tensor& Y, - const std::string& name = "where"); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/all_ops.h b/onnxruntime/core/codegen/passes/op_ir_creator/all_ops.h deleted file mode 100644 index 1463e50bd72fb..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/all_ops.h +++ /dev/null @@ -1,47 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/passes/utils/codegen_context.h" -#include "core/codegen/common/op_macro.h" -#include "core/codegen/passes/op_ir_creator/tvm_op_creator.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// This macro declares a TVM IR builder -// based on ORT OP type with postfix DefaultTVM -#define DECLARE_GENERIC_OP_IR_CREATOR_CLASS(OP) \ - DECLARE_OP_IR_CREATOR_CLASS(OP, DefaultTVM) - -// This macro returns a TVM IR builder class name -// based ORT OP type with postfix DefaultTVM -#define GENERIC_OP_IR_CREATOR_CLASS(OP) \ - CREATOR_CLASS(OP, DefaultTVM##IRCreator) - -#define GENERIC_OP_IR_CREATOR_STRING(OP) \ - STRINGIZE(GENERIC_OP_IR_CREATOR_CLASS(OP)) - -// define all ops for DefaultTVM -#define ADD_OP_ITEM(OP) DECLARE_GENERIC_OP_IR_CREATOR_CLASS(OP) -#define BINARY_OP(OP) ADD_OP_ITEM(OP) -#define BINARY_CMP_OP(OP) ADD_OP_ITEM(OP) -#define POOL_OP(OP) ADD_OP_ITEM(OP) -#define UNARY_OP(OP) ADD_OP_ITEM(OP) -#define VARIADIC_OP(OP) ADD_OP_ITEM(OP) -#define REDUCE_INDEXED_OP(OP) ADD_OP_ITEM(OP) -#define REDUCE_OP(OP) ADD_OP_ITEM(OP) - -LIST_ALL_GENERIC_OPS() - -#undef ADD_OP_ITEM -#undef BINARY_OP -#undef BINARY_CMP_OP -#undef POOL_OP -#undef REDUCE_OP -#undef REDUCE_INDEXED_OP -#undef UNARY_OP -#undef VARIADIC_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/binary_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/binary_ops.cc deleted file mode 100644 index 9452146621ac7..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/binary_ops.cc +++ /dev/null @@ -1,46 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/common/op_macro.h" -#include "core/codegen/mti/math/binary_ops.h" -#include "core/codegen/mti/tensor/cast_ops.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// helper local macro defines Evaluate of BINARY_OP OpIRCreators -#define BINARY_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y = name(inputs[0], inputs[1], node.Name()); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -LIST_BINARY_OPS() - -#undef BINARY_OP - -// helper local macro defines Evaluate of BINARY_CMP_OP OpIRCreators -#define BINARY_CMP_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y = Cast(name(inputs[0], inputs[1], node.Name()), HalideIR::UInt(8), "cast_bool_" #name); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -LIST_BINARY_CMP_OPS() - -#undef BINARY_CMP_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc deleted file mode 100644 index bb33e6e70accf..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/clip.cc +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/unary_ops.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Clip OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Clip)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - int version = ctx_codegen.GetCodeGenHandle()->domain_version_lookup_func(node.Domain()); - tvm::Expr min_value, max_value; - if (version < 11) { - float max_v, min_v; - info.GetAttrOrDefault("min", &min_v, std::numeric_limits::lowest()); - info.GetAttrOrDefault("max", &max_v, std::numeric_limits::max()); - min_value = tvm::make_const(tvm::Float(32), min_v); - max_value = tvm::make_const(tvm::Float(32), max_v); - } else { - // for op_version >= 11, max and min are optional inputs - min_value = tvm::make_const(tvm::Float(32), std::numeric_limits::lowest()); - max_value = tvm::make_const(tvm::Float(32), std::numeric_limits::max()); - auto num_inputs = inputs.size(); - if (num_inputs >= 2 && inputs[1].defined()) { - min_value = inputs[1](); - } - if (num_inputs == 3 && inputs[2].defined()) { - max_value = inputs[2](); - } - } - - tvm::Tensor Y = Clip(inputs[0], min_value, max_value, node.Name() + "_Clip"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/gemm.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/gemm.cc deleted file mode 100644 index 64f995076e1bb..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/gemm.cc +++ /dev/null @@ -1,39 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/gemm.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Gemm OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Gemm)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& /*ctx_codegen*/, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - tvm::Tensor A = inputs[0]; - tvm::Tensor B = inputs[1]; - tvm::Tensor C = inputs[2]; - - int64_t trans_A, trans_B; - ORT_RETURN_IF_ERROR(attrs.GetAttr("transA", &trans_A)); - ORT_RETURN_IF_ERROR(attrs.GetAttr("transB", &trans_B)); - - float alpha, beta; - ORT_ENFORCE(attrs.GetAttr("alpha", &alpha).IsOK()); - ORT_ENFORCE(attrs.GetAttr("beta", &beta).IsOK()); - - tvm::Tensor Y = Gemm(A, B, C, trans_A != 0, trans_B != 0, alpha, beta, node.Name() + "_Gemm"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/logsoftmax.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/logsoftmax.cc deleted file mode 100644 index cb09518bf63d1..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/logsoftmax.cc +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/logsoftmax.h" -#include "core/framework/op_kernel_info.h" -#include "core/providers/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of LogSoftmax OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(LogSoftmax)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - int64_t axis_i64; - ORT_RETURN_IF_ERROR(info.GetAttr("axis", &axis_i64)); - axis_i64 = HandleNegativeAxis(axis_i64, gsl::narrow_cast(inputs[0]->shape.size())); - - tvm::Tensor Y = LogSoftmax(inputs[0], axis_i64, node.Name() + "_LogSoftmax"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/matmul.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/matmul.cc deleted file mode 100644 index ab1ac237bfa5d..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/matmul.cc +++ /dev/null @@ -1,23 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/matmul_ops.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of MatMul OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(MatMul)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - tvm::Tensor Y = MatMul(inputs[0], inputs[1], node.Name() + "_MatMul"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/quantize/matmul_integer.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/quantize/matmul_integer.cc deleted file mode 100644 index 6f66b1f1a2afb..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/quantize/matmul_integer.cc +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/binary_ops.h" -#include "core/codegen/mti/math/matmul_ops.h" -#include "core/codegen/mti/tensor/cast_ops.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of MatMulInteger OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(MatMulInteger)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - const auto& A = inputs[0]; - const auto& B = inputs[1]; - auto& name = node.Name(); - - // A generic path, cast to int32 - // Support skipped trailing inputs - auto A_Int32 = (node.InputDefs().size() >= 3 && node.InputDefs()[2]->Exists()) - ? Sub(Cast(A, HalideIR::Int(32)), Cast(inputs[2], HalideIR::Int(32))) - : Cast(A, HalideIR::Int(32)); - auto B_Int32 = (node.InputDefs().size() >= 4 && node.InputDefs()[3]->Exists()) - ? Sub(Cast(B, HalideIR::Int(32)), Cast(inputs[3], HalideIR::Int(32))) - : Cast(B, HalideIR::Int(32)); - tvm::Tensor Y = MatMul(A_Int32, B_Int32, name + "_MatMulInteger"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/reduce_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/reduce_ops.cc deleted file mode 100644 index f29a3f3e7cdf7..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/reduce_ops.cc +++ /dev/null @@ -1,111 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/common/op_macro.h" -#include "core/codegen/mti/math/reduce_ops.h" -#include "core/codegen/mti/tensor/cast_ops.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include "core/framework/op_kernel_info.h" -#include "core/providers/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -using ReduceIndexedFunc = tvm::Tensor (*)(const tvm::Tensor& X, int64_t axis, bool keep_dims, const std::string& name); -using ReduceFunc = tvm::Tensor (*)(const tvm::Tensor& X, const std::vector& axes, bool keep_dims, const std::string& name); - -// helper class for for REDUCE_INDEXED_OP -class FuncReduceIndexed { - public: - FuncReduceIndexed(const Node& node, ReduceIndexedFunc func, const std::string& name) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - axis_ = info.GetAttrOrDefault("axis", 0); - int64_t keepdims_i = 1; - ORT_ENFORCE(info.GetAttr("keepdims", &keepdims_i).IsOK()); - keep_dims_ = (keepdims_i == 1); - func_ = func; - name_ = name; - } - - tvm::Tensor operator()(const tvm::Tensor& X) const { - auto axis = HandleNegativeAxis(axis_, gsl::narrow_cast(X->shape.size())); - tvm::Tensor index32 = func_(X, axis, keep_dims_, name_); - return Cast(index32, tvm::Int(64)); - } - - private: - int64_t axis_; - bool keep_dims_; - ReduceIndexedFunc func_; - std::string name_; -}; - -// helper class for REDUCE_OP -class FuncReduce { - public: - FuncReduce(const Node& node, ReduceFunc func, const std::string& name) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - axes_ = info.GetAttrsOrDefault("axes"); - int64_t keepdims_i = 1; - ORT_ENFORCE(info.GetAttr("keepdims", &keepdims_i).IsOK()); - keep_dims_ = (keepdims_i == 1); - func_ = func; - name_ = name; - } - - tvm::Tensor operator()(const tvm::Tensor& X) const { - std::vector axes; - for (auto i : axes_) - axes.push_back(HandleNegativeAxis(i, gsl::narrow_cast(X->shape.size()))); - - return func_(X, axes, keep_dims_, name_); - } - - private: - std::vector axes_; - bool keep_dims_; - ReduceFunc func_; - std::string name_; -}; - -// helper macro defines Evaluate of REDUCE_OP OpIRCreators -#define REDUCE_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y; \ - if (ShapeRank(node.OutputDefs()[0]) == 0) { \ - tvm::Tensor temp = FuncReduce(node, &name, #name)(inputs[0]); \ - Y = Reshape(temp, {}); \ - } else { \ - Y = FuncReduce(node, &name, #name)(inputs[0]); \ - } \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -// helper macro defines Evaluate of REDUCE_INDEXED_OP OpIRCreators -#define REDUCE_INDEXED_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y = FuncReduceIndexed(node, &name, #name)(inputs[0]); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -LIST_REDUCE_OPS() - -#undef REDUCE_OP -#undef REDUCE_INDEXED_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/softmax.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/softmax.cc deleted file mode 100644 index 7b13de5a94e48..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/softmax.cc +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/softmax.h" -#include "core/framework/op_kernel_info.h" -#include "core/providers/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Softmax OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Softmax)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - int64_t axis_i64; - ORT_RETURN_IF_ERROR(info.GetAttr("axis", &axis_i64)); - - axis_i64 = HandleNegativeAxis(axis_i64, gsl::narrow_cast(inputs[0]->shape.size())); - tvm::Tensor Y = Softmax(inputs[0], axis_i64, node.Name() + "_Softmax"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_funcs.h b/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_funcs.h deleted file mode 100644 index 29e6519af0ef1..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_funcs.h +++ /dev/null @@ -1,51 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { -// helper class for unary_ops with alpha -class FuncWithAlpha { - public: - FuncWithAlpha(const Node& node) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - ORT_ENFORCE(attrs.GetAttr("alpha", &alpha_).IsOK()); - } - - protected: - float alpha_; -}; - -// helper class for unary_ops with alpha and beta -class FuncWithAlphaBeta { - public: - FuncWithAlphaBeta(const Node& node) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - ORT_ENFORCE(attrs.GetAttr("alpha", &alpha_).IsOK()); - ORT_ENFORCE(attrs.GetAttr("beta", &beta_).IsOK()); - } - - protected: - float alpha_; - float beta_; -}; - -// helper class for unary_ops with alpha and gamma -class FuncWithAlphaGamma { - public: - FuncWithAlphaGamma(const Node& node) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - ORT_ENFORCE(attrs.GetAttr("alpha", &alpha_).IsOK()); - ORT_ENFORCE(attrs.GetAttr("gamma", &gamma_).IsOK()); - } - - protected: - float alpha_; - float gamma_; -}; -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_ops.cc deleted file mode 100644 index 0407c0a06abf6..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/unary_ops.cc +++ /dev/null @@ -1,93 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/common/op_macro.h" -#include "core/codegen/mti/math/unary_ops.h" -#include "core/codegen/passes/op_ir_creator/math/unary_funcs.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// helper macro declares unary_ops helper class without attribute -#define FuncClass(name) \ - class Func##name { \ - public: \ - Func##name(const Node&) {} \ - tvm::Tensor operator()(const tvm::Tensor& X) const { \ - return name(X); \ - } \ - } - -// helper macro declares unary_ops helper class with alpha -#define FuncClassAlpha(name) \ - class Func##name : public FuncWithAlpha { \ - public: \ - Func##name(const Node& node) : FuncWithAlpha(node) {} \ - tvm::Tensor operator()(const tvm::Tensor& X) const { \ - return name(X, alpha_); \ - } \ - } - -// helper macro declares unary_ops helper class with alpha and beta -#define FuncClassAlphaBeta(name) \ - class Func##name : public FuncWithAlphaBeta { \ - public: \ - Func##name(const Node& node) : FuncWithAlphaBeta(node) {} \ - tvm::Tensor operator()(const tvm::Tensor& X) const { \ - return name(X, alpha_, beta_); \ - } \ - } - -// helper macro declares unary_ops helper class with alpha and gamma -#define FuncClassAlphaGamma(name) \ - class Func##name : public FuncWithAlphaGamma { \ - public: \ - Func##name(const Node& node) : FuncWithAlphaGamma(node) {} \ - tvm::Tensor operator()(const tvm::Tensor& X) const { \ - return name(X, alpha_, gamma_); \ - } \ - } - -FuncClass(Abs); -FuncClassAlphaBeta(Affine); -FuncClass(Ceil); -FuncClassAlpha(Elu); -FuncClass(Exp); -FuncClass(Floor); -FuncClassAlphaBeta(HardSigmoid); -FuncClassAlpha(LeakyRelu); -FuncClass(Log); -FuncClass(Neg); -FuncClassAlphaBeta(ParametricSoftplus); -FuncClass(Reciprocal); -FuncClass(Relu); -FuncClassAlphaBeta(ScaledTanh); -FuncClassAlphaGamma(Selu); -FuncClass(Sigmoid); -FuncClass(Softplus); -FuncClass(Softsign); -FuncClass(Sqrt); -FuncClass(Tanh); -FuncClassAlpha(ThresholdedRelu); - -// helper macro defines Evaluate of UNARY_OP OpIRCreators -#define UNARY_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y = Func##name(node)(inputs[0]); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -// helper local macros to replace some calls in LIST_UNARY_OPS -LIST_UNARY_OPS() - -#undef UNARY_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/math/variadic_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/math/variadic_ops.cc deleted file mode 100644 index 9559a713c2876..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/math/variadic_ops.cc +++ /dev/null @@ -1,36 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/math/binary_ops.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -tvm::Tensor Sum(const tvm::Tensor& lhs, const tvm::Tensor& rhs, const std::string& name) { - return Add(lhs, rhs, name); -} - -// helper local macro defines Evaluate of BINARY_OP OpIRCreators -#define VARIADIC_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext&, \ - tvm::Array& outputs) { \ - tvm::Tensor Y = Identity(inputs[0], node.Name() + "0"); \ - for (size_t i = 1; i < inputs.size(); ++i) \ - Y = name(Y, inputs[i], node.Name() + std::to_string(i)); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -LIST_VARIADIC_OPS() - -#undef VARIADIC_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc b/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc deleted file mode 100644 index 19545d1554405..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc +++ /dev/null @@ -1,131 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/nn/conv_ops.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/concat_ops.h" -#include "core/codegen/mti/tensor/split.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -Status GENERIC_OP_IR_CREATOR_CLASS(Conv)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - // Attributes - int64_t group; - std::string auto_pad; - std::vector kernel_shape, strides, dilations, pads; - - info.GetAttrOrDefault("group", &group, 1); - info.GetAttrOrDefault("auto_pad", &auto_pad, "NOTSET"); - - ORT_THROW_IF_ERROR(info.GetAttrs("kernel_shape", kernel_shape)); - ORT_ENFORCE(kernel_shape.size() <= 2, "Only support 1D/2D convolution currently!"); - ORT_THROW_IF_ERROR(info.GetAttrs("strides", strides)); - - dilations = info.GetAttrs("dilations", dilations).IsOK() ? dilations : std::vector(kernel_shape.size(), 1); - ORT_ENFORCE(dilations == std::vector(kernel_shape.size(), 1), "Only support dilation is 1 currently"); - - pads = info.GetAttrs("pads", pads).IsOK() ? pads : std::vector(kernel_shape.size() * 2, 0); - - // auto_pad - if (auto_pad != "NOTSET") { - auto rank = inputs[0]->shape.size() - 2; - ORT_ENFORCE(rank > 0); - for (uint64_t i = 0; i < rank; i++) { - if (auto_pad == "VALID") { - pads[i] = 0; - pads[i + rank] = 0; - } else if (auto_pad == "SAME_UPPER" || auto_pad == "SAME_LOWER") { - // TODO: handle symbolic dim - ORT_ENFORCE(ShapeHasValue(node.InputDefs()[0], 2 + i)); - - int64_t input_dim_value = ShapeValue(node.InputDefs()[0], 2 + i); - int64_t output_dim_value = (input_dim_value + strides[i] - 1) / strides[i]; - int64_t pad_needed = (output_dim_value - 1) * strides[i] + kernel_shape[i] - input_dim_value; - - pads[i] = auto_pad == "SAME_LOWER" ? (pad_needed + 1) / 2 : pad_needed / 2; - pads[i + rank] = pad_needed - pads[i]; - } else { - ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Unknown auto_pad value"); - } - } - } - - // Inputs - tvm::Tensor X = inputs[0]; - tvm::Tensor W = inputs[1]; - // Outputs - tvm::Tensor Y; - tvm::Array Y_shape = ShapeToTvmArray(node.OutputDefs()[0], ctx_codegen); - - // 1-D convolution - if (kernel_shape.size() == 1) { - Y = Conv1D(X, W, Y_shape, ToTvmArray(strides), ToTvmArray(pads), node.Name() + "_Conv1D"); - } - // 2-D convolution - else if (kernel_shape.size() == 2) { - if (group == 1) { - Y = Conv2D(X, W, Y_shape, ToTvmArray(strides), ToTvmArray(pads), node.Name() + "_Conv2D"); - } else { - int64_t channel_out = ShapeValue(node.InputDefs()[1], 0); - int64_t channel_in = ShapeValue(node.InputDefs()[1], 1); - ORT_ENFORCE(channel_out % group == 0); - - int64_t cout_group = channel_out / group; - Y_shape.Set(1, Y_shape[1] / gsl::narrow_cast(group)); - - tvm::Array split_index0; - tvm::Array split_index1; - - for (int i = 1; i < group; i++) { - split_index0.push_back(i * channel_in); - split_index1.push_back(i * cout_group); - } - - auto input_groups = SplitWithIndices(X, split_index0, 1); - auto weight_groups = SplitWithIndices(W, split_index1, 0); - - // FIXME: This will trigger a llvm buffer overflow when group is too large - // TODO: fix this change it to batched gemm/conv - tvm::Array output_tensors; - for (int i = 0; i < group; i++) { - auto output_tensor = Conv2D(input_groups[i], - weight_groups[i], - Y_shape, - ToTvmArray(strides), - ToTvmArray(pads), - node.Name() + "_Conv2D"); - output_tensors.push_back(output_tensor); - } - Y = Concat(output_tensors, 1); - } - } - - // Add bias if provided - // Support skipped trailing inputs - if (node.InputDefs().size() > 2 && node.InputDefs()[2]->Exists()) { - tvm::Tensor B = inputs[2]; - Y = tvm::compute( - Y_shape, - [&](const tvm::Array& indices) { - return Y(indices) + B(indices[1]); - }); - } - - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/nn/lstm.cc b/onnxruntime/core/codegen/passes/op_ir_creator/nn/lstm.cc deleted file mode 100644 index 88170bb56dd2d..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/nn/lstm.cc +++ /dev/null @@ -1,64 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/nn/lstm.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// In the cell computation, we don't have the "direction" dimension and sequence dimension, -// which have been processed outside of the cell. -// Here we implement an LTSM cell. -// For those args (inputs/outputs) of hidden states we put AFTER regular args (inputs/outputs) -// with a pre-defined order -// In a LSTM, the order is H and then C. -// Ouputs of LSTM is Y_h and then Y_c -Status GENERIC_OP_IR_CREATOR_CLASS(LSTM)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - std::string direction_attr; - ORT_RETURN_IF_ERROR(attrs.GetAttr("direction", &direction_attr)); - int64_t hidden_size; - ORT_RETURN_IF_ERROR(attrs.GetAttr("hidden_size", &hidden_size)); - - // input tensor with shape [seq_length, batch_size, input_size] - const tvm::Tensor& X = inputs[0]; // input tensor with shape [seq_length, batch_size, input_size] - const tvm::Tensor& W = inputs[1]; // weights tensor with shape [4*hidden_size, input_size] - const tvm::Tensor& R = inputs[2]; // recurrence tensor with shape [4*hidden_size, hidden_size] - const tvm::Tensor& B = inputs[3]; // optional bias tensor with shape [8*hidden_size] - bool has_B = node.InputDefs()[3]->Exists(); - - // Unsupported the 4th inputs - // optional tensor specifying sequence lengths in a batch, shape: [batch_size] - // const tvm::Tensor* seq_len = inputs[4] ? &inputs[4]->tensor : nullptr; - - const tvm::Tensor& prev_H = inputs[5]; // optional initial H, shape: [batch_size, hidden_size] - const tvm::Tensor& prev_C = inputs[6]; // optional initial C, shape: [batch_size, hidden_size] - - const tvm::Tensor& P = inputs[7]; // optional peepholes tensor with shape [3*hidde_size] - bool has_P = node.InputDefs()[7]->Exists(); - - tvm::Tensor Y_h; // shape: [batch_size, hidden_size] - tvm::Tensor Y_c; // shape: [batch_size, hidden_size] - LSTMAttributes lstm_attrs(hidden_size); - LSTM_cell(lstm_attrs, X, W, R, B, has_B, prev_H, prev_C, P, has_P, Y_h, Y_c); - - // Since we only generate lstm cell, lstm's states need to be always outputs, - // regardless whethere they are skipped or not. - // The skipped trailing outputs need to be handled by Execution - outputs.push_back(Y_h); - outputs.push_back(Y_c); - - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/nn/pool_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/nn/pool_ops.cc deleted file mode 100644 index 84d3b7c1e0f79..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/nn/pool_ops.cc +++ /dev/null @@ -1,51 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/nn/pool_ops.h" -#include "core/framework/op_kernel_info.h" -#include "core/providers/cpu/nn/pool_attributes.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// A local macro to create Pool Ops - -// helper macro defines Evaluate of of POOL_OP OpIRCreators -#define POOL_OP(name) \ - Status GENERIC_OP_IR_CREATOR_CLASS(name)::Evaluate( \ - const tvm::Array& inputs, \ - const Node& node, \ - CodeGenContext& ctx_codegen, \ - tvm::Array& outputs) { \ - ORT_RETURN_IF_NOT(outputs.size() == 1, "multiple outputs are not supported yet!"); \ - ProtoHelperNodeContext ctx(node); \ - OpNodeProtoHelper info(&ctx); \ - int version = ctx_codegen.GetCodeGenHandle()->domain_version_lookup_func(node.Domain()); \ - PoolAttributes pool_attrs(info, #name, version); \ - for (auto n : pool_attrs.dilations) { \ - ORT_RETURN_IF_NOT(n <= 1, "dilations are not supported yet!"); \ - } \ - if (pool_attrs.global_pooling) { \ - if (inputs[0]->shape.size() != 4) { \ - ORT_NOT_IMPLEMENTED(gsl::narrow_cast(inputs[0]->shape.size()) - 2, "d global pooling is not implementated"); \ - } \ - } else { \ - if (pool_attrs.kernel_shape.size() != 2) { \ - ORT_NOT_IMPLEMENTED(pool_attrs.kernel_shape.size(), "d pooling is not implementated"); \ - } \ - } \ - tvm::Array dummy_output_shape; \ - tvm::Tensor Y = name(inputs[0], pool_attrs, dummy_output_shape); \ - outputs.push_back(Y); \ - return Status::OK(); \ - } - -LIST_POOL_OPS() - -#undef POOL_OP - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/cast.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/cast.cc deleted file mode 100644 index bd324fd359edf..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/cast.cc +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/tensor/cast_ops.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Cast OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Cast)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - int64_t to; - ORT_RETURN_IF_ERROR(attrs.GetAttr("to", &to)); - auto to_type_proto = gsl::narrow_cast(to); - - tvm::Tensor X = inputs[0]; - tvm::Tensor Y; - if (to_type_proto == ONNX_NAMESPACE::TensorProto_DataType_BOOL) { - // special case for bool as ONNX bool is uint8, while in tvm it's uint1 - Y = CastToUInt8Bool(X, node.Name() + "_Cast"); - } else { - Y = Cast(X, ToTvmType(to_type_proto), node.Name() + "_Cast"); - } - - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/concat.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/concat.cc deleted file mode 100644 index 418296889419e..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/concat.cc +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/tensor/concat_ops.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Concat OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Concat)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - int64_t axis; - ORT_RETURN_IF_ERROR(info.GetAttr("axis", &axis)); - - tvm::Tensor Y = Concat(inputs, axis, node.Name() + "_Concat"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/crop.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/crop.cc deleted file mode 100644 index 3b6a9a76f0723..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/crop.cc +++ /dev/null @@ -1,46 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/crop.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Crop OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Crop)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - if (inputs[0]->shape.size() != 4) { - return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, - "Input is expected to have four dimensions corresponding to [N,C,H,W]"); - } - - std::vector border; - std::vector scale; - - ORT_ENFORCE(attrs.GetAttrs("border", border).IsOK()); - // scale is optional and status is false when omit - bool is_ok = attrs.GetAttrs("scale", scale).IsOK(); - ORT_UNUSED_PARAMETER(is_ok); - - if (border.size() != 4) { - return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, - "Attribute border needs to be specified with four border elements"); - } - - tvm::Tensor Y = Crop(inputs[0], ToTvmArray(border), ToTvmArray(scale), node.Name() + "_Crop"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/expand.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/expand.cc deleted file mode 100644 index 0f0e0cf0987b3..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/expand.cc +++ /dev/null @@ -1,26 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/expand.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Expand OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Expand)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Expand(inputs[0], ShapeToTvmArray(node.OutputDefs()[0], ctx_codegen), node.Name() + "_Expand"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather.cc deleted file mode 100644 index 3a5d801b6839f..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather.cc +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/tensor/gather.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Gather OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Gather)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - int64_t axis; - ORT_ENFORCE(attrs.GetAttr("axis", &axis).IsOK()); - - tvm::Tensor Y = Gather(inputs[0], axis, inputs[1], node.Name() + "_Gather"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather_elements.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather_elements.cc deleted file mode 100644 index 0b71506cceed3..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/gather_elements.cc +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/tensor/gather_elements.h" -#include "core/framework/op_kernel_info.h" -#include "core/providers/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of GatherElements OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(GatherElements)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - int64_t axis; - ORT_ENFORCE(attrs.GetAttr("axis", &axis).IsOK()); - axis = HandleNegativeAxis(axis, gsl::narrow_cast(inputs[0]->shape.size())); - - tvm::Tensor Y = GatherElements(inputs[0], axis, inputs[1], node.Name() + "_GatherElements"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc deleted file mode 100644 index e9e20e8a43998..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc +++ /dev/null @@ -1,49 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/pad_ops.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Pad OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Pad)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - std::string mode; - std::vector pads; - float value; - - ORT_THROW_IF_ERROR(attrs.GetAttr("mode", &mode)); - ORT_THROW_IF_ERROR(attrs.GetAttrs("pads", pads)); - ORT_THROW_IF_ERROR(attrs.GetAttr("value", &value)); - - if (mode != "constant" && mode != "edge" && mode != "reflect") - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Pad: Unsupported padding mode!"); - - if (pads.size() != 2 * inputs[0]->shape.size()) - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Pad: pads rank does not match inputs rank!"); - - std::vector pad_before, pad_after; - size_t offset = pads.size() / 2; - for (size_t i = 0; i < offset; i++) { - pad_before.push_back(pads[i]); - pad_after.push_back(pads[i + offset]); - } - - tvm::Tensor Y = Pad(inputs[0], ToTvmArray(pad_before), ToTvmArray(pad_after), value, mode, node.Name() + "_Pad"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/reshape_ops.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/reshape_ops.cc deleted file mode 100644 index a83f598bc8ad1..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/reshape_ops.cc +++ /dev/null @@ -1,99 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/reshape_ops.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Dropout OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Dropout)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Identity(inputs[0]); - outputs.push_back(Y); - - // optional mask - // Support skipped trailing outputs - if (node.OutputDefs().size() > 1 && node.OutputDefs()[1]->Exists()) { - // A fake mask with all ones - auto l = [&](const tvm::Array& /*indices*/) { - return tvm::make_const(tvm::UInt(8), 1); - }; - tvm::Tensor mask = tvm::compute(inputs[0]->shape, l, "mask"); - outputs.push_back(mask); - } - - return Status::OK(); -} - -// Evaluate of Flatten OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Flatten)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - int64_t axis; - ORT_RETURN_IF_ERROR(attrs.GetAttr("axis", &axis)); - - tvm::Tensor Y = Flatten(inputs[0], axis, node.Name() + "_Flatten"); - outputs.push_back(Y); - return Status::OK(); -} - -// Evaluate of Identity OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Identity)::Evaluate( - const tvm::Array& inputs, - const Node&, - CodeGenContext&, - tvm::Array& outputs) { - tvm::Tensor Y = Identity(inputs[0]); - outputs.push_back(Y); - return Status::OK(); -} - -// Evaluate of Reshape OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Reshape)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Reshape(inputs[0], ShapeToTvmArray(node.OutputDefs()[0], ctx_codegen), node.Name() + "_Reshape"); - outputs.push_back(Y); - return Status::OK(); -} - -// Evaluate of Squeeze OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Squeeze)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Reshape(inputs[0], ShapeToTvmArray(node.OutputDefs()[0], ctx_codegen), node.Name() + "_Squeeze"); - outputs.push_back(Y); - return Status::OK(); -} - -// Evaluate of Unsqueeze OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Unsqueeze)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Reshape(inputs[0], ShapeToTvmArray(node.OutputDefs()[0], ctx_codegen), node.Name() + "_Unsqueeze"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/shape_op.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/shape_op.cc deleted file mode 100644 index 84761ecac1397..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/shape_op.cc +++ /dev/null @@ -1,26 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/shape_op.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Expand OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Shape)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - tvm::Tensor Y = Shape(inputs[0], node.Name() + "_Expand"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/slice.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/slice.cc deleted file mode 100644 index 6a016580c41e4..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/slice.cc +++ /dev/null @@ -1,71 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/slice.h" -#include "core/framework/op_kernel_info.h" -#include "core/framework/tensorprotoutils.h" - -#include - -namespace onnxruntime { -namespace tvm_codegen { - -Status SliceCommon(const tvm::Array& inputs, - const Node& node, - tvm::Array& outputs, - const std::vector& starts, - const std::vector& ends, - const std::vector& axes1, - const std::vector& steps1) { - ORT_RETURN_IF_NOT(nullptr != node.InputDefs()[0], "nullptr == node.InputDefs()[0]"); - - std::vector axes; - if (axes1.size() == 0) { - for (size_t i = 0; i < starts.size(); ++i) { - axes.push_back(gsl::narrow_cast(i)); - } - } else { - axes = axes1; - } - - std::vector steps; - if (steps1.size() == 0) { - steps.resize(starts.size(), 1); - } else { - steps = steps1; - } - - tvm::Tensor Y = Slice(inputs[0], starts, ends, axes, steps, node.Name() + "_Slice"); - outputs.push_back(Y); - return Status::OK(); -} - -// Evaluate of Slice OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Slice)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - // NOTE that in opset 10, Slice has changed starts/ends/axes from attribute to input - // which may lead to dynamic output shape. - int version = ctx_codegen.GetCodeGenHandle()->domain_version_lookup_func(node.Domain()); - ORT_RETURN_IF_NOT(version <= 9, "Dynamic Slice is not supported yet"); - - std::vector starts, ends, steps; - ORT_RETURN_IF_ERROR(info.GetAttrs("starts", starts)); - ORT_RETURN_IF_ERROR(info.GetAttrs("ends", ends)); - ORT_RETURN_IF_NOT(starts.size() == ends.size(), "starts.size() != ends.size()"); - - auto axes = info.GetAttrsOrDefault("axes"); - - return SliceCommon(inputs, node, outputs, starts, ends, axes, steps); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/split.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/split.cc deleted file mode 100644 index ec52d98b5bf96..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/split.cc +++ /dev/null @@ -1,66 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/split.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Split OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Split)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper info(&ctx); - - int64_t axis; - ORT_RETURN_IF_ERROR(info.GetAttr("axis", &axis)); - axis = HandleNegativeAxis(axis, gsl::narrow_cast(inputs[0]->shape.size())); - std::vector split_sizes; - - int64_t split_size_sum = 0; - if (info.GetAttrs("split", split_sizes).IsOK()) { - // optional - split_size_sum = std::accumulate(split_sizes.cbegin(), split_sizes.cend(), 0LL); - ORT_RETURN_IF_NOT(std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; }), - "Invalid value in 'split' attribute. All values must be > 0"); - - // check split sizes - for (size_t i = 0; i < node.OutputDefs().size(); ++i) { - ORT_RETURN_IF_NOT(split_sizes[i] == ShapeValue(node.OutputDefs()[i], gsl::narrow(axis)), - "split_sizes[i] != ShapeValue(node.OutputDefs()[i], axis)"); - } - - } else { - for (size_t i = 0; i < node.OutputDefs().size(); ++i) { - split_sizes.push_back(ShapeValue(node.OutputDefs()[i], gsl::narrow(axis))); - split_size_sum += split_sizes[i]; - } - } - - // check total size - if (ShapeHasValue(node.InputDefs()[0], axis)) { - int64_t input_axis_dim = ShapeValue(node.InputDefs()[0], axis); - if (split_size_sum != input_axis_dim) { - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, - "Cannot split using values in 'split' attribute. Axis=", axis, - " Dim being splitted=", input_axis_dim, - " Sum of sizes in 'split' (must equal size of selected axis) was ", split_size_sum); - } - } - - tvm::Array output_tensors = Split(inputs[0], ToTvmArray(split_sizes), axis, node.Name() + "_Split"); - for (size_t i = 0; i < node.OutputDefs().size(); ++i) { - outputs.push_back(output_tensors[i]); - } - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/transpose.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/transpose.cc deleted file mode 100644 index 43999ebd1f465..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/transpose.cc +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/transpose.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Transpose OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Transpose)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - size_t input_0_shape_rank = inputs[0]->shape.size(); - std::vector permute; - bool is_ok = attrs.GetAttrs("perm", permute).IsOK(); - if (permute.size() != 0 && permute.size() != input_0_shape_rank) - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Transpose: Incorrect permute size"); - - std::vector default_permute; - const std::vector* perm; - // either we don't have perm attribute or the perm attribute is empty - bool use_default_perm = !is_ok || permute.size() == 0; - if (use_default_perm) { - default_permute.resize(input_0_shape_rank); - for (size_t i = 0; i < input_0_shape_rank; ++i) { - default_permute[i] = gsl::narrow(input_0_shape_rank - 1 - i); - } - perm = &default_permute; - } else { - perm = &permute; - } - - tvm::Tensor Y = Transpose(inputs[0], ToTvmArrayInt(*perm), node.Name() + "_Transpose"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/where.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/where.cc deleted file mode 100644 index 9d6df7c1c430d..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/where.cc +++ /dev/null @@ -1,28 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/all_ops.h" - -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/mti/tensor/where.h" -#include "core/framework/op_kernel_info.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Evaluate of Transpose OpIRCreator -Status GENERIC_OP_IR_CREATOR_CLASS(Where)::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext&, - tvm::Array& outputs) { - ProtoHelperNodeContext ctx(node); - OpNodeProtoHelper attrs(&ctx); - - tvm::Tensor Y = Where(inputs[0], inputs[1], inputs[2], node.Name() + "_Where"); - outputs.push_back(Y); - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.cc deleted file mode 100644 index 7889e2add755e..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.cc +++ /dev/null @@ -1,125 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/tvm_ir_builder.h" - -#include "core/codegen/common/op_macro.h" -#include "core/codegen/passes/op_ir_creator/all_ops.h" -#include "core/common/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -TVMIRBuilder::TVMIRBuilder(const std::string& name) - : name_(name) {} - -const std::string& TVMIRBuilder::Name() const { - return name_; -} - -void TVMIRBuilder::InsertDispatcher(std::unique_ptr&& ptr) { - dispatchers_.push_back(std::move(ptr)); -} - -void TVMIRBuilder::ClearAllDispatchers() { - dispatchers_.clear(); -} - -void TVMIRBuilder::DumpAllOpIRCreators() const { - int count = 0; - for (auto& d : dispatchers_) { - std::cout << "************ TVM OpIRDispatcher " - << count << " : " - << d->Name() - << " ************" << std::endl; - - d->ForEach([](const std::string& key, OpIRCreator* builder) { - std::cout << "Key " << key - << ", Creator " << builder->Name() << std::endl; - }); - - ++count; - } -} - -// Evaluate finds ONE proper OpIRCreator and build the corresponding OpIR -// If a TVMIRBuilder has more than one OpIRCreator for an ORT Op, -// the first one will be used. -// Please adjust registration order and dispatcher in TVMIRBuilder -// to make sure the proper OpIRCreator is called. -Status TVMIRBuilder::Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx_codegen, - tvm::Array& outputs) { - OpIRCreator* candidate = nullptr; - for (auto& d : dispatchers_) { - candidate = d->Find(node); - if (nullptr != candidate) - break; - } - - if (nullptr == candidate) { - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Not implemented: ", node.OpType()); - } - - ORT_RETURN_IF_ERROR(candidate->Evaluate(inputs, node, ctx_codegen, outputs)); - - return Status::OK(); -} - -// BEGIN: Generic IR creator classes -#define ADD_OP_ITEM(name) \ - op_ir_registry->Register(std::make_unique()); - -#define BINARY_OP(name) ADD_OP_ITEM(name) -#define BINARY_CMP_OP(name) ADD_OP_ITEM(name) -#define POOL_OP(name) ADD_OP_ITEM(name) -#define REDUCE_OP(name) ADD_OP_ITEM(name) -#define REDUCE_INDEXED_OP(name) ADD_OP_ITEM(name) -#define UNARY_OP(name) ADD_OP_ITEM(name) -#define VARIADIC_OP(name) ADD_OP_ITEM(name) - -void RegisterAllGenericOpIRCreators(OpIRRegistry* op_ir_registry) { - LIST_ALL_GENERIC_OPS(); -} - -#undef ADD_OP_ITEM -#undef BINARY_OP -#undef BINARY_CMP_OP -#undef POOL_OP -#undef REDUCE_OP -#undef REDUCE_INDEXED_OP -#undef UNARY_OP -#undef VARIADIC_OP - -// BEGIN: Plugin Generic IR creator classes -#define ADD_OP_ITEM(name) \ - dispatcher->Register(#name, registry->Get(GENERIC_OP_IR_CREATOR_STRING(name))); - -#define BINARY_OP(name) ADD_OP_ITEM(name) -#define BINARY_CMP_OP(name) ADD_OP_ITEM(name) -#define POOL_OP(name) ADD_OP_ITEM(name) -#define REDUCE_OP(name) ADD_OP_ITEM(name) -#define REDUCE_INDEXED_OP(name) ADD_OP_ITEM(name) -#define UNARY_OP(name) ADD_OP_ITEM(name) -#define VARIADIC_OP(name) ADD_OP_ITEM(name) - -void RegisterGenericOrtOpTypeDispatcher(const std::shared_ptr& builder, - const OpIRRegistry* registry) { - auto dispatcher = std::make_unique("GenericOrtOpTypeOpIRCreators"); - LIST_ALL_GENERIC_OPS() - builder->InsertDispatcher(std::move(dispatcher)); -} - -#undef ADD_OP_ITEM -#undef BINARY_OP -#undef BINARY_CMP_OP -#undef POOL_OP -#undef REDUCE_OP -#undef REDUCE_INDEXED_OP -#undef UNARY_OP -// END: Generic IR creators classes - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.h b/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.h deleted file mode 100644 index c80056e619d6d..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_ir_builder.h +++ /dev/null @@ -1,64 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/passes/utils/codegen_context.h" -#include "core/codegen/passes/op_ir_creator/tvm_op_creator.h" -#include "core/common/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// TVMIRBuilder contains all applicable TVM OpIRCreators -// OpIRCreators are stored in multiple dispatchers -// that check different conditions of an ORT Node. - -// If an ORT Node satisfies more than one OpIRCreators, -// the first dispatched pass will be applied. - -class TVMIRBuilder { - public: - TVMIRBuilder(const std::string& name); - ~TVMIRBuilder() = default; - - // A debug dumps all existing in this TVMIRBuilders - void DumpAllOpIRCreators() const; - - // Evaluates an OpIRCreator that first satisfies condtions of all dispatchers - Status Evaluate( - const tvm::Array& inputs, - const Node& node, - CodeGenContext& ctx, - tvm::Array& outputs); - - // Inserts a dispatcher and move its ownership to this TVMIRBuilder - void InsertDispatcher(std::unique_ptr&& ptr); - - // Clears all dispatchers in this TVMIRBuilder - void ClearAllDispatchers(); - - // Dumps the name of this TVMIRBuilder - const std::string& Name() const; - - private: - std::vector> dispatchers_; - std::string name_; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(TVMIRBuilder); -}; - -// Utility function to register all builtin generic OpIRCreators into an OpIRRegistry. -// It creates instances of all generic OpIRCreators -// and registers them to op_ir_registry -void RegisterAllGenericOpIRCreators(OpIRRegistry* op_ir_registry); - -// Utility function to bind all builtin generic OpIRCreators to a TVMIRBuilder. -// It creates an instance of a Dispatcher that contains all generic OpIRCreators created above -// and uses OrtOpType to dispatch OpIRCreators. -// Then, it registers the created Dispatcher to a TVMIRBuilder, builder. -void RegisterGenericOrtOpTypeDispatcher(const std::shared_ptr& builder, - const OpIRRegistry* registry); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.cc deleted file mode 100644 index 992272753f5a4..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.cc +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/op_ir_creator/tvm_op_creator.h" - -#include "core/codegen/common/common.h" -#include "core/codegen/common/dispatcher.h" -#include "core/codegen/passes/utils/codegen_context.h" - -namespace onnxruntime { -namespace codegen { -// Explicit instantiation for OpIRCreator -template class CreatorBase&, - const Node&, - tvm_codegen::CodeGenContext&, - tvm::Array&, - Status>; - -// Explicit instantiation for OpIRCreators' dispatcher -template class DispatcherBase; - -} // namespace codegen - -namespace tvm_codegen { - -// One dispatcher is based on ORT OpType -OpIRCreator* OP_IR_DISPATCHER_CLASS(OpType)::Find(const Node& node) { - return DispatcherBase::Get(node.OpType()); -} - -// Another dispatcher is based ORT NodeArg name (GetKey) -OpIRCreator* OP_IR_DISPATCHER_CLASS(NodeName)::Find(const Node& node) { - return DispatcherBase::Get(GetKey(&node)); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.h b/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.h deleted file mode 100644 index e29c4a9f20767..0000000000000 --- a/onnxruntime/core/codegen/passes/op_ir_creator/tvm_op_creator.h +++ /dev/null @@ -1,84 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/creator.h" -#include "core/codegen/common/dispatcher.h" -#include "core/codegen/common/registry.h" -#include "core/graph/graph.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -class CodeGenContext; - -// OpIRCreator lowers an Ort Node to its corresponding TVM IRs -using OpIRCreator = codegen::CreatorBase< - const tvm::Array&, - const Node&, - CodeGenContext&, - tvm::Array&, - Status>; - -// OpIRDispatcher is the base dispatcher for TVM IR Builder -// It checks whether an Ort Node satisfying a criteria (in Find) -// and dispatches a corresponding OpIRCreator. -class OpIRDispatcher : public codegen::DispatcherBase { - public: - OpIRDispatcher(const std::string& name) - : DispatcherBase(name) {} - - virtual ~OpIRDispatcher() = default; - - virtual OpIRCreator* Find(const Node&) = 0; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(OpIRDispatcher); -}; - -// Macro returns an OpIRCreators' dispatcher's name -#define OP_IR_DISPATCHER_CLASS(OP) \ - TVM##OP##IRCreator - -// Macro declares an OpIRCreators' dispatcher -#define DECLARE_OP_IR_DISPATCHER_CLASS(OP) \ - class OP_IR_DISPATCHER_CLASS(OP) : public OpIRDispatcher { \ - public: \ - TVM##OP##IRCreator(const std::string& name) \ - : OpIRDispatcher(name) {} \ - ~TVM##OP##IRCreator() = default; \ - OpIRCreator* Find(const Node&) override; \ - \ - private: \ - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(OP_IR_DISPATCHER_CLASS(OP)); \ - }; - -// Declare two common dispatchers for TVM Op IR builders -// One dispatcher is based on Ort OpType -DECLARE_OP_IR_DISPATCHER_CLASS(OpType) -// Another dispatcher is based Ort NodeArg name -DECLARE_OP_IR_DISPATCHER_CLASS(NodeName) - -// OpIRCreator Registry is a registry holds all OpIRCreators -using OpIRRegistry = codegen::RegistryBase; - -// Macro declares an OpIRCreator -#define DECLARE_OP_IR_CREATOR_CLASS(OP, PREFIX) \ - DECLARE_CREATOR_CLASS(OP, PREFIX##IRCreator, \ - const tvm::Array&, \ - const Node&, \ - tvm_codegen::CodeGenContext&, \ - tvm::Array&, \ - Status) - -// Macro returns an OpIRCreator's name with prefix -#define OP_IR_CREATOR_CLASS_EX(OP, PREFIX, ARCH) \ - CREATOR_CLASS(OP, PREFIX##ARCH##IRCreator) - -// Macro declares an OpIRCreator with prefix and arch -#define DECLARE_OP_IR_CREATOR_CLASS_EX(OP, PREFIX, ARCH) \ - DECLARE_OP_IR_CREATOR_CLASS(OP, PREFIX##ARCH) - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/all_schedules.h b/onnxruntime/core/codegen/passes/scheduler/all_schedules.h deleted file mode 100644 index fe4be90f9fc84..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/all_schedules.h +++ /dev/null @@ -1,20 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/passes/scheduler/tvm_scheduler.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// AlwaysRoot is for debug purpose -DECLARE_TVM_SCHEDULER_CLASS(AlwaysRoot, GenericTVMRule) -// Create schedule for TVM Rule -DECLARE_TVM_SCHEDULER_CLASS(Extern, GenericTVMRule) -DECLARE_TVM_SCHEDULER_CLASS(Reduce, GenericTVMRule) - -// Crete scheduler for ORT OpType, Softmax -DECLARE_TVM_SCHEDULER_CLASS(Softmax, GenericOrtOpType) - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/ort_type_schedule.cc b/onnxruntime/core/codegen/passes/scheduler/ort_type_schedule.cc deleted file mode 100644 index 59f492d164b14..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/ort_type_schedule.cc +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/scheduler/all_schedules.h" - -#include "core/codegen/passes/scheduler/schedule_utils.h" - -namespace onnxruntime { -namespace tvm_codegen { - -bool TVM_SCHEDULER_CLASS(Softmax, GenericOrtOpType)::Evaluate( - const tvm::Tensor& tensor, - const Node*, - CodeGenContext&, - ScheduleContext& ctx_sched) { - // compute root the exp since it is reused more than once - auto& tensor_exp = tensor->op->InputTensors()[0]; - return InsertRootSchedule(tensor_exp, ctx_sched); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/schedule_utils.cc b/onnxruntime/core/codegen/passes/scheduler/schedule_utils.cc deleted file mode 100644 index 76c2ad509c401..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/schedule_utils.cc +++ /dev/null @@ -1,178 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/common/utils.h" -#include "core/codegen/passes/scheduler/schedule_utils.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// Check the schedule of tensor -// If it has no compute_root, Insert compute_root to tensor, and record it to ctx.scheduled_tensors -bool InsertRootSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx) { - auto it = ctx.scheduled_tensors.find(tensor->op.get()); - if (it != ctx.scheduled_tensors.end()) { - if (it->second == ScheduleType::ScheduleClosure || - it->second == ScheduleType::ScheduleRoot) { - return false; - } - it->second = ScheduleType::ScheduleRoot; - } else { - ctx.scheduled_tensors.insert(std::make_pair(tensor->op.get(), ScheduleType::ScheduleRoot)); - } - ctx.schedule[tensor->op].compute_root(); - return true; -} - -// Check the schedule of tensor -// If it is not labeled as closure, lable it. -bool InsertClosure(const tvm::Tensor& tensor, - ScheduleContext& ctx) { - auto it = ctx.scheduled_tensors.find(tensor->op.get()); - if (it != ctx.scheduled_tensors.end()) { - if (it->second == ScheduleType::ScheduleClosure) - return false; - it->second = ScheduleType::ScheduleClosure; - } else { - ctx.scheduled_tensors.insert(std::make_pair(tensor->op.get(), ScheduleType::ScheduleClosure)); - } - return true; -} - -// Combination of InsertRootSchedule and InsertClosure -bool InsertRootScheduleAndClosure( - const tvm::Tensor& tensor, - ScheduleContext& ctx) { - auto it = ctx.scheduled_tensors.find(tensor->op.get()); - if (it != ctx.scheduled_tensors.end()) { - if (it->second == ScheduleType::ScheduleClosure) { - return false; - } - it->second = ScheduleType::ScheduleClosure; - } else { - ctx.scheduled_tensors.insert(std::make_pair(tensor->op.get(), ScheduleType::ScheduleClosure)); - } - ctx.schedule[tensor->op].compute_root(); - return true; -} - -// Check precondition for vectorize schedule -bool ShouldTryVectorization( - const tvm::Tensor& tensor, - ScheduleContext& ctx) { - auto it = ctx.scheduled_tensors.find(tensor->op.get()); - if (it != ctx.scheduled_tensors.end()) { - if (it->second > ScheduleType::ScheduleInline) { - return false; - } - } - return true; -} - -// Check the schedule of tensor -// If it is not scheduled, try to vectorize it. -// Note TryVectorization has to use with compute_root. -// Therefore, there is a safety check of tensor's schedule -bool TryVectorization( - const tvm::Tensor& tensor, - int64_t natural_vector_size, - ScheduleContext& ctx) { - if (!ShouldTryVectorization(tensor, ctx)) - return false; - - auto shape = tensor->shape; - auto rank = shape.size(); - if (rank < 1) { - return false; - } - const int64_t* tail_dim = as_const_int(shape[rank - 1]); - - if (nullptr != tail_dim) { - auto extern_op = tensor->op.as(); - if (nullptr != extern_op) { - return false; - } - - auto compute_op = tensor->op.as(); - - if (nullptr != compute_op) { - auto axis = compute_op->axis; - tvm::IterVar x = axis[rank - 1]; - if ((*tail_dim) > natural_vector_size) { - if ((*tail_dim) % natural_vector_size != 0) { - natural_vector_size = GCD(natural_vector_size, (*tail_dim)); - } - - if (natural_vector_size > 1) { - tvm::IterVar xi, xo; - ctx.schedule[tensor->op].split(x, static_cast(natural_vector_size), &xo, &xi); - ctx.schedule[tensor->op].vectorize(xi); - return true; - } - } else if (*tail_dim > 0) { - // don't vectorize if dim is 0 - ctx.schedule[tensor->op].vectorize(x); - return true; - } - } - } - return false; -} - -// Check the schedule of tensor -// If it is not scheduled, try to add compute_inline on it. -// Note TryInlineSchedule cannot be used with compute_root. -// Therefore, there is a safety check of tensor's schedule. -bool TryInlineSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx) { - auto it = ctx.scheduled_tensors.find(tensor->op.get()); - if (it != ctx.scheduled_tensors.end()) { - if ((int)it->second < (int)ScheduleType::ScheduleInline) { - ctx.schedule[tensor->op].compute_inline(); - it->second = ScheduleType::ScheduleInline; - return true; - } else { - return false; - } - } - ctx.schedule[tensor->op].compute_inline(); - ctx.scheduled_tensors.insert(std::make_pair(tensor->op.get(), ScheduleType::ScheduleInline)); - return true; -} - -// Check the schedule of tensor's inputs, and call InsertRootSchedule for each of them -bool InputRootSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx) { - bool status = false; - for (auto& t : tensor->op->InputTensors()) { - if (t->op->InputTensors().size() > 0) { - bool status_root = InsertRootSchedule(t, ctx); - status = status || status_root; - } - } - return status; -} - -// Check the schedule of tensor's inputs, -// and call InsertRootSchedule and TryVectorization for each of them -bool InputRootScheduleWithVectorization( - const tvm::Tensor& tensor, - int64_t natural_vector_size, - ScheduleContext& ctx) { - bool status = false; - for (auto& t : tensor->op->InputTensors()) { - if (t->op->InputTensors().size() > 0) { - bool status_vec = TryVectorization(t, natural_vector_size, ctx); - bool status_root = InsertRootSchedule(t, ctx); - status = status || status_root || status_vec; - } - } - return status; -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/schedule_utils.h b/onnxruntime/core/codegen/passes/scheduler/schedule_utils.h deleted file mode 100644 index 4a0781f94d385..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/schedule_utils.h +++ /dev/null @@ -1,65 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// Check the schedule of tensor -// If it has no compute_root, Insert compute_root to tensor, -// and record it to ctx.scheduled_tensors -bool InsertRootSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Check the schedule of tensor -// If it is not labeled as closure, lable it. -bool InsertClosure( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Combination of InsertRootSchedule and InsertClosure -bool InsertRootScheduleAndClosure( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Check precondition for vectorize schedule -bool ShouldTryVectorization( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Check the schedule of tensor -// If it is not scheduled, try to vectorize it. -// Note TryVectorization has to use with compute_root. -// Therefore, there is a safety check of tensor's schedule -bool TryVectorization( - const tvm::Tensor& tensor, - int64_t natural_vector_size, - ScheduleContext& ctx); - -// Check the schedule of tensor -// If it is not scheduled, try to add compute_inline on it. -// Note TryInlineSchedule cannot be used with compute_root. -// Therefore, there is a safety check of tensor's schedule. -bool TryInlineSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Check the schedule of tensor's inputs, -// and call InsertRootSchedule for each of them -bool InputRootSchedule( - const tvm::Tensor& tensor, - ScheduleContext& ctx); - -// Check the schedule of tensor's inputs, -// and call InsertRootSchedule and TryVectorization for each of them -bool InputRootScheduleWithVectorization( - const tvm::Tensor& tensor, - int64_t natural_vector_size, - ScheduleContext& ctx); - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/tvm_rule_schedule.cc b/onnxruntime/core/codegen/passes/scheduler/tvm_rule_schedule.cc deleted file mode 100644 index 33162deddc983..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/tvm_rule_schedule.cc +++ /dev/null @@ -1,41 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/scheduler/all_schedules.h" - -#include "core/codegen/passes/scheduler/schedule_utils.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// This is for debug -bool TVM_SCHEDULER_CLASS(AlwaysRoot, GenericTVMRule)::Evaluate( - const tvm::Tensor& tensor, - const Node*, - CodeGenContext&, - ScheduleContext& ctx_sched) { - return InsertRootSchedule(tensor, ctx_sched); -} - -// For External tvm::Tensor -bool TVM_SCHEDULER_CLASS(Extern, GenericTVMRule)::Evaluate( - const tvm::Tensor& tensor, - const Node*, - CodeGenContext&, - ScheduleContext& ctx_sched) { - bool status = InsertRootScheduleAndClosure(tensor, ctx_sched); - bool status_input = InputRootSchedule(tensor, ctx_sched); - return status || status_input; -} - -// For Reduce Compute tvm::Tensor -bool TVM_SCHEDULER_CLASS(Reduce, GenericTVMRule)::Evaluate( - const tvm::Tensor& tensor, - const Node*, - CodeGenContext&, - ScheduleContext& ctx_sched) { - return InsertRootScheduleAndClosure(tensor, ctx_sched); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.cc b/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.cc deleted file mode 100644 index 2c8250198fa5f..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.cc +++ /dev/null @@ -1,104 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/scheduler/tvm_schedule_builder.h" - -#include "core/codegen/common/op_macro.h" -#include "core/codegen/common/settings.h" -#include "core/common/common.h" -#include "core/common/logging/logging.h" - -namespace onnxruntime { -namespace tvm_codegen { - -TVMScheduleBuilder::TVMScheduleBuilder(const std::string& name) - : name_(name) { -} - -const std::string& TVMScheduleBuilder::Name() const { - return name_; -} - -void TVMScheduleBuilder::InsertDispatcher(std::unique_ptr&& ptr) { - dispatchers_.push_back(std::move(ptr)); -} - -void TVMScheduleBuilder::ClearDispatcher() { - dispatchers_.clear(); -} - -void TVMScheduleBuilder::DumpAllSchedulers() const { - std::ostringstream stream; - int count = 0; - stream << "[CODEGEN_DUMP_SCHEDULE]" << std::endl; - for (auto& d : dispatchers_) { - stream << "************ TVM Scheduler Dispatcher " - << count << " : " - << d->Name() - << " ************" << std::endl; - - d->ForEach([&stream](const std::string& key, Scheduler* op) { - stream << "Key " << key - << ", Creator " << op->Name() << std::endl; - }); - - ++count; - } - - LOGS_DEFAULT(CODEGEN_SETTINGS_LOG_LEVEL) << stream.str(); -} - -Status TVMScheduleBuilder::Evaluate( - const tvm::Tensor& tensor, - const Node* node, - CodeGenContext& ctx_codegen, - ScheduleContext& sched) { - Scheduler* candidate = nullptr; - - for (auto& d : dispatchers_) { - candidate = d->Find(tensor, node, ctx_codegen); - if (nullptr != candidate) - break; - } - - bool enable_dump_schedule = codegen::CodeGenSettings::Instance().HasOption(codegen::CodeGenSettings::kCodeGenDumpSchedule); - - if (nullptr == candidate) { - if (nullptr != node) - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Not implemented: ", node->OpType()); - return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Not implemented an internal tvm::Tensor: ", tensor->op->name); - } - - bool status = candidate->Evaluate(tensor, node, ctx_codegen, sched); - - if (enable_dump_schedule) { - std::ostringstream stream; - if (nullptr != node) { - stream << std::endl; - stream << "[CODEGEN_DUMP_SCHEDULE] " - << "Schedule Node: " << node->Name() << std::endl; - } else { - stream << std::endl; - } - - if (status) { - stream << "[CODEGEN_DUMP_SCHEDULE] " - << "Schedule tvm::Tesnor " - << tensor->op->name - << " with " - << candidate->Name() << std::endl; - } else { - stream << "[CODEGEN_DUMP_SCHEDULE] " - << "Schedule tvm::Tesnor " - << tensor->op->name - << " is suppressed " << std::endl; - } - - LOGS_DEFAULT(CODEGEN_SETTINGS_LOG_LEVEL) << stream.str(); - } - - return Status::OK(); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.h b/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.h deleted file mode 100644 index 9f0a1b3ef45c2..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/tvm_schedule_builder.h +++ /dev/null @@ -1,46 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/passes/scheduler/tvm_scheduler.h" -#include "core/common/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -// TVMScheduleBuilder contains all applicable TVM scheduler passes. -// Scheduler passes are stored in multiple dispatchers -// that check different conditions of a tvm::Tensor. - -// If a tvm::Tensor satisfies more than one TVM scheduler passes, -// the first dispatched pass will be applied. - -class TVMScheduleBuilder { - public: - // TODO: add more parameter in consructor to support different target - TVMScheduleBuilder(const std::string& name); - ~TVMScheduleBuilder() = default; - - void DumpAllSchedulers() const; - - Status Evaluate( - const tvm::Tensor& tensor, - const Node* node, - CodeGenContext& ctx, - ScheduleContext& sched); - - void InsertDispatcher(std::unique_ptr&& ptr); - void ClearDispatcher(); - - const std::string& Name() const; - - private: - std::vector> dispatchers_; - std::string name_; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(TVMScheduleBuilder); -}; - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.cc b/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.cc deleted file mode 100644 index 071200a234e33..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.cc +++ /dev/null @@ -1,79 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/scheduler/tvm_scheduler.h" - -#include "core/codegen/common/common.h" -#include "core/codegen/common/dispatcher.h" -#include "core/codegen/passes/utils/codegen_context.h" - -namespace onnxruntime { -namespace codegen { -// explicit instantiation -template class CreatorBase; - -template class DispatcherBase; - -} // namespace codegen - -namespace tvm_codegen { - -static const std::string TMVOpRuleKey_Extern("TVMOpRule_Extern"); -static const std::string TMVOpRuleKey_ComputeReduce("TVMOpRule_ComputeReduce"); -static const std::string TMVOpRuleKey_ComputeRegular("TVMOpRule_ComputeRegular"); -static const std::string TMVOpRuleKey_AlwaysRoot("TMVOpRuleKey_AlwaysRoot"); -static const std::string TMVOpRuleKey_NoRule("TVMOpRule_NoRule"); - -const std::string& GetTVMOpRule(TVMOpRuleType rule) { - if (rule == TVMOpRuleType::Extern) { - return TMVOpRuleKey_Extern; - } else if (rule == TVMOpRuleType::ComputeReduce) { - return TMVOpRuleKey_ComputeReduce; - } else if (rule == TVMOpRuleType::AlwaysRoot) { - return TMVOpRuleKey_AlwaysRoot; - } - return TMVOpRuleKey_NoRule; -} - -const std::string& GetTVMOpRule(const tvm::Tensor& tensor) { - auto extern_op = tensor->op.as(); - - if (nullptr != extern_op) { - return TMVOpRuleKey_Extern; - } - - auto compute_op = tensor->op.as(); - if (nullptr != compute_op) { - if (compute_op->reduce_axis.size() > 0) { - return TMVOpRuleKey_ComputeReduce; - } - } - - return TMVOpRuleKey_NoRule; -} - -Scheduler* SCHEDULE_DISPATCHER_CLASS(OrtOpType):: - Find(const tvm::Tensor&, const Node* node, tvm_codegen::CodeGenContext&) { - if (nullptr == node) - return nullptr; - return DispatcherBase::Get(node->OpType()); -} - -Scheduler* SCHEDULE_DISPATCHER_CLASS(TVMOpRule):: - Find(const tvm::Tensor& tensor, const Node*, tvm_codegen::CodeGenContext&) { - return DispatcherBase::Get(GetTVMOpRule(tensor)); -} - -Scheduler* SCHEDULE_DISPATCHER_CLASS(OrtOpName):: - Find(const tvm::Tensor&, const Node* node, tvm_codegen::CodeGenContext&) { - if (nullptr == node) - return nullptr; - return DispatcherBase::Get(GetKey(node)); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.h b/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.h deleted file mode 100644 index d022497c77f7e..0000000000000 --- a/onnxruntime/core/codegen/passes/scheduler/tvm_scheduler.h +++ /dev/null @@ -1,128 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/common/common.h" -#include "core/codegen/common/creator.h" -#include "core/codegen/common/registry.h" -#include "core/codegen/passes/utils/codegen_context.h" -#include "core/graph/graph.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// These are current generic TVMOpRule we used. -enum class TVMOpRuleType : int { - Extern = 0, - ComputeReduce = 1, - ComputeRegular = 2, - AlwaysRoot = 3, // for debug - NoRule, -}; - -const std::string& GetTVMOpRule(const tvm::Tensor& tensor); -const std::string& GetTVMOpRule(TVMOpRuleType rule); - -// These are current generic ScheduleType in tvm_codegen -enum class ScheduleType : int { - ScheduleNone = 0, - ScheduleInline = 1, - ScheduleAt = 2, - ScheduleRoot = 3, - ScheduleClosure = 4, -}; - -// Data struct to bundle tvm::Schedule and scheduled tensor -struct ScheduleContext { - ScheduleContext(const tvm::Array& ops) { - schedule = tvm::create_schedule(ops); - } - tvm::Schedule schedule; - std::map scheduled_tensors; -}; - -// Scheduler inserts a tvm::Schedule content to a tvm::Tensor -using Scheduler = codegen::CreatorBase< - const tvm::Tensor&, - const Node*, - tvm_codegen::CodeGenContext&, - ScheduleContext&, - bool>; - -// TVMScheduleDispatcher is the base dispatcher for TVM Schedule Builder -// It checks whether a pair of {tvm::Tensor, Ort Node} satisfying a criteria (in Find) -// and dispatches a corresponding Scheduler. -class TVMScheduleDispatcher : public codegen::DispatcherBase { - public: - TVMScheduleDispatcher(const std::string& name) - : DispatcherBase(name) {} - - virtual ~TVMScheduleDispatcher() = default; - - virtual Scheduler* Find(const tvm::Tensor&, - const Node*, - tvm_codegen::CodeGenContext&) = 0; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(TVMScheduleDispatcher); -}; - -// Macro returns an Schedulers' dispatcher's name -#define SCHEDULE_DISPATCHER_CLASS(TYPE) \ - TVM##TYPE##Schedulers - -// Macro declares an Schedulers' dispatcher -#define DECLARE_SCHEDULE_DISPATCHER_CLASS(TYPE) \ - class SCHEDULE_DISPATCHER_CLASS(TYPE) : public tvm_codegen::TVMScheduleDispatcher { \ - public: \ - TVM##TYPE##Schedulers(const std::string& name) \ - : TVMScheduleDispatcher(name) {} \ - ~TVM##TYPE##Schedulers() = default; \ - tvm_codegen::Scheduler* Find(const tvm::Tensor&, \ - const Node*, \ - tvm_codegen::CodeGenContext&) override; \ - \ - private: \ - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(TVM##TYPE##Schedulers); \ - }; - -// Common dispatchers are listed here -// For a special pattern, it can be created later. -// One dispatcher is based on Ort OpType -DECLARE_SCHEDULE_DISPATCHER_CLASS(OrtOpType) -// One dispatcher is based on TVMOpRule -DECLARE_SCHEDULE_DISPATCHER_CLASS(TVMOpRule) -// One dispatcher is based Ort NodeArg name -DECLARE_SCHEDULE_DISPATCHER_CLASS(OrtOpName) - -// Scheduler Registry is a registry holds all Schedulers -using TVMScheduleRegistry = codegen::RegistryBase; - -// Macro declares TVM scheduler class -#define DECLARE_TVM_SCHEDULER_CLASS(OP, PRETFIX) \ - DECLARE_CREATOR_CLASS(OP, PRETFIX##Scheduler, \ - const tvm::Tensor&, \ - const Node*, \ - tvm_codegen::CodeGenContext&, \ - tvm_codegen::ScheduleContext&, \ - bool) - -// Macro returns TVM scheduler's name with prefix -#define TVM_SCHEDULER_CLASS(OP, PREFIX) \ - CREATOR_CLASS(OP, PREFIX##Scheduler) - -// Macro returns TVM scheduler's name as string -#define TVM_SCHEDULER_STRING(OP, PREFIX) \ - STRINGIZE(TVM_SCHEDULER_CLASS(OP, PREFIX)) - -// Macro returns TVM scheduler's name with prefix and arch -#define TVM_SCHEDULER_CLASS_EX(OP, PREFIX, ARCH) \ - CREATOR_CLASS(OP, PREFIX##ARCH##Scheduler) - -// Macro declares TVM scheduler class with prefix and arch -#define DECLARE_TVM_SCHEDULER_CLASS_EX(OP, PREFIX, ARCH) \ - DECLARE_TVM_SCHEDULER_CLASS(OP, PREFIX##ARCH) - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/utils/codegen_context.cc b/onnxruntime/core/codegen/passes/utils/codegen_context.cc deleted file mode 100644 index 2f1a59b4a92eb..0000000000000 --- a/onnxruntime/core/codegen/passes/utils/codegen_context.cc +++ /dev/null @@ -1,27 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/utils/codegen_context.h" - -#include "core/codegen/common/common.h" - -namespace onnxruntime { -namespace tvm_codegen { - -CodeGenContext::CodeGenContext( - const codegen::CodeGenHandle* handle) - : handle_(handle), unname_symbol_counter_(0) {} - -tvm::Var CodeGenContext::GetOrCreateDynamicDim(const std::string& name) { - if (dynamic_dims_.count(name) == 0) - dynamic_dims_.emplace(name, tvm::Var(name)); - - return dynamic_dims_.at(name); -} - -std::string CodeGenContext::CreateUnnamedSymbol() { - return "unnamed_" + std::to_string(unname_symbol_counter_++); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/utils/codegen_context.h b/onnxruntime/core/codegen/passes/utils/codegen_context.h deleted file mode 100644 index 641552bd3b2e8..0000000000000 --- a/onnxruntime/core/codegen/passes/utils/codegen_context.h +++ /dev/null @@ -1,44 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/handle.h" -#include "core/codegen/common/common.h" -#include "core/common/common.h" -#include "core/framework/data_types.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// CodeGenContext is a data structure involving across passes -// Compiler developers can use it to store meta data -// to support fine-grained control of code generation -class CodeGenContext { - public: - CodeGenContext(const codegen::CodeGenHandle* handle); - - virtual ~CodeGenContext() = default; - - // returns tvm::Var for the dynamic dim - tvm::Var GetOrCreateDynamicDim(const std::string& name); - - const codegen::CodeGenHandle* GetCodeGenHandle() const { - return handle_; - } - - std::string CreateUnnamedSymbol(); - - protected: - std::unordered_map dynamic_dims_; - - const codegen::CodeGenHandle* handle_; - - int unname_symbol_counter_; -}; - -// Add Promote for CodeGenContext -DYNAMIC_PROMOTE(CodeGenContext) - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.cc b/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.cc deleted file mode 100644 index 55892974aa33f..0000000000000 --- a/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.cc +++ /dev/null @@ -1,194 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/utils/ort_tvm_utils.h" - -#include "core/codegen/common/profile.h" -#include "core/codegen/passes/utils/codegen_context.h" -#include "core/framework/tensorprotoutils.h" -#include "core/providers/common.h" -#include - -#include - -namespace onnxruntime { -namespace tvm_codegen { - -#define RETURN_DLDATATYPE_IF_MATCH(type_enum, type, type_code) \ - case type_enum: \ - return {type_code, sizeof(type) * 8, 1}; \ - break; - -// DLDataType: {DLDataTypeCode, bits, lanes} -DLDataType ToTvmDLDataType(MLDataType ml_type) { - if (ml_type->IsTensorType()) { - ml_type = ml_type->AsTensorType()->GetElementType(); - } - auto prim_type = ml_type->AsPrimitiveDataType(); - if (prim_type == nullptr) { - ORT_NOT_IMPLEMENTED("converting MLDataType ", ml_type, " to tvm DLDataType is not implemented"); - } - - switch (prim_type->GetDataType()) { - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_INT8, int8_t, kDLInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_UINT8, uint8_t, kDLUInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_INT16, int16_t, kDLInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_UINT16, uint16_t, kDLUInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_INT32, int32_t, kDLInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_UINT32, uint32_t, kDLUInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_INT64, int64_t, kDLInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_UINT64, uint64_t, kDLUInt); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_BOOL, bool, kDLUInt); - - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, float, kDLFloat); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_DOUBLE, double, kDLFloat); - RETURN_DLDATATYPE_IF_MATCH(ONNX_NAMESPACE::TensorProto_DataType_FLOAT16, MLFloat16, kDLFloat); - default: - ORT_NOT_IMPLEMENTED("converting MLDataType ", ml_type, " to tvm DLDataType is not implemented"); - } -} - -tvm::Type ToTvmType(ONNX_NAMESPACE::TensorProto_DataType proto_type) { - switch (proto_type) { - // Note that bool is uint1 in tvm, but uint8 in ONNX, so it always require special handling - // case ONNX_NAMESPACE::TensorProto_DataType_BOOL: - // return tvm::UInt(1); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_INT16: - return tvm::Int(16); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_INT32: - return tvm::Int(32); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_INT64: - return tvm::Int(64); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_UINT8: - return tvm::UInt(8); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_UINT16: - return tvm::UInt(16); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_UINT32: - return tvm::UInt(32); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_UINT64: - return tvm::UInt(64); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: - return tvm::Float(32); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: - return tvm::Float(64); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_INT8: - return tvm::Int(8); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16: - return tvm::Float(16); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_STRING: - ORT_THROW("Casting to and from strings is not supported yet."); /*break;*/ - case ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED: - ORT_THROW("Cast op must have 'to' argument of type DataType"); /*break;*/ - default: - ORT_THROW("Unexpected 'to' argument value: ", proto_type); - } -} - -tvm::Array ShapeToTvmArray(const NodeArg* def, CodeGenContext& ctx) { - ORT_ENFORCE(nullptr != def); - const ONNX_NAMESPACE::TensorShapeProto* shape_proto = def->Shape(); - ORT_ENFORCE(nullptr != shape_proto); - - tvm::Array arr; - for (int i = 0; i < shape_proto->dim_size(); ++i) { - arr.push_back(ShapeDimToTvmDim(shape_proto->dim(i), ctx)); - } - return arr; -} - -tvm::Expr ShapeDimToTvmDim(const ONNX_NAMESPACE::TensorShapeProto_Dimension& dim, CodeGenContext& ctx) { - if (utils::HasDimParam(dim)) { - return ctx.GetOrCreateDynamicDim(dim.dim_param()); - } else if (utils::HasDimValue(dim)) { - return tvm::Expr(gsl::narrow_cast(dim.dim_value())); - } - return ctx.GetOrCreateDynamicDim(ctx.CreateUnnamedSymbol()); -} - -#ifdef CODEGEN_ENABLE_PROFILER -struct event_in_bracket_and_id { - bool in_bracket; - size_t id; -}; -std::unordered_map g_codegen_profiler_event_ids; -std::vector> g_codegen_profiler_events(1024); - -TVM_REGISTER_GLOBAL("tvm.contrib.onnxruntime.profile_event") - .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* ret) { - DLTensor* X = args[0]; - DLTensor* Y = args[1]; - size_t event_id = args[2]; - bool is_begin = args[3]; - if (!is_begin) { - DCHECK(event_id < g_codegen_profiler_event_ids.size()); - profiling::Profiler::Instance().EndTimeAndRecordEvent( - profiling::EventCategory::NODE_EVENT, - g_codegen_profiler_events[event_id].first, - g_codegen_profiler_events[event_id].second); - } - - { - CODEGEN_PROFILER_EVENT("profile_stub"); - int64_t elem_count = 1; - for (int i = 0; i < X->ndim; ++i) { - elem_count *= X->shape[i]; - } - // there's overhead in this copy, so put begin after copy and end before copy - memcpy(static_cast(Y->data) + Y->byte_offset, - static_cast(X->data) + X->byte_offset, - elem_count * X->dtype.bits / 8); - } - - if (is_begin) { - DCHECK(g_codegen_profiler_events.size() > event_id); - DCHECK(!g_codegen_profiler_events[event_id].first.empty()); - DCHECK(g_codegen_profiler_event_ids[g_codegen_profiler_events[event_id].first].id == event_id); - g_codegen_profiler_events[event_id].second = - profiling::Profiler::Instance().StartTime(); - } - }); - -tvm::Tensor ProfileBegin(tvm::Tensor X, const std::string& event_name) { - size_t event_id; - if (g_codegen_profiler_event_ids.count(event_name) == 0) { - event_id = g_codegen_profiler_event_ids.size(); - ORT_ENFORCE(event_id < g_codegen_profiler_events.size()); - } else { - ORT_ENFORCE(!g_codegen_profiler_event_ids[event_name].in_bracket); - event_id = g_codegen_profiler_event_ids[event_name].id; - } - g_codegen_profiler_event_ids[event_name] = {true, event_id}; - g_codegen_profiler_events[event_id].first = event_name; - return topi::detail::make_extern( - {X->shape}, {X->dtype}, {X}, - [&](tvm::Array ins, tvm::Array outs) { - return topi::detail::call_packed({tvm::Expr("tvm.contrib.onnxruntime.profile_event"), - topi::detail::pack_buffer(ins[0]), - topi::detail::pack_buffer(outs[0]), - gsl::narrow(event_id), - true}); - }, - event_name + "_begin", "", {})[0]; -} - -tvm::Tensor ProfileEnd(tvm::Tensor X, const std::string& event_name) { - ORT_ENFORCE(g_codegen_profiler_event_ids.at(event_name).in_bracket); - g_codegen_profiler_event_ids.at(event_name).in_bracket = false; - size_t event_id = g_codegen_profiler_event_ids.at(event_name).id; - ORT_ENFORCE(event_id < g_codegen_profiler_events.size()); - ORT_ENFORCE(g_codegen_profiler_events[event_id].first == event_name); - return topi::detail::make_extern( - {X->shape}, {X->dtype}, {X}, - [&](tvm::Array ins, tvm::Array outs) { - return topi::detail::call_packed({tvm::Expr("tvm.contrib.onnxruntime.profile_event"), - topi::detail::pack_buffer(ins[0]), - topi::detail::pack_buffer(outs[0]), - gsl::narrow(event_id), - false}); - }, - event_name + "_end", "", {})[0]; -} -#endif - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.h b/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.h deleted file mode 100644 index f13e91a2d5cea..0000000000000 --- a/onnxruntime/core/codegen/passes/utils/ort_tvm_utils.h +++ /dev/null @@ -1,31 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/common.h" -#include "core/framework/data_types.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -class CodeGenContext; - -// Helper function that converts a onnxruntime MLDataType to TVM DLDataType -DLDataType ToTvmDLDataType(MLDataType ml_type); - -tvm::Type ToTvmType(ONNX_NAMESPACE::TensorProto_DataType proto_type); - -tvm::Array ShapeToTvmArray(const NodeArg* def, CodeGenContext& ctx); - -tvm::Expr ShapeDimToTvmDim(const ONNX_NAMESPACE::TensorShapeProto_Dimension& dim, CodeGenContext& ctx); - -#ifdef CODEGEN_ENABLE_PROFILER -// Helper functions to inspect into lowered function -tvm::Tensor ProfileBegin(tvm::Tensor X, const std::string& event_name); - -tvm::Tensor ProfileEnd(tvm::Tensor X, const std::string& event_name); -#endif - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.cc b/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.cc deleted file mode 100644 index c65132f6d4bca..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.cc +++ /dev/null @@ -1,105 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/weight_layout/tiling_2d.h" - -#include "core/codegen/passes/utils/codegen_context.h" - -namespace onnxruntime { -namespace tvm_codegen { - -constexpr auto local_name_prefix = "tiling_2d_"; -constexpr int num_bits = 8; - -const std::string WeightLayoutTiling2D::GetKey( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int vector_width) { - return WeightLayout::GetKey( - local_name_prefix + std::to_string(vector_width), - proto_type, 2, 0.0f); -} - -WeightLayoutTiling2D::WeightLayoutTiling2D( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int vector_width) - : WeightLayout( - local_name_prefix + std::to_string(vector_width), - proto_type, 2, 0.0f), - vector_width_(vector_width) {} - -CoordTransFunc WeightLayoutTiling2D::ToActual(const tvm::Tensor& /*X*/) const { - return [&](const tvm::Array& nominal_coord) { - ORT_ENFORCE(nominal_coord.size() == 2); - const auto& y = nominal_coord[0]; - const auto& x = nominal_coord[1]; - return tvm::Array{ - x, - y}; - }; -} - -CoordTransFunc WeightLayoutTiling2D::ToNominal(const tvm::Tensor& X) const { - return [&](const tvm::Array& actual_coord) { - ORT_ENFORCE(actual_coord.size() == 2); - ORT_ENFORCE(X->dtype == HalideIR::type_of() || - X->dtype == HalideIR::type_of()); - - int tile_row = (sizeof(int32_t) * num_bits) / X->dtype.bits(); - int tile_col = ((vector_width_ * num_bits) / X->dtype.bits()) / tile_row; - - const auto& x = actual_coord[0]; - const auto& y = actual_coord[1]; - - const int block_dimy = tile_row; - const int block_dimx = tile_col; - - const auto& y0 = y % block_dimy; - const auto& y1 = (y / block_dimy) % block_dimx; - const auto& y2 = y / block_dimy / block_dimx; - - const auto& x0 = x % block_dimx; - const auto& x1 = x / block_dimx; - - return tvm::Array{ - y0 + y2 * block_dimx * block_dimy + x0 * block_dimy, - y1 + x1 * block_dimx}; - }; -} - -tvm::Array WeightLayoutTiling2D::ToActualShape(const tvm::Tensor& X) const { - ORT_ENFORCE(X->dtype == HalideIR::type_of() || - X->dtype == HalideIR::type_of()); - - auto pad_row = tvm::make_const(tvm::Int(32), (vector_width_ * num_bits) / X->dtype.bits()); - auto pad_col = tvm::make_const(tvm::Int(32), vector_width_ / sizeof(int32_t)); - - auto new_shape0 = ((X->shape[1] + pad_col - 1) / pad_col) * pad_col; - auto new_shape1 = ((X->shape[0] + pad_row - 1) / pad_row) * pad_row; - - tvm::Array - new_shape = { - new_shape0, - new_shape1}; - return new_shape; -} - -std::vector WeightLayoutTiling2D::ToActualShape(const Tensor* X) const { - ORT_ENFORCE(X != nullptr); - ORT_ENFORCE(X->Shape().GetDims().size() == 2); - - int pad_row = vector_width_ / X->DataType()->Size(); - int pad_col = vector_width_ / sizeof(int32_t); - - auto old_shape = X->Shape().GetDims(); - auto new_shape0 = (old_shape[1] + pad_col - 1) / pad_col * pad_col; - auto new_shape1 = ((old_shape[0] + pad_row - 1) / pad_row) * pad_row; - - std::vector new_shape = { - new_shape0, - new_shape1}; - - return new_shape; -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.h b/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.h deleted file mode 100644 index 64334a069f94f..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/tiling_2d.h +++ /dev/null @@ -1,43 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -#include "core/codegen/passes/weight_layout/weight_layout.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -/* - * \class! WeightLayoutTiling2D - * \breif! Transform 2D weight to 4D by tiling both dimension, - * this layout is used for tensorization. - * [M, N] => [M/Tx, N/Ty, Tx, Ty] - */ - -class WeightLayoutTiling2D : public WeightLayout { - public: - static const std::string GetKey(ONNX_NAMESPACE::TensorProto_DataType proto_type, - int vector_width); - - public: - WeightLayoutTiling2D(ONNX_NAMESPACE::TensorProto_DataType proto_type, - int vector_width); - - ~WeightLayoutTiling2D() = default; - - CoordTransFunc ToNominal(const tvm::Tensor& X) const override; - CoordTransFunc ToActual(const tvm::Tensor& X) const override; - tvm::Array ToActualShape(const tvm::Tensor& X) const override; - std::vector ToActualShape(const Tensor* X) const override; - - private: - int vector_width_; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(WeightLayoutTiling2D); -}; - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.cc b/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.cc deleted file mode 100644 index ea8597f7dd89d..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.cc +++ /dev/null @@ -1,64 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/weight_layout/transpose_2d.h" - -#include "core/codegen/passes/utils/codegen_context.h" - -namespace onnxruntime { -namespace tvm_codegen { - -constexpr auto local_layout_name = "transpose_2d"; - -const std::string WeightLayoutTranspose2D::GetKey( - ONNX_NAMESPACE::TensorProto_DataType proto_type) { - return WeightLayout::GetKey(local_layout_name, proto_type, 2, 0.0f); -} - -WeightLayoutTranspose2D::WeightLayoutTranspose2D( - ONNX_NAMESPACE::TensorProto_DataType proto_type) - : WeightLayout(local_layout_name, proto_type, 2, 0.0f) {} - -CoordTransFunc WeightLayoutTranspose2D::ToActual(const tvm::Tensor& /*X*/) const { - return [&](const tvm::Array& nominal_coord) { - ORT_ENFORCE(nominal_coord.size() == 2); - const auto& y = nominal_coord[0]; - const auto& x = nominal_coord[1]; - return tvm::Array{ - x, - y}; - }; -} - -CoordTransFunc WeightLayoutTranspose2D::ToNominal(const tvm::Tensor& /*X*/) const { - return [&](const tvm::Array& actual_coord) { - ORT_ENFORCE(actual_coord.size() == 2); - const auto& y = actual_coord[0]; - const auto& x = actual_coord[1]; - return tvm::Array{ - x, - y}; - }; -} - -tvm::Array WeightLayoutTranspose2D::ToActualShape(const tvm::Tensor& X) const { - tvm::Array new_shape = { - X->shape[1], - X->shape[0]}; - return new_shape; -} - -std::vector WeightLayoutTranspose2D::ToActualShape(const Tensor* X) const { - ORT_ENFORCE(X != nullptr); - ORT_ENFORCE(X->Shape().GetDims().size() == 2); - auto old_shape = X->Shape().GetDims(); - - std::vector new_shape = { - old_shape[1], - old_shape[0]}; - - return new_shape; -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.h b/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.h deleted file mode 100644 index 65babaaec8dac..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/transpose_2d.h +++ /dev/null @@ -1,33 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -#include "core/codegen/passes/weight_layout/weight_layout.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// WeightLayoutTranspose2D for transposing a 2D weight -// [W, H] => [H, W] -class WeightLayoutTranspose2D : public WeightLayout { - public: - static const std::string GetKey(ONNX_NAMESPACE::TensorProto_DataType proto_type); - - public: - WeightLayoutTranspose2D(ONNX_NAMESPACE::TensorProto_DataType proto_type); - - ~WeightLayoutTranspose2D() = default; - - CoordTransFunc ToNominal(const tvm::Tensor& X) const override; - CoordTransFunc ToActual(const tvm::Tensor& X) const override; - tvm::Array ToActualShape(const tvm::Tensor& X) const override; - std::vector ToActualShape(const Tensor* X) const override; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(WeightLayoutTranspose2D); -}; - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.cc b/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.cc deleted file mode 100644 index b1ddb791a3b3d..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.cc +++ /dev/null @@ -1,77 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/weight_layout/vertical_stripes_2d.h" - -#include "core/codegen/passes/utils/codegen_context.h" - -namespace onnxruntime { -namespace tvm_codegen { - -constexpr auto local_name_prefix = "vertical_stripe_2d_"; - -const std::string WeightLayoutVerticalStripe2D::GetKey( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int stripe_width) { - return WeightLayout::GetKey( - local_name_prefix + std::to_string(stripe_width), - proto_type, 2, 0.0f); -} - -WeightLayoutVerticalStripe2D::WeightLayoutVerticalStripe2D( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int stripe_width) - : WeightLayout( - local_name_prefix + std::to_string(stripe_width), - proto_type, 2, 0.0f), - stripe_width_(stripe_width) { -} - -CoordTransFunc WeightLayoutVerticalStripe2D::ToActual(const tvm::Tensor& /*X*/) const { - return [&](const tvm::Array& nominal_coord) { - ORT_ENFORCE(nominal_coord.size() == 2); - const auto& y = nominal_coord[0]; - const auto& x = nominal_coord[1]; - return tvm::Array{ - x / stripe_width_, - y, - x % stripe_width_}; - }; -} - -CoordTransFunc WeightLayoutVerticalStripe2D::ToNominal(const tvm::Tensor& /*X*/) const { - return [&](const tvm::Array& actual_coord) { - ORT_ENFORCE(actual_coord.size() == 3); - const auto& z = actual_coord[0]; - const auto& y = actual_coord[1]; - const auto& x = actual_coord[2]; - return tvm::Array{ - y, - x + stripe_width_ * z}; - }; -} - -tvm::Array WeightLayoutVerticalStripe2D::ToActualShape(const tvm::Tensor& X) const { - tvm::Array new_shape = { - (X->shape[1] + stripe_width_ - 1) / stripe_width_, - X->shape[0], - stripe_width_}; - return new_shape; -} - -std::vector WeightLayoutVerticalStripe2D::ToActualShape(const Tensor* X) const { - ORT_ENFORCE(X != nullptr); - auto old_shape = X->Shape().GetDims(); - - ORT_ENFORCE(old_shape.size() == 2); - - std::vector new_shape = { - (old_shape[1] + stripe_width_ - 1) / stripe_width_, - old_shape[0], - stripe_width_}; - - return new_shape; -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.h b/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.h deleted file mode 100644 index b9b65025dc014..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/vertical_stripes_2d.h +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/common.h" -#include "core/codegen/passes/weight_layout/weight_layout.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -// WeightLayoutVerticalStripe2D for making a 2D weight to 3D, by tiling the lowest (verteical) dimension -// [W, H] => [H/stripe, W, stripe] -class WeightLayoutVerticalStripe2D : public WeightLayout { - public: - static const std::string GetKey( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int stripe_width); - - public: - WeightLayoutVerticalStripe2D( - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int stripe_width); - - ~WeightLayoutVerticalStripe2D() = default; - - virtual CoordTransFunc ToNominal(const tvm::Tensor& X) const override; - virtual CoordTransFunc ToActual(const tvm::Tensor& X) const override; - tvm::Array ToActualShape(const tvm::Tensor& X) const override; - std::vector ToActualShape(const Tensor* X) const override; - - private: - int stripe_width_; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(WeightLayoutVerticalStripe2D); -}; - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/weight_layout.cc b/onnxruntime/core/codegen/passes/weight_layout/weight_layout.cc deleted file mode 100644 index ab3e647fd284a..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/weight_layout.cc +++ /dev/null @@ -1,91 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/codegen/passes/weight_layout/weight_layout.h" - -#include "core/codegen/common/common.h" -#include "core/codegen/common/utils.h" -#include "core/codegen/mti/mti_tvm_utils.h" -#include "core/codegen/passes/utils/ort_tvm_utils.h" - -namespace onnxruntime { -namespace tvm_codegen { - -static tvm::Tensor CreateTVMPlaceholder( - const std::string& name, - HalideIR::Type type, - int dim) { - tvm::Array shape; - if (dim > 0) { - for (int i = 0; i < dim; ++i) { - shape.push_back(tvm::Var(name + "_v" + std::to_string(i))); - } - } else { - shape.push_back(1); - } - return tvm::placeholder(shape, type, name + "_placeholder"); -} - -const std::string WeightLayout::GetKey( - const std::string& name, - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int input_dim, - float pad_zero) { - std::ostringstream key; - key << name << "_type_" << static_cast(proto_type); - key << "_dim_" << input_dim; - key << "_pad_zero_" << pad_zero; - return NormalizeCppName(key.str()); -} - -WeightLayout::WeightLayout( - const std::string& name, - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int input_dim, - float pad_zero) - : name_(GetKey(name, proto_type, input_dim, pad_zero)), - proto_type_(proto_type), - input_dim_(input_dim), - pad_zero_(pad_zero) {} - -const std::string& WeightLayout::Name() const { - return name_; -} - -void WeightLayout::CreateLayoutMarshallingTVMOp(tvm::Array& inputs, - tvm::Array& outputs) const { - HalideIR::Type halide_type = ToTvmType(proto_type_); - - tvm::Tensor placeholder = CreateTVMPlaceholder(name_, halide_type, input_dim_); - inputs.push_back(placeholder); - - tvm::Array new_shape = ToActualShape(placeholder); - CoordTransFunc new_coord_to_old_coord_func = ToNominal(placeholder); - tvm::Expr pad_zero_expr = tvm::make_const(halide_type, pad_zero_); - - tvm::Tensor output = tvm::compute( - new_shape, - [&](const tvm::Array& output_coord) { - tvm::Array output_coord1; - for (const auto& coord : output_coord) - output_coord1.push_back(coord); - auto input_coord = new_coord_to_old_coord_func(output_coord1); - ORT_ENFORCE(input_coord.size() == placeholder->shape.size()); - - if (input_coord.size() > 0) { - auto in_range = (input_coord[0] >= 0) && (input_coord[0] < placeholder->shape[0]); - for (size_t dim = 1; dim < input_coord.size(); ++dim) - in_range = in_range && (input_coord[dim] >= 0) && (input_coord[dim] < placeholder->shape[dim]); - - return tvm::if_then_else(in_range, placeholder(input_coord), pad_zero_expr); - } else { - // scalar - return placeholder(input_coord); - } - }); - - outputs.push_back(output); -} - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/codegen/passes/weight_layout/weight_layout.h b/onnxruntime/core/codegen/passes/weight_layout/weight_layout.h deleted file mode 100644 index 1b45a38e7e24e..0000000000000 --- a/onnxruntime/core/codegen/passes/weight_layout/weight_layout.h +++ /dev/null @@ -1,68 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once -#include "core/codegen/common/common.h" -#include "core/codegen/common/registry.h" -#include "core/common/common.h" -#include "core/framework/tensor.h" -#include - -namespace onnxruntime { -namespace tvm_codegen { - -using CoordTransFunc = std::function(const tvm::Array&)>; - -// WeightLayout is data layout transformer for weight/initializer -class WeightLayout { - public: - // Static function to return unique string as a key - static const std::string GetKey( - const std::string& name, - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int input_dim, - float pad_zero); - - public: - WeightLayout( - const std::string& name, - ONNX_NAMESPACE::TensorProto_DataType proto_type, - int input_dim, - float pad_zero); - - virtual ~WeightLayout() = default; - - // Return a CoordTransFunc from actual (transformed) coordinate to normial (original) coordinate - virtual CoordTransFunc ToNominal(const tvm::Tensor& X) const = 0; - - // Return a CoordTransFunc from normial (original) coordinate to actual (transformed) coordinate - virtual CoordTransFunc ToActual(const tvm::Tensor& X) const = 0; - - // Return actual (transformed) shape in tvm::Array (tvm_codegen) - virtual tvm::Array ToActualShape(const tvm::Tensor& X) const = 0; - - // Return actual (transformed) shape in vector (ort) - virtual std::vector ToActualShape(const Tensor* X) const = 0; - - // Create Layout Marshalling op in outputs - void CreateLayoutMarshallingTVMOp(tvm::Array& inputs, - tvm::Array& outputs) const; - - // Layout name - const std::string& Name() const; - - protected: - std::string name_; - ONNX_NAMESPACE::TensorProto_DataType proto_type_; - int input_dim_; - float pad_zero_; - - private: - ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(WeightLayout); -}; - -// Weight Layout Registry is a registry holds all WeightLayout -using WeightLayoutRegistry = codegen::RegistryBase; - -} // namespace tvm_codegen -} // namespace onnxruntime diff --git a/onnxruntime/core/framework/utils.cc b/onnxruntime/core/framework/utils.cc index 9eed0249711f9..ff664c2c76703 100644 --- a/onnxruntime/core/framework/utils.cc +++ b/onnxruntime/core/framework/utils.cc @@ -57,7 +57,6 @@ void DestroyStrings(void* p_data, int64_t elements) { bool ProviderIsCpuBased(const std::string& provider_type) { return provider_type == onnxruntime::kCpuExecutionProvider || provider_type == onnxruntime::kDnnlExecutionProvider || - provider_type == onnxruntime::kTvmExecutionProvider || provider_type == onnxruntime::kVitisAIExecutionProvider || provider_type == onnxruntime::kOpenVINOExecutionProvider || provider_type == onnxruntime::kNnapiExecutionProvider || diff --git a/onnxruntime/core/platform/windows/stacktrace.cc b/onnxruntime/core/platform/windows/stacktrace.cc index 3401507ae911f..cc23d70c0f11f 100644 --- a/onnxruntime/core/platform/windows/stacktrace.cc +++ b/onnxruntime/core/platform/windows/stacktrace.cc @@ -30,7 +30,6 @@ class CaptureStackTrace { // Get the stack trace. Currently only enabled for a DEBUG build as we require the DbgHelp library. std::vector GetStackTrace() { #ifndef NDEBUG -// TVM need to run with shared CRT, so won't work with debug helper now #if (defined __cpp_lib_stacktrace) && !(defined _OPSCHEMA_LIB_) && !(defined _GAMING_XBOX) && !(defined ONNXRUNTIME_ENABLE_MEMLEAK_CHECK) return detail::CaptureStackTrace().Trace(); #else diff --git a/onnxruntime/core/providers/get_execution_providers.cc b/onnxruntime/core/providers/get_execution_providers.cc index d2a72c3a38b03..7d8c5525667b9 100644 --- a/onnxruntime/core/providers/get_execution_providers.cc +++ b/onnxruntime/core/providers/get_execution_providers.cc @@ -66,14 +66,6 @@ constexpr ProviderInfo kProvidersInPriorityOrder[] = true, #else false, -#endif - }, - { - kTvmExecutionProvider, -#ifdef USE_TVM - true, -#else - false, #endif }, { diff --git a/onnxruntime/core/providers/provider_factory_creators.h b/onnxruntime/core/providers/provider_factory_creators.h index 41e418d9eb97f..1c62c1a7a8d0b 100644 --- a/onnxruntime/core/providers/provider_factory_creators.h +++ b/onnxruntime/core/providers/provider_factory_creators.h @@ -78,10 +78,6 @@ #include "core/providers/tensorrt/tensorrt_provider_factory_creator.h" #endif -#if defined(USE_TVM) -#include "core/providers/tvm/tvm_provider_factory_creator.h" -#endif - #if defined(USE_VITISAI) #include "core/providers/vitisai/vitisai_provider_factory_creator.h" #endif diff --git a/onnxruntime/core/providers/tvm/custom_logging.cc b/onnxruntime/core/providers/tvm/custom_logging.cc deleted file mode 100644 index 1cabe81f8e87e..0000000000000 --- a/onnxruntime/core/providers/tvm/custom_logging.cc +++ /dev/null @@ -1,52 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. -// -// Enable custom logging - this will cause TVM to use a custom implementation -// of tvm::runtime::detail::LogMessage. We use this to change the absolute -// file path to relative file path. - -#include -#include -#include -#include -#include -#include - -// TODO(agladyshev): Make conditional choice of sep for Windows and UNIX -std::string GetFileName(const std::string& file_path, char sep = '/') { - return {std::next(file_path.begin(), file_path.find_last_of(sep) + 1), - file_path.end()}; -} - -std::string GetTimedLogMessage(const std::string& file, int lineno, const std::string& message) { - std::stringstream sstream; - std::string file_name = GetFileName(file); - std::time_t t = std::time(nullptr); - sstream << "[" -#ifdef _WIN32 -// TODO(vvchernov): use #include instead of and localtime_s() approach for WIN32 -#pragma warning(disable : 4996) // _CRT_SECURE_NO_WARNINGS -#endif - << std::put_time(std::localtime(&t), "%H:%M:%S") -#ifdef _WIN32 -#pragma warning(default : 4996) -#endif - << "][TVM] " - << file_name << ":" << lineno << ": " + message; - return sstream.str(); -} - -namespace tvm { -namespace runtime { -namespace detail { -void LogFatalImpl(const std::string& file, int lineno, const std::string& message) { - throw std::runtime_error(GetTimedLogMessage(file, lineno, message)); -} - -void LogMessageImpl(const std::string& file, int lineno, const std::string& message) { - std::cerr << GetTimedLogMessage(file, lineno, message) << std::endl; -} - -} // namespace detail -} // namespace runtime -} // namespace tvm diff --git a/onnxruntime/core/providers/tvm/hash_alg/hasher.cc b/onnxruntime/core/providers/tvm/hash_alg/hasher.cc deleted file mode 100644 index bb62b41c7aa85..0000000000000 --- a/onnxruntime/core/providers/tvm/hash_alg/hasher.cc +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/common/common.h" - -#include "hasher.h" // NOLINT(build/include_subdir) -#include "hasher_impl.h" // NOLINT(build/include_subdir) - -namespace onnxruntime { -namespace tvm { - -Hasher::Hasher(const std::string& hash_type) { - hasher_ = getHasherImpl(hash_type); -} - -std::string Hasher::hash(const char* src, size_t size) const { - return hasher_->hash(src, size); -} - -std::shared_ptr Hasher::getHasherImpl(const std::string& hash_type) { - if (hash_type == "sha256") { - return std::make_shared(); - } else { - ORT_NOT_IMPLEMENTED("Hasher was not implemented for hash type: ", hash_type); - } - return nullptr; -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/hash_alg/hasher.h b/onnxruntime/core/providers/tvm/hash_alg/hasher.h deleted file mode 100644 index 7b2f50def2e36..0000000000000 --- a/onnxruntime/core/providers/tvm/hash_alg/hasher.h +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_H_ -#define ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_H_ - -#include -#include - -namespace onnxruntime { -namespace tvm { -class HasherImpl; - -class Hasher { - public: - Hasher() = delete; - explicit Hasher(const std::string& hash_type); - virtual ~Hasher() = default; - - std::string hash(const char* src, size_t size) const; - - private: - std::shared_ptr getHasherImpl(const std::string& hash_type); - - private: - std::shared_ptr hasher_; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_H_ diff --git a/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.cc b/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.cc deleted file mode 100644 index 20aef66f3046a..0000000000000 --- a/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.cc +++ /dev/null @@ -1,39 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "hasher_impl.h" // NOLINT(build/include_subdir) - -namespace onnxruntime { -namespace tvm { - -std::string HasherSHA256Impl::hash(const char* src, size_t size) const { - return hexdigest(src, size); -} - -void HasherSHA256Impl::digest(const Ipp8u* src, int size, Ipp8u* dst) { - IppStatus status = ippStsNoErr; - const IppsHashMethod* hashMethod = ippsHashMethod_SHA256(); - status = ippsHashMessage_rmf(src, size, dst, hashMethod); - if (ippStsNoErr != status) { - ORT_THROW("Can't get SHA-256..."); - } -} - -std::string HasherSHA256Impl::digest(const char* src, size_t size) { - const int digest_size_byte = IPP_SHA256_DIGEST_BITSIZE / 8; - auto dst = std::unique_ptr(new char[digest_size_byte]); - digest(reinterpret_cast(src), static_cast(size), reinterpret_cast(dst.get())); - return std::string(dst.get(), digest_size_byte); -} - -std::string HasherSHA256Impl::hexdigest(const char* src, size_t size) { - std::string byte_digest = digest(src, size); - std::stringstream ss; - for (char c : byte_digest) { - ss << std::hex << std::setw(2) << std::setfill('0') << (0xff & c); - } - return ss.str(); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.h b/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.h deleted file mode 100644 index 6c285dd0c78f3..0000000000000 --- a/onnxruntime/core/providers/tvm/hash_alg/hasher_impl.h +++ /dev/null @@ -1,42 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_IMPL_H_ -#define ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_IMPL_H_ - -#include -#include -#include -#include -#include - -#include "core/common/common.h" - -namespace onnxruntime { -namespace tvm { - -class HasherImpl { - public: - HasherImpl() = default; - virtual ~HasherImpl() = default; - - virtual std::string hash(const char* src, size_t size) const = 0; -}; - -class HasherSHA256Impl : public HasherImpl { - public: - HasherSHA256Impl() = default; - virtual ~HasherSHA256Impl() = default; - - std::string hash(const char* src, size_t size) const final; - - private: - static void digest(const Ipp8u* src, int size, Ipp8u* dst); - static std::string digest(const char* src, size_t size); - static std::string hexdigest(const char* src, size_t size); -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // ONNXRUNTIME_CORE_PROVIDERS_TVM_HASH_ALG_HASHER_IMPL_H_ diff --git a/onnxruntime/core/providers/tvm/symbols.txt b/onnxruntime/core/providers/tvm/symbols.txt deleted file mode 100644 index 8d903acd9ea76..0000000000000 --- a/onnxruntime/core/providers/tvm/symbols.txt +++ /dev/null @@ -1 +0,0 @@ -OrtSessionOptionsAppendExecutionProvider_Tvm diff --git a/onnxruntime/core/providers/tvm/tvm_allocator.cc b/onnxruntime/core/providers/tvm/tvm_allocator.cc deleted file mode 100644 index 4b68f6432e8cc..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_allocator.cc +++ /dev/null @@ -1,29 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include - -#include "tvm_allocator.h" -#include "core/framework/session_state.h" -#include "xpu_data_transfer.h" - -namespace onnxruntime { -namespace tvm { - -void* TVMAllocator::Alloc(size_t size) { - void* p = nullptr; - if (size > 0) { - DLDataType dl_type{kDLInt, 8, 1}; - int err = TVMDeviceAllocDataSpace(ctx, size, ::tvm::runtime::kAllocAlignment, dl_type, reinterpret_cast(&p)); - CHECK_EQ(err, 0); - return p; - } - return p; -} - -void TVMAllocator::Free(void* p) { - TVMDeviceFreeDataSpace(ctx, p); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_allocator.h b/onnxruntime/core/providers/tvm/tvm_allocator.h deleted file mode 100644 index f3ba544b8ac46..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_allocator.h +++ /dev/null @@ -1,45 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_ALLOCATOR -#define TVM_ALLOCATOR - -#include "core/framework/allocator.h" -#include "tvm_common.h" - -namespace onnxruntime { -namespace tvm { - -#define TVM_ALLOC_ALIGN 128 - -class TVMAllocator : public IAllocator { - public: - TVMAllocator() : TVMAllocator(OrtMemoryInfo("TVM", - OrtAllocatorType::OrtDeviceAllocator, - OrtDevice(OrtDevice::CPU, OrtDevice::MemType::DEFAULT, 0), - 0, - OrtMemTypeDefault)) {} - explicit TVMAllocator(const OrtMemoryInfo& info) - : IAllocator(info) { - switch (info.device.Type()) { - case OrtDevice::CPU: - ctx = {kDLCPU, info.device.Id()}; - break; - case OrtDevice::GPU: - ctx = {kDLVulkan, info.device.Id()}; - break; - default: - ORT_NOT_IMPLEMENTED("Unsupported device"); - break; - } - } - - virtual void* Alloc(size_t size) override; - virtual void Free(void* p) override; - DLDevice ctx; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_ALLOCATOR diff --git a/onnxruntime/core/providers/tvm/tvm_api.cc b/onnxruntime/core/providers/tvm/tvm_api.cc deleted file mode 100644 index e9a7d002e77c8..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_api.cc +++ /dev/null @@ -1,303 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifdef _WIN32 -#include -#else -#include // glob(), globfree() -#endif -#include // memset() -#include -#include -#include - -#include -#include -#include - -#include "core/common/common.h" -#include - -#include "tvm_api.h" - -namespace onnxruntime { -namespace tvm { - -using TvmIntArray = ::tvm::Array<::tvm::Integer>; -using TvmPackedFunc = ::tvm::PackedFunc; -namespace tvm_rt = ::tvm::runtime; -namespace tvm_rt_vm = tvm_rt::vm; - -TvmModule TVMCompile(const TvmEPOptions& options, - const std::string& onnx_txt, - const std::string& model_path, - int opset, - const TVMTensorShapes& input_shapes) { - ::tvm::Array shapes; - for (size_t i = 0; i < input_shapes.size(); ++i) { - TvmIntArray shape; - for (auto& dim : input_shapes[i]) { - shape.push_back(::tvm::Integer(dim)); - } - shapes.push_back(shape); - } - - const TvmPackedFunc* compile = tvm_rt::Registry::Get("tvm_onnx_import_and_compile"); - ORT_ENFORCE(compile != nullptr, "Unable to retrieve 'tvm_onnx_import_and_compile'."); - TvmModule mod = (*compile)(TVMByteArray{onnx_txt.data(), onnx_txt.size()}, - model_path, - options.executor, - options.target, - options.target_host, - options.opt_level, - opset, - options.freeze_weights, - shapes, - options.to_nhwc, - options.tuning_file_path, - options.tuning_type); - ORT_ENFORCE(mod.get() != nullptr, "Compiled TVM Module is nullptr!"); - return mod; -} - -std::vector glob(const std::string& dir, const std::string& extension) { - std::vector filenames; -#ifdef _WIN32 - std::string pattern = dir + "/*." + extension; - WIN32_FIND_DATA fd; - HANDLE hFind = ::FindFirstFile(pattern.c_str(), &fd); - if (hFind != INVALID_HANDLE_VALUE) { - do { - if (!(fd.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY)) { - filenames.push_back( - dir + - ToUTF8String(PathString{k_preferred_path_separator}) + - fd.cFileName); - } - } while (::FindNextFile(hFind, &fd)); - ::FindClose(hFind); - } -#else - glob_t glob_result; - memset(&glob_result, 0, sizeof(glob_result)); - - std::string pattern = dir + "/*." + extension; - int return_value = glob(pattern.c_str(), GLOB_TILDE, NULL, &glob_result); - ORT_ENFORCE(return_value == 0, "No results of glob for pattern: " + pattern); - - for (size_t i = 0; i < glob_result.gl_pathc; ++i) { - filenames.push_back(std::string(glob_result.gl_pathv[i])); - } - globfree(&glob_result); -#endif - return filenames; -} - -std::string filter_lib_paths(const std::vector& lib_paths, const std::string& lib_ext) { - std::string lib_path; - size_t counter = 0; - for (const auto& path : lib_paths) { - if (path.find("libtvm_runtime." + lib_ext) != std::string::npos || - path.find("liboctomized_model." + lib_ext) != std::string::npos) { - ++counter; - } else { - lib_path = path; - } - } - ORT_ENFORCE((lib_paths.size() - counter) == 1, "It should be only one shared library for model after filtering"); - - return lib_path; -} - -static std::unordered_map str2dev_type = { - {"llvm", 1}, - {"stackvm", 1}, - {"cpu", 1}, - {"c", 1}, - {"hybrid", 1}, - {"composite", 1}, - {"cuda", 2}, - {"nvptx", 2}, - {"cl", 4}, - {"opencl", 4}, - {"sdaccel", 4}, - {"aocl", 5}, - {"aocl_sw_emu", 5}, - {"vulkan", 7}, - {"metal", 8}, - {"vpi", 9}, - {"rocm", 10}, - {"ext_dev", 12}, - {"hexagon", 14}, - {"webgpu", 15}}; - -TvmModule TVMSoCompile(const TvmEPOptions& options) { - const std::string& dir = options.so_folder; -#ifdef _WIN32 - std::string lib_ext = "dll"; -#else - std::string lib_ext = "so"; -#endif - const std::string lib_path = filter_lib_paths(glob(dir, lib_ext), lib_ext); - const std::string consts_path = dir + - ToUTF8String(PathString{k_preferred_path_separator}) + - "consts"; - const auto& ro_paths = glob(dir, "ro"); - ORT_ENFORCE(ro_paths.size() == 1, "It should be only one ro file in folder: " + dir); - const std::string vm_exec_code_path = ro_paths[0]; - - TvmModule lib = TvmModule::LoadFromFile(lib_path); - - std::ifstream code(vm_exec_code_path, std::ios::binary); - std::stringstream ss; - ss << code.rdbuf(); - - auto exec_mod = tvm_rt_vm::Executable::Load(ss.str(), lib); - const tvm_rt_vm::Executable* tmp = exec_mod.as(); - auto exec = tvm_rt::GetObjectPtr(const_cast(tmp)); - exec->LoadLateBoundConstantsFromFile(consts_path); - - auto vm = tvm_rt::make_object(); - vm->LoadExecutable(exec); - - size_t pos = options.target.find(" "); - const std::string dev_type_str = options.target.substr(0, pos); - ORT_ENFORCE(!dev_type_str.empty(), "Device was not found in target string"); - uint64_t dev_type = str2dev_type[dev_type_str]; - const uint64_t cpu_type = str2dev_type["cpu"]; - // Initialize the VM for the specified device. If the device is not a CPU, - // We'll need to add a CPU context to drive it. - int arity; - if (dev_type == cpu_type) { - arity = 3; - } else { - arity = 6; - } - uint64_t alloc_type = uint64_t(tvm_rt_vm::AllocatorType::kPooled); - // TODO(vchernov): multiple devices using and using device with specified id are not supported - // Always use the first device of the specified type. - uint64_t device_id = 0; - std::vector init_vals(arity); - std::vector codes(arity); - tvm_rt::TVMArgsSetter setter(init_vals.data(), codes.data()); - setter(0, dev_type); - setter(1, device_id); - setter(2, alloc_type); - // Also initialize a CPU device context. - if (dev_type != cpu_type) { - setter(3, cpu_type); - setter(4, device_id); - setter(5, alloc_type); - } - tvm_rt::TVMRetValue rv; - // Call the packed func with the init arguments. - vm->GetFunction("init", nullptr).CallPacked(tvm_rt::TVMArgs(init_vals.data(), codes.data(), arity), &rv); - - return TvmModule(vm); -} - -void TVMSetInputs(TvmModule& mod, - std::vector& inds, - std::vector& inputs) { - TvmPackedFunc set_input = mod.GetFunction("set_input", false); - TvmPackedFunc set_input_zero_copy = mod.GetFunction("set_input_zero_copy", false); - for (size_t i = 0; i < inds.size(); ++i) { - if (reinterpret_cast(inputs[i].data) % tvm_rt::kAllocAlignment == 0) { - set_input_zero_copy(inds[i], &inputs[i]); - } else { - set_input(inds[i], &inputs[i]); - } - } -} - -void TVM_VM_SetInputs(TvmModule& mod, - std::vector& inds, - std::vector& inputs) { - size_t num_total_args = inputs.size() + 1; - std::vector tvm_values(num_total_args); - std::vector tvm_type_codes(num_total_args); - ::tvm::runtime::TVMArgsSetter setter(tvm_values.data(), tvm_type_codes.data()); - const std::string func_name = "main"; - setter(0, func_name.c_str()); - for (size_t k = 0; k < num_total_args - 1; ++k) { - setter(inds[k] + 1, &inputs[k]); - } - - TvmPackedFunc set_input = mod.GetFunction("set_input", false); - ::tvm::runtime::TVMRetValue rv; - set_input.CallPacked(::tvm::runtime::TVMArgs(tvm_values.data(), tvm_type_codes.data(), gsl::narrow_cast(num_total_args)), &rv); -} - -void TVMSetOutputsZeroCopy(TvmModule& mod, - std::vector& outputs) { - TvmPackedFunc set_output = mod.GetFunction("set_output_zero_copy", false); - for (size_t i = 0; i < outputs.size(); ++i) { - set_output(i, &outputs[i]); - } -} - -void TVM_VM_SetOutputsZeroCopy(TvmModule& mod, - std::vector& outputs) { - size_t num_total_args = outputs.size() + 1; - std::vector tvm_values(num_total_args); - std::vector tvm_type_codes(num_total_args); - tvm_rt::TVMArgsSetter setter(tvm_values.data(), tvm_type_codes.data()); - const std::string func_name = "main"; - setter(0, func_name.c_str()); - for (size_t k = 0; k < num_total_args - 1; ++k) { - setter(k + 1, &outputs[k]); - } - - TvmPackedFunc set_output = mod.GetFunction("set_outputs", false); - tvm_rt::TVMRetValue rv; - set_output.CallPacked(tvm_rt::TVMArgs(tvm_values.data(), tvm_type_codes.data(), gsl::narrow_cast(num_total_args)), &rv); -} - -void TVMGetOutputs(TvmModule& mod, - std::vector& outputs) { - TvmPackedFunc get_output = mod.GetFunction("get_output", false); - for (size_t i = 0; i < outputs.size(); ++i) { - get_output(i, &outputs[i]); - } -} - -void TVM_VM_GetOutputs(TvmModule& mod, - std::vector& outputs) { - TvmPackedFunc get_output = mod.GetFunction("get_output", false); - for (size_t i = 0; i < outputs.size(); ++i) { - // TODO(vvchernov): think about improvement of memory management - tvm_rt::NDArray output_array = get_output(i); - output_array.CopyTo(&outputs[i]); - } -} - -void TVMGetOutputShapes(TvmModule& mod, - TVMTensorShapes& output_shapes) { - size_t size = output_shapes.size(); - TvmPackedFunc get_output = mod.GetFunction("get_output", false); - for (size_t i = 0; i < size; ++i) { - tvm_rt::NDArray output_array = get_output(i); - tvm_rt::ShapeTuple shape_tuple = output_array.Shape(); - size_t dims_num = shape_tuple.size(); - TensorShapeVector dims; - for (size_t j = 0; j < dims_num; ++j) { - dims.push_back(int64_t(shape_tuple[j])); - } - output_shapes[i] = dims; - } -} - -void TVMRun(TvmModule& mod) { - TvmPackedFunc run = mod.GetFunction("run", false); - ORT_ENFORCE(run != nullptr, "Unable to retrieve graph executor run."); - run(); -} - -void TVM_VM_Run(TvmModule& mod) { - TvmPackedFunc run = mod.GetFunction("invoke", false); - ORT_ENFORCE(run != nullptr, "Unable to retrieve virtual machine invoke."); - run("main"); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_api.h b/onnxruntime/core/providers/tvm/tvm_api.h deleted file mode 100644 index bbf05f4fc06d9..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_api.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_API_H -#define TVM_API_H - -#include -#include - -#include "tvm_common.h" -#include "tvm_defaults.h" -#include "tvm_ep_options.h" - -namespace onnxruntime { -namespace tvm { - -TvmModule TVMCompile(const TvmEPOptions& options, - const std::string& onnx_txt, - const std::string& model_path, - int opset, - const TVMTensorShapes& input_shapes); -TvmModule TVMSoCompile(const TvmEPOptions& options); - -void TVMSetInputs(TvmModule& mod, std::vector& inds, std::vector& inputs); -void TVM_VM_SetInputs(TvmModule& mod, std::vector& inds, std::vector& inputs); -void TVMSetOutputsZeroCopy(TvmModule& mod, std::vector& outputs); -void TVM_VM_SetOutputsZeroCopy(TvmModule& mod, std::vector& outputs); -void TVMGetOutputs(TvmModule& mod, std::vector& outputs); -void TVM_VM_GetOutputs(TvmModule& mod, std::vector& outputs); -void TVMGetOutputShapes(TvmModule& mod, - TVMTensorShapes& output_shapes); -void TVMRun(TvmModule& mod); -void TVM_VM_Run(TvmModule& mod); - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_API_H diff --git a/onnxruntime/core/providers/tvm/tvm_common.h b/onnxruntime/core/providers/tvm/tvm_common.h deleted file mode 100644 index 68e3b6496328a..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_common.h +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_COMMON_H -#define TVM_COMMON_H - -#include -#include - -#include -#include -#include - -namespace onnxruntime { -namespace tvm { - -using TvmModule = ::tvm::runtime::Module; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_COMMON_H diff --git a/onnxruntime/core/providers/tvm/tvm_compiler.cc b/onnxruntime/core/providers/tvm/tvm_compiler.cc deleted file mode 100644 index 8f4e7e7de9a36..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_compiler.cc +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include - -#include "tvm_compiler.h" -#include "tvm_api.h" - -namespace onnxruntime { -namespace tvm { - -auto TVMCompilerBase::operator()(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes) -> ModulePtr { - if (mod_) { - return mod_; - } - - mod_ = std::make_shared(); - this->compileTVMModule(options, input_shapes); - - return mod_; -} - -TVMCompiler::TVMCompiler(std::string&& onnx_model_str, - const std::string& model_path, - int opset) : onnx_model_str_(std::move(onnx_model_str)), - model_path_(model_path), - opset_(opset) { -} - -void TVMCompiler::compileTVMModule(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes) { - *mod_ = tvm::TVMCompile(options, - onnx_model_str_, - model_path_, - opset_, - input_shapes); - - onnx_model_str_.clear(); -} - -void TVMSoCompiler::compileTVMModule(const TvmEPOptions& options, - [[maybe_unused]] const TVMTensorShapes& input_shapes) { - *mod_ = tvm::TVMSoCompile(options); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_compiler.h b/onnxruntime/core/providers/tvm/tvm_compiler.h deleted file mode 100644 index bfc73d67aa07f..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_compiler.h +++ /dev/null @@ -1,63 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_COMPILER_H -#define TVM_COMPILER_H - -#include -#include - -#include "tvm_common.h" -#include "tvm_ep_options.h" - -namespace onnxruntime { -namespace tvm { - -class TVMCompilerBase { - public: - using ModulePtr = std::shared_ptr; - - TVMCompilerBase() = default; - virtual ~TVMCompilerBase() = default; - - ModulePtr operator()(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes); - - virtual void compileTVMModule(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes) = 0; - - protected: - ModulePtr mod_; -}; - -class TVMCompiler : public TVMCompilerBase { - public: - TVMCompiler() = delete; - ~TVMCompiler() = default; - - TVMCompiler(std::string&& onnx_model_str, - const std::string& model_path, - int opset); - - void compileTVMModule(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes) final; - - private: - std::string onnx_model_str_; - std::string model_path_; - int opset_; -}; - -class TVMSoCompiler : public TVMCompilerBase { - public: - TVMSoCompiler() = default; - ~TVMSoCompiler() = default; - - void compileTVMModule(const TvmEPOptions& options, - const TVMTensorShapes& input_shapes) final; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_COMPILER_H diff --git a/onnxruntime/core/providers/tvm/tvm_defaults.h b/onnxruntime/core/providers/tvm/tvm_defaults.h deleted file mode 100644 index 197d1f363c50d..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_defaults.h +++ /dev/null @@ -1,35 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_DEFAULTS_H_ -#define ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_DEFAULTS_H_ - -#include - -namespace onnxruntime { -namespace tvm { - -namespace env_vars { -static const std::string kDumpSubgraphs = "ORT_TVM_DUMP_SUBGRAPHS"; -} // namespace env_vars - -constexpr const char* default_executor_type = "vm"; -constexpr const char* vm_executor_type = "vm"; -constexpr const char* graph_executor_type = "graph"; - -constexpr const char* default_target_str = "llvm"; -constexpr const char* llvm_target_str = "llvm"; - -constexpr const char* cpu_target_str = "cpu"; -constexpr const char* gpu_target_str = "gpu"; - -constexpr const char* default_tuning_type = "AutoTVM"; -constexpr const char* autotvm_tuning_type = "AutoTVM"; -constexpr const char* ansor_tuning_type = "Ansor"; - -constexpr const unsigned int default_opt_level = 3; - -} // namespace tvm -} // namespace onnxruntime - -#endif // ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_DEFAULTS_H_ diff --git a/onnxruntime/core/providers/tvm/tvm_ep_options.cc b/onnxruntime/core/providers/tvm/tvm_ep_options.cc deleted file mode 100644 index 70e99833cd78b..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_ep_options.cc +++ /dev/null @@ -1,273 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include -#include - -#include "core/common/common.h" -#include "core/common/cpuid_info.h" -#include "core/framework/provider_options_utils.h" - -#include "tvm_ep_options.h" - -namespace onnxruntime { -namespace tvm { - -namespace provider_option_names { -constexpr const char* kExecutor = "executor"; -constexpr const char* kSoFolder = "so_folder"; -constexpr const char* kCheckHash = "check_hash"; -constexpr const char* kHashFilePath = "hash_file_path"; -constexpr const char* kTarget = "target"; -constexpr const char* kTargetHost = "target_host"; -constexpr const char* kOptLevel = "opt_level"; -constexpr const char* kFreezeWeights = "freeze_weights"; -constexpr const char* kSetOutputZeroCopy = "set_output_zero_copy"; -constexpr const char* kToNHWC = "to_nhwc"; -constexpr const char* kTuningFilePath = "tuning_file_path"; -constexpr const char* kTuningType = "tuning_type"; -constexpr const char* kInputNames = "input_names"; -constexpr const char* kInputShapes = "input_shapes"; - -static const std::unordered_set valid_keys{ - std::string{kExecutor}, - std::string{kSoFolder}, - std::string{kCheckHash}, - std::string{kHashFilePath}, - std::string{kTarget}, - std::string{kTargetHost}, - std::string{kOptLevel}, - std::string{kFreezeWeights}, - std::string{kSetOutputZeroCopy}, - std::string{kToNHWC}, - std::string{kTuningFilePath}, - std::string{kTuningType}, - std::string{kInputNames}, - std::string{kInputShapes}}; - -} // namespace provider_option_names - -size_t split(const std::string& src, std::vector& dst, char ch) { - dst.clear(); - - size_t pos = src.find(ch); - size_t initialPos = 0; - while (pos != std::string::npos) { - dst.push_back(src.substr(initialPos, pos - initialPos)); - initialPos = pos + 1; - - pos = src.find(ch, initialPos); - } - dst.push_back(src.substr(initialPos, std::min(pos, src.size()) - initialPos + 1)); - - return dst.size(); -} - -TvmEPOptions TvmEPOptionsHelper::FromOptionsString(const char* opt_str) { - std::string settings{opt_str}; - ProviderOptions options; - if (!settings.empty()) { - const std::string& str = settings; - - // tokenize settings - std::regex reg("\\s*,\\s*"); - std::sregex_token_iterator iter(str.begin(), str.end(), reg, -1); - std::sregex_token_iterator iter_end; - std::vector pairs(iter, iter_end); - - ORT_ENFORCE(pairs.size() > 0); - - for (const auto& pair : pairs) { - auto pos_colon = pair.find(':'); - ORT_ENFORCE(pos_colon != std::string::npos, "Invalid key value pair."); - std::string key = pair.substr(0, pos_colon); - std::string value = pair.substr(pos_colon + 1); - - // trim leading and trailing spaces from key/value - key = whitespace_trimming(key); - value = whitespace_trimming(value); - - // Check keys of obtained options - if (tvm::provider_option_names::valid_keys.count(key) == 0) { - ORT_NOT_IMPLEMENTED("TvmOptions: unknown option (", key, ")"); - } - - options[key] = value; - } - } - - return TvmEPOptionsHelper::FromProviderOptions(options); -} - -std::string TvmEPOptionsHelper::whitespace_trimming(const std::string& str) { - const std::string WHITESPACE = " \n\r\t\f\v"; - size_t start = str.find_first_not_of(WHITESPACE); - if (start == std::string::npos) { - return ""; - } else { - size_t end = str.find_last_not_of(WHITESPACE); - ORT_ENFORCE(end != std::string::npos); - return str.substr(start, end + 1); - } -} - -TvmEPOptions TvmEPOptionsHelper::FromProviderOptions(const ProviderOptions& pr_options) { - TvmEPOptions options{}; - - ORT_THROW_IF_ERROR( - ProviderOptionsParser{} - .AddAssignmentToReference(tvm::provider_option_names::kExecutor, options.executor) - .AddAssignmentToReference(tvm::provider_option_names::kSoFolder, options.so_folder) - .AddAssignmentToReference(tvm::provider_option_names::kCheckHash, options.check_hash) - .AddAssignmentToReference(tvm::provider_option_names::kHashFilePath, options.hash_file_path) - .AddAssignmentToReference(tvm::provider_option_names::kTarget, options.target) - .AddAssignmentToReference(tvm::provider_option_names::kTargetHost, options.target_host) - .AddAssignmentToReference(tvm::provider_option_names::kOptLevel, options.opt_level) - .AddAssignmentToReference(tvm::provider_option_names::kFreezeWeights, options.freeze_weights) - .AddAssignmentToReference(tvm::provider_option_names::kSetOutputZeroCopy, options.set_output_zero_copy) - .AddAssignmentToReference(tvm::provider_option_names::kToNHWC, options.to_nhwc) - .AddAssignmentToReference(tvm::provider_option_names::kTuningFilePath, options.tuning_file_path) - .AddAssignmentToReference(tvm::provider_option_names::kTuningType, options.tuning_type) - .AddAssignmentToReference(tvm::provider_option_names::kInputNames, options.input_names_str) - .AddAssignmentToReference(tvm::provider_option_names::kInputShapes, options.input_shapes_str) - .Parse(pr_options)); - - optionsPostprocess(options); - - return options; -} - -void TvmEPOptionsHelper::optionsPostprocess(TvmEPOptions& options) { - setInputShapes(options); - targetPostprocess(options.target); - targetHostPostprocess(options.target, options.target_host); - optLevelPostprocess(options.opt_level); -} - -bool TvmEPOptionsHelper::checkCPUTarget(const std::string& target) { - bool check = target.find("llvm") != std::string::npos; - return check; -} - -bool TvmEPOptionsHelper::checkGPUTarget(const std::string& target) { - bool check = (target.find("cuda") != std::string::npos || - target.find("opencl") != std::string::npos || - target.find("metal") != std::string::npos || - target.find("vulkan") != std::string::npos); - return check; -} - -void TvmEPOptionsHelper::setInputShapes(TvmEPOptions& options) { - if (options.input_names_str.empty() && options.input_shapes_str.empty()) - return; - ORT_ENFORCE(!options.input_names_str.empty() && !options.input_shapes_str.empty(), - "Both provider options \"input_names\" and \"input_shapes\" should be empty or full"); - - std::vector name_set; - std::string trimmed_names = whitespace_trimming(options.input_names_str); - size_t inp_tensors_num = split(trimmed_names, name_set, ' '); - ORT_ENFORCE(inp_tensors_num, "There is no any input tensor names!"); - - std::string trimmed_shapes = whitespace_trimming(options.input_shapes_str); - size_t end_pos = trimmed_shapes.find_last_of(']'); - ORT_ENFORCE(end_pos != std::string::npos, "Invalid string for input shapes. Symbol ] is not found"); - ORT_ENFORCE(end_pos == (trimmed_shapes.size() - 1), - "Invalid string for input shapes. Symbol ] should be last after whitespace trimming"); - - std::vector shape_set; - split(trimmed_shapes, shape_set, ']'); - shape_set.pop_back(); - ORT_ENFORCE(shape_set.size() == inp_tensors_num, - "Number of shapes is not the same as number of input tensor names"); - - for (size_t i = 0; i < inp_tensors_num; ++i) { - size_t pos = shape_set[i].find('['); - ORT_ENFORCE(pos != std::string::npos, "There is no symbol [ as pair for ]"); - std::string numbers = shape_set[i].substr(pos + 1); - std::vector number_set; - ORT_ENFORCE(split(numbers, number_set, ' '), "There is no any number between [ and ] symbols"); - - TensorShapeVector dims; - for (const auto& number : number_set) { - dims.push_back(std::stoi(number)); - } - - options.input_shapes[name_set[i]] = dims; - } -} - -void TvmEPOptionsHelper::targetPostprocess(std::string& target) { - if (target == tvm::cpu_target_str || - target == tvm::llvm_target_str) { - ProcessCPUTarget(target); - } else if (target == tvm::gpu_target_str) { - ProcessGPUTarget(); - } else if (target.empty()) { - ORT_NOT_IMPLEMENTED("target option is empty!"); - } else { - // TODO(vvchernov): extend mechanism of auto-definition of target - // target is gotten from option set up by client - } -} - -void TvmEPOptionsHelper::ProcessCPUTarget(std::string& target) { - const auto& cpu_id_info = CPUIDInfo::GetCPUIDInfo(); - // auto detect from CPU ID - if (cpu_id_info.HasAVX512Skylake()) { - target = tvm::cpu_targets::LLVM_TARGET_SKYLAKE_AVX512; - } else if (cpu_id_info.HasAVX512f()) { - target = tvm::cpu_targets::LLVM_TARGET_AVX512; - } else if (cpu_id_info.HasAVX2()) { - target = tvm::cpu_targets::LLVM_TARGET_AVX2; - } else if (cpu_id_info.HasAVX()) { - target = tvm::cpu_targets::LLVM_TARGET_AVX; - } else { - // TODO(vvchernov): extend mechanism of auto-definition of cpu target - target = tvm::llvm_target_str; - } -} - -void TvmEPOptionsHelper::ProcessGPUTarget() { - ORT_NOT_IMPLEMENTED("GPU target auto-defenition is not implemented now!"); -} - -void TvmEPOptionsHelper::targetHostPostprocess(const std::string& target, std::string& target_host) { - if ((target_host == tvm::cpu_target_str || - target_host == tvm::llvm_target_str) && - target_host != target) { - target_host = target; - } else if (target_host.empty()) { - target_host = target; - } else { - // TODO(vvchernov): extend mechanism of auto-definition of target host - // target host is gotten from option set up by client - } -} - -void TvmEPOptionsHelper::optLevelPostprocess(unsigned int& opt_level) { - if (opt_level < 1) { - opt_level = tvm::default_opt_level; - } -} - -std::ostream& operator<<(std::ostream& out, const TvmEPOptions& options) { - out << "TVM EP options:\n" - << "executor type: " << options.executor << "\n" - << "so_folder: " << options.so_folder << "\n" - << "check_hash: " << options.check_hash << "\n" - << "hash_file_path: " << options.hash_file_path << "\n" - << "target: " << options.target << "\n" - << "target_host: " << options.target_host << "\n" - << "opt level: " << options.opt_level << "\n" - << "freeze weights: " << options.freeze_weights << "\n" - << "set_output_zero_copy: " << options.set_output_zero_copy << "\n" - << "tuning file path: " << options.tuning_file_path << "\n" - << "tuning type: " << options.tuning_type << "\n" - << "convert layout to NHWC: " << options.to_nhwc << "\n" - << "input tensor names: " << options.input_names_str << "\n" - << "input tensor shapes: " << options.input_shapes_str; - return out; -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_ep_options.h b/onnxruntime/core/providers/tvm/tvm_ep_options.h deleted file mode 100644 index 0f2db30a3b304..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_ep_options.h +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_EXECUTION_PROVIDER_OPTIONS_H -#define TVM_EXECUTION_PROVIDER_OPTIONS_H - -#include -#include -#include -#include - -#include "core/framework/provider_options.h" -#include "core/framework/tensor_shape.h" - -#include "tvm_defaults.h" - -namespace onnxruntime { - -namespace tvm { -namespace cpu_targets { -// TODO(vvchernov): avx and avx512 need more careful differentiation for target -const std::string LLVM_TARGET_AVX = "llvm -mcpu=corei7-avx"; -const std::string LLVM_TARGET_AVX2 = "llvm -mcpu=core-avx2"; -const std::string LLVM_TARGET_SKYLAKE_AVX512 = "llvm -mcpu=skylake-avx512"; -const std::string LLVM_TARGET_AVX512 = "llvm -mcpu=skylake-avx512"; -} // namespace cpu_targets - -using TVMTensorShapes = std::vector; -using TVMInputShapes = std::unordered_map; -using InputsInfoMap = std::unordered_map; - -// Information needed to construct an TVM execution provider. -struct TvmEPOptions { - std::string executor{tvm::default_executor_type}; - std::string so_folder{""}; - bool check_hash = false; - std::string hash_file_path{""}; - std::string target{tvm::default_target_str}; - std::string target_host{tvm::default_target_str}; - unsigned int opt_level{tvm::default_opt_level}; - bool freeze_weights = true; - bool to_nhwc = false; - bool set_output_zero_copy = true; - std::string tuning_file_path{""}; - std::string tuning_type{tvm::default_tuning_type}; - std::string input_names_str{""}; - std::string input_shapes_str{""}; - TVMInputShapes input_shapes{}; - TVMTensorShapes output_shapes{}; -}; - -std::ostream& operator<<(std::ostream& out, const TvmEPOptions& options); - -class TvmEPOptionsHelper { - public: - static TvmEPOptions FromOptionsString(const char* options); - static TvmEPOptions FromProviderOptions(const ProviderOptions& options); - static std::string whitespace_trimming(const std::string& str); - - static bool checkCPUTarget(const std::string& target); - static bool checkGPUTarget(const std::string& target); - - private: - static void optionsPostprocess(TvmEPOptions& options); - static void setInputShapes(TvmEPOptions& options); - static void targetPostprocess(std::string& target); - static void ProcessCPUTarget(std::string& target); - static void ProcessGPUTarget(); - static void targetHostPostprocess(const std::string& target, std::string& target_host); - static void optLevelPostprocess(unsigned int& opt_level); -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_EXECUTION_PROVIDER_OPTIONS_H diff --git a/onnxruntime/core/providers/tvm/tvm_execution_provider.cc b/onnxruntime/core/providers/tvm/tvm_execution_provider.cc deleted file mode 100644 index 61ee8f899dbf1..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_execution_provider.cc +++ /dev/null @@ -1,304 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include -#include -#include - -#include "core/common/common.h" -#include "core/framework/execution_provider.h" -#include "core/framework/tensorprotoutils.h" -#include "core/framework/kernel_registry.h" -#include "core/framework/compute_capability.h" -#include "core/graph/graph_proto_serializer.h" -#include "core/platform/env.h" -#include "core/graph/model.h" - -#include "tvm_execution_provider.h" -#include "xpu_data_transfer.h" -#include "tvm_allocator.h" -#include "tvm_utils.h" -#include "tvm_api.h" - -using namespace ONNX_NAMESPACE; - -namespace onnxruntime { -namespace tvm { - -// Information to construct kernel function state. -struct TVMFuncState { - AllocateFunc allocate_func = nullptr; - DestroyFunc release_func = nullptr; - AllocatorHandle allocator = nullptr; - std::shared_ptr compiler = nullptr; -}; - -TvmExecutionProvider::TvmExecutionProvider(const TvmEPOptions& options) - : IExecutionProvider{kTvmExecutionProvider}, - options_{options} { - AllocatorCreationInfo default_memory_info = {[](int) { - return std::make_unique(); - }, - 0, false}; - // Get environment variables - const Env& env_instance = Env::Default(); - - const std::string dump_subgraphs_env = env_instance.GetEnvironmentVar(env_vars::kDumpSubgraphs); - if (!dump_subgraphs_env.empty()) { - dump_subgraphs_ = std::stoi(dump_subgraphs_env) != 0; - } -} - -std::vector TvmExecutionProvider::CreatePreferredAllocators() { - AllocatorCreationInfo default_memory_info = {[](int) { - return std::make_unique(); - }, - 0, false}; - return std::vector{CreateAllocator(default_memory_info)}; // TODO(leca): REVIEW: will CPU EP also use this? -} - -TvmExecutionProvider::~TvmExecutionProvider() {} - -std::vector> -TvmExecutionProvider::GetCapability(const GraphViewer& graph_viewer, - const IKernelLookup& /*kernel_lookup*/) const { - std::vector> result; - if (graph_viewer.IsSubgraph()) { - return result; - } - - const auto& init_tensors = graph_viewer.GetAllInitializedTensors(); - - std::unordered_set required_initializers; - const std::vector& sorted_nodes = graph_viewer.GetNodesInTopologicalOrder(); - std::unique_ptr sub_graph = std::make_unique(); - for (auto& node_idx : sorted_nodes) { - graph_viewer.GetNode(node_idx)->ForEachDef([&required_initializers, &init_tensors](const NodeArg& node_arg, bool is_input) { - if(is_input && init_tensors.count(node_arg.Name())) { - required_initializers.insert(node_arg.Name()); - } }, true); - } - - auto meta_def = std::make_unique<::onnxruntime::IndexedSubGraph::MetaDef>(); - meta_def->name = "TVMStandalone"; - meta_def->domain = "StandaloneTest"; - std::vector inputs; - std::vector outputs; - - for (auto& nodeArgPtr : graph_viewer.GetInputs()) { - inputs.push_back(nodeArgPtr->Name()); - } - - for (auto& name : required_initializers) { - inputs.push_back(name); - } - - for (auto& nodeArgPtr : graph_viewer.GetOutputs()) { - outputs.push_back(nodeArgPtr->Name()); - } - meta_def->inputs = inputs; - meta_def->outputs = outputs; - meta_def->since_version = 1; - meta_def->status = ONNX_NAMESPACE::EXPERIMENTAL; - sub_graph->SetMetaDef(std::move(meta_def)); - sub_graph->nodes = sorted_nodes; - result.push_back( - std::make_unique(std::move(sub_graph))); - return result; -} - -common::Status TvmExecutionProvider::Compile(const std::vector& fused_nodes_and_graphs, - std::vector& node_compute_funcs) { - printOptions(); - for (auto& fused_node_graph : fused_nodes_and_graphs) { - const GraphViewer& graph_body_viewer = fused_node_graph.filtered_graph; - const Node& fused_node = fused_node_graph.fused_node; - const std::string func_name = fused_node.Name(); - Model model(graph_body_viewer.Name(), true, ModelMetaData(), PathString(), - IOnnxRuntimeOpSchemaRegistryList(), graph_body_viewer.DomainToVersionMap(), - std::vector(), *GetLogger()); - ONNX_NAMESPACE::ModelProto model_proto = model.ToProto(); - // TVM EP is using static lib approach, so invoke serializer directly. - GraphViewerToProto(graph_body_viewer, *model_proto.mutable_graph(), true, true); - auto opset = model_proto.add_opset_import(); - opset->set_domain(kOnnxDomain); - opset->set_version(graph_body_viewer.DomainToVersionMap().at(kOnnxDomain)); - - std::string onnx_model_str; - model_proto.SerializeToString(&onnx_model_str); - compilers_[func_name] = std::make_shared(std::move(onnx_model_str), - ToUTF8String(fused_node.ModelPath().ToPathString()), - int(opset->version())); - InputsInfoMap all_input_shapes; - auto mod = compileModel(func_name, graph_body_viewer, all_input_shapes); - - std::vector output_tensors; - prepareOutputTensors(mod, output_tensors, graph_body_viewer.GetOutputs().size()); - - runners_[func_name] = std::make_shared(options_, mod, all_input_shapes, output_tensors); - - if (dump_subgraphs_) { - std::fstream dump("/tmp/" + func_name + ".onnx", - std::ios::out | std::ios::trunc | std::ios::binary); - model_proto.SerializeToOstream(&dump); - } - - // TODO(vvchernov): implement ops checking and mechanism of gracefully passing the responsibility to other EPs - // if the checking fails due to unsupported op(s) - NodeComputeInfo compute_info = prepareComputeInfo(func_name); - - node_compute_funcs.push_back(compute_info); - } - return Status::OK(); -} - -std::unique_ptr TvmExecutionProvider::GetDataTransfer() const { - // TODO(vvchernov): target or target host? - if (TvmEPOptionsHelper::checkGPUTarget(options_.target)) { - return std::make_unique(); - } else if (TvmEPOptionsHelper::checkCPUTarget(options_.target)) { - return std::make_unique(); - } else { - ORT_NOT_IMPLEMENTED("TVM GetDataTransfer is not implemented for target ", options_.target); - } -} - -void TvmExecutionProvider::printOptions() { - LOGS(*GetLogger(), INFO) << options_; -} - -std::shared_ptr TvmExecutionProvider::compileModel(const std::string& func_name, - const GraphViewer& graph_viewer, - InputsInfoMap& all_input_shapes) { - all_input_shapes.clear(); - - TVMTensorShapes input_shapes; - if (options_.freeze_weights) { - setInputShapesForFreezedNN(graph_viewer, input_shapes, all_input_shapes); - } else { - setInputShapesForUnfreezedNN(graph_viewer, input_shapes, all_input_shapes); - } - - std::shared_ptr mod = compilers_[func_name]->operator()(options_, input_shapes); - - return mod; -} - -void TvmExecutionProvider::setInputShapesForFreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, - InputsInfoMap& all_input_shapes) { - const std::vector& all_nodes = graph_viewer.GetInputsIncludingInitializers(); - - size_t indx = 0; - for (const auto* node : all_nodes) { - if (!graph_viewer.IsInitializedTensor(node->Name())) { - TensorShapeVector shape = getInputShape(node); - all_input_shapes[indx++] = shape; - input_shapes.emplace_back(shape); - } - } -} - -void TvmExecutionProvider::setInputShapesForUnfreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, - InputsInfoMap& all_input_shapes) { - const std::vector& all_nodes = graph_viewer.GetInputsIncludingInitializers(); - - size_t indx = 0; - for (const auto* node : all_nodes) { - TensorShapeVector shape = getInputShape(node); - all_input_shapes[indx++] = shape; - if (!graph_viewer.IsInitializedTensor(node->Name())) { - input_shapes.emplace_back(shape); - } - } -} - -TensorShapeVector TvmExecutionProvider::getInputShape(const NodeArg* node) { - TensorShapeVector shape; - const auto& node_name = node->Name(); - if (!options_.input_shapes.empty() && - options_.input_shapes.count(node_name)) { - shape = options_.input_shapes[node_name]; - } else { - shape = convertTensorShape(*node->Shape()); - } - - return shape; -} - -TensorShapeVector TvmExecutionProvider::convertTensorShape(const TensorShapeProto& shape_proto) { - TensorShape ort_shape = utils::GetTensorShapeFromTensorShapeProto(shape_proto); - size_t dims = ort_shape.NumDimensions(); - - TensorShapeVector shape(dims); - for (size_t j = 0; j < dims; ++j) { - int64_t dim = int64_t(ort_shape[j]); - ORT_ENFORCE(dim > 0, "Input dimension is not positive value (dim = " + std::to_string(dim) + "). " + - "Please use provider options to setup input_names and input_shapes"); - shape[j] = dim; - } - - return shape; -} - -void TvmExecutionProvider::prepareOutputTensors(const std::shared_ptr& mod, - std::vector& output_tensors, - size_t num) { - ORT_ENFORCE(mod != nullptr, "TVM module is not compiled"); - output_tensors.clear(); - options_.output_shapes.clear(); - options_.output_shapes.resize(num); - - if (options_.executor != "vm") { - TVMGetOutputShapes(*mod, options_.output_shapes); - } - - for (auto& output_shape : options_.output_shapes) { - DLTensor t; - // Draft for tensor, correct data is defined during inference - t.strides = nullptr; - t.byte_offset = 0; - t.data = nullptr; - if (options_.executor == "vm") { - t.ndim = 0; - t.shape = nullptr; - } else { - t.ndim = output_shape.size(); - t.shape = output_shape.data(); - } - - output_tensors.push_back(t); - } -} - -NodeComputeInfo TvmExecutionProvider::prepareComputeInfo(const std::string& func_name) { - NodeComputeInfo compute_info; - compute_info.create_state_func = std::bind(&TvmExecutionProvider::createStateFunc, - this, - std::placeholders::_1, - std::placeholders::_2); - - compute_info.release_state_func = [](FunctionState state) { - if (state) - delete static_cast(state); - }; - - compute_info.compute_func = *runners_[func_name].get(); - - return compute_info; -} - -int TvmExecutionProvider::createStateFunc(ComputeContext* context, FunctionState* state) { - auto* state_ptr = new TVMFuncState(); - *state_ptr = {context->allocate_func, - context->release_func, - context->allocator_handle, - compilers_[context->node_name]}; - // TODO(vvchernov): Who and when release state? - *state = state_ptr; - return 0; -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_execution_provider.h b/onnxruntime/core/providers/tvm/tvm_execution_provider.h deleted file mode 100644 index baa46c593fa07..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_execution_provider.h +++ /dev/null @@ -1,71 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_EXECUTION_PROVIDER_H -#define TVM_EXECUTION_PROVIDER_H - -#include -#include -#include -#include - -#include "core/common/logging/logging.h" -#include "core/framework/execution_provider.h" -#include - -#include "tvm_compiler.h" -#include "tvm_runner.h" - -namespace onnxruntime { -class Graph; -class NodeArg; -namespace tvm { - -class TvmExecutionProvider : public IExecutionProvider { - using Compiler = TVMCompilerBase; - using Compilers = std::unordered_map>; - using Runner = TVMRunner; - using Runners = std::unordered_map>; - - public: - explicit TvmExecutionProvider(const TvmEPOptions& options); - virtual ~TvmExecutionProvider(); - - std::vector> - GetCapability(const onnxruntime::GraphViewer& graph, - const IKernelLookup& /*kernel_lookup*/) const override; - - common::Status Compile(const std::vector& fused_nodes_and_graphs, - std::vector& node_compute_funcs) override; - std::unique_ptr GetDataTransfer() const override; - std::vector CreatePreferredAllocators() override; - - private: - void printOptions(); - std::shared_ptr compileModel(const std::string& func_name, - const GraphViewer& graph_viewer, - InputsInfoMap& inputs_info); // NOLINT - void setInputShapesForFreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, // NOLINT - InputsInfoMap& all_input_shapes); // NOLINT - void setInputShapesForUnfreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, // NOLINT - InputsInfoMap& all_input_shapes); // NOLINT - TensorShapeVector getInputShape(const NodeArg* node); - TensorShapeVector convertTensorShape(const ONNX_NAMESPACE::TensorShapeProto& shape_proto); - void prepareOutputTensors(const std::shared_ptr& mod, - std::vector& output_tensors, size_t num); // NOLINT - NodeComputeInfo prepareComputeInfo(const std::string& func_name); - int createStateFunc(ComputeContext*, FunctionState*); - - private: - TvmEPOptions options_; - Compilers compilers_; - Runners runners_; - bool dump_subgraphs_ = false; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_EXECUTION_PROVIDER_H diff --git a/onnxruntime/core/providers/tvm/tvm_provider_factory.cc b/onnxruntime/core/providers/tvm/tvm_provider_factory.cc deleted file mode 100644 index d83fd8ee4d1cb..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_provider_factory.cc +++ /dev/null @@ -1,53 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include -#include - -#include "core/providers/tvm/tvm_provider_factory.h" -#include "core/session/abi_session_options_impl.h" - -#include "tvm_execution_provider.h" -#include "tvm_provider_factory_creator.h" -#include "tvm_so_execution_provider.h" // NOLINT(build/include_subdir) - -namespace onnxruntime { - -struct TvmProviderFactory : IExecutionProviderFactory { - TvmProviderFactory(const tvm::TvmEPOptions& options) : options_{options} {} - ~TvmProviderFactory() = default; - - std::unique_ptr CreateProvider() override { - std::unique_ptr provider = nullptr; - if (options_.so_folder != "") { - ORT_ENFORCE(options_.executor == "vm", - "Only virtual machine module is compiled from shared lib and dependences!"); - provider = std::move(std::make_unique(options_)); - } else { - provider = std::move(std::make_unique(options_)); - } - - return provider; - } - - private: - tvm::TvmEPOptions options_; -}; - -std::shared_ptr TVMProviderFactoryCreator::Create(const char* opt_str) { - tvm::TvmEPOptions options = tvm::TvmEPOptionsHelper::FromOptionsString(opt_str); - return std::make_shared(options); -} - -std::shared_ptr TVMProviderFactoryCreator::Create(const tvm::TvmEPOptions& options) { - return std::make_shared(options); -} -} // namespace onnxruntime - -ORT_API_STATUS_IMPL(OrtSessionOptionsAppendExecutionProvider_Tvm, - _In_ OrtSessionOptions* options, - _In_ const char* opt_str) { - onnxruntime::tvm::TvmEPOptions tvm_options = onnxruntime::tvm::TvmEPOptionsHelper::FromOptionsString(opt_str); - options->provider_factories.push_back(onnxruntime::TVMProviderFactoryCreator::Create(tvm_options)); - return nullptr; -} diff --git a/onnxruntime/core/providers/tvm/tvm_provider_factory_creator.h b/onnxruntime/core/providers/tvm/tvm_provider_factory_creator.h deleted file mode 100644 index 2d7e06b5b7c59..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_provider_factory_creator.h +++ /dev/null @@ -1,19 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#pragma once - -#include - -#include "core/providers/providers.h" - -namespace onnxruntime { -namespace tvm { -struct TvmEPOptions; -} - -struct TVMProviderFactoryCreator { - static std::shared_ptr Create(const tvm::TvmEPOptions& options); - static std::shared_ptr Create(const char* params); -}; -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_runner.cc b/onnxruntime/core/providers/tvm/tvm_runner.cc deleted file mode 100644 index 5dda8f5bf9c3e..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_runner.cc +++ /dev/null @@ -1,26 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/graph/model.h" -#include "core/framework/tensorprotoutils.h" - -#include "tvm_runner.h" - -using namespace ONNX_NAMESPACE; -namespace onnxruntime { -namespace tvm { - -TVMRunner::TVMRunner(const TvmEPOptions& options, - const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const std::vector& output_tensors) { - runner_ = getTVMRunnerImpl(mod, options, inputs_info, output_tensors); -} - -common::Status TVMRunner::operator()(FunctionState state, const OrtApi* /*api*/, OrtKernelContext* context) { - Ort::KernelContext ctx(context); - return runner_->run(ctx); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_runner.h b/onnxruntime/core/providers/tvm/tvm_runner.h deleted file mode 100644 index 4b7349ee3405e..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_runner.h +++ /dev/null @@ -1,34 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_RUNNER_H -#define TVM_RUNNER_H - -#include -#include - -#include "tvm_runner_impl.h" - -namespace onnxruntime { -namespace tvm { - -class TVMRunner { - public: - TVMRunner() = delete; - virtual ~TVMRunner() = default; - - TVMRunner(const TvmEPOptions& options, - const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const std::vector& output_tensor); - - common::Status operator()(FunctionState state, const OrtApi* api, OrtKernelContext* context); - - private: - std::shared_ptr runner_; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_TVM_RUNNER_H diff --git a/onnxruntime/core/providers/tvm/tvm_runner_impl.cc b/onnxruntime/core/providers/tvm/tvm_runner_impl.cc deleted file mode 100644 index c88de2652f14b..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_runner_impl.cc +++ /dev/null @@ -1,175 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/framework/tensorprotoutils.h" - -#include "tvm_runner_impl.h" -#include "tvm_utils.h" -#include "tvm_api.h" - -namespace onnxruntime { -namespace tvm { - -/* ------------------------------------ RunnerImplFactory ----------------------------- */ - -std::shared_ptr getTVMRunnerImpl(const std::shared_ptr& mod, - const TvmEPOptions& options, - const InputsInfoMap& inputs_info, - const std::vector output_tensors) { - const std::string& name = options.executor; - if (name == "graph") { - return std::make_shared(mod, inputs_info, options.output_shapes, - output_tensors, options.set_output_zero_copy); - } else if (name == "vm") { - return std::make_shared(mod, inputs_info, options.output_shapes, - output_tensors, options.set_output_zero_copy); - } - return nullptr; -} - -/* ------------------------------------ RunnerImpl ------------------------------------ */ - -RunnerImpl::RunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector output_tensors, - bool set_output_zero_copy) : mod_(mod), - inputs_info_(inputs_info), - output_shapes_(output_shapes), - output_tensors_(output_tensors), - set_output_zero_copy_(set_output_zero_copy) { -} - -void RunnerImpl::convert_input_tensors2dl_tensors(Ort::KernelContext& context, - std::vector& dst, - std::vector& dst_inds) { - size_t num = inputs_info_.size(); - dst.reserve(num); - dst_inds.reserve(num); - for (auto& info : inputs_info_) { - // TODO(vvchernov): decomposition declaration only available with -std=c++1z or -std=gnu++1z - auto& i = info.first; - auto& shape = info.second; - - auto input_tensor = context.GetInput(i); - ORT_ENFORCE(input_tensor.IsTensor()); - - auto ort_device_type = input_tensor.GetTensorMemoryInfo().GetDeviceType(); - const auto tensor_type = input_tensor.GetTensorTypeAndShapeInfo().GetElementType(); - - DLTensor t; - t.device = GetDLDevice(ort_device_type); - t.dtype = GetDataType(tensor_type); - t.strides = nullptr; - t.byte_offset = 0; - t.data = const_cast(input_tensor.GetTensorRawData()); - t.ndim = shape.size(); - t.shape = shape.data(); - dst.emplace_back(t); - dst_inds.push_back(i); - } -} - -void RunnerImpl::add_device_type_data2output_tensors(Ort::KernelContext& context) { - size_t num_outputs = output_tensors_.size(); - for (auto i = 0u; i < num_outputs; i++) { - // setup output tensor property - auto output_tensor = context.GetOutput(i, - output_shapes_[i].data(), - output_shapes_[i].size()); - ORT_ENFORCE(output_tensor.IsTensor()); - - output_tensors_[i].device = - GetDLDevice(output_tensor.GetTensorMemoryInfo().GetDeviceType()); - output_tensors_[i].dtype = - GetDataType(output_tensor.GetTensorTypeAndShapeInfo().GetElementType()); - output_tensors_[i].data = output_tensor.GetTensorMutableRawData(); - } -} - -/* ------------------------------------ GERunnerImpl ------------------------------------ */ - -GERunnerImpl::GERunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector output_tensors, - bool set_output_zero_copy) : RunnerImpl(mod, inputs_info, output_shapes, output_tensors, set_output_zero_copy) { -} - -void GERunnerImpl::set_input(Ort::KernelContext& context) { - std::vector inds; - std::vector dl_tensors_inputs; - convert_input_tensors2dl_tensors(context, dl_tensors_inputs, inds); - - tvm::TVMSetInputs(*mod_, inds, dl_tensors_inputs); -} - -void GERunnerImpl::connect_output_tensors2ort(Ort::KernelContext& context) { - add_device_type_data2output_tensors(context); -} - -void GERunnerImpl::set_output_zero_copy() { - tvm::TVMSetOutputsZeroCopy(*mod_, output_tensors_); -} - -void GERunnerImpl::run() { - tvm::TVMRun(*mod_); -} - -void GERunnerImpl::get_outputs() { - tvm::TVMGetOutputs(*mod_, output_tensors_); -} - -/* ------------------------------------ VMRunnerImpl ------------------------------------ */ - -VMRunnerImpl::VMRunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector output_tensors, - bool set_output_zero_copy) : RunnerImpl(mod, inputs_info, output_shapes, output_tensors, set_output_zero_copy) { -} - -void VMRunnerImpl::set_input(Ort::KernelContext& context) { - std::vector inds; - std::vector dl_tensors_inputs; - convert_input_tensors2dl_tensors(context, dl_tensors_inputs, inds); - - tvm::TVM_VM_SetInputs(*mod_, inds, dl_tensors_inputs); -} - -void VMRunnerImpl::connect_output_tensors2ort(Ort::KernelContext& context) { - // TODO(vvchernov): try to find more flexible solution - if (!probe_infer_) { - infer_once_to_get_output_shapes(); - } - - add_device_type_data2output_tensors(context); -} - -void VMRunnerImpl::set_output_zero_copy() { - tvm::TVM_VM_SetOutputsZeroCopy(*mod_, output_tensors_); -} - -void VMRunnerImpl::run() { - tvm::TVM_VM_Run(*mod_); -} - -void VMRunnerImpl::get_outputs() { - tvm::TVM_VM_GetOutputs(*mod_, output_tensors_); -} - -void VMRunnerImpl::infer_once_to_get_output_shapes() { - run(); - size_t num_outputs = output_tensors_.size(); - // TODO(vvchernov): check it - output_shapes_.resize(num_outputs); - tvm::TVMGetOutputShapes(*mod_, output_shapes_); - for (size_t i = 0; i < num_outputs; ++i) { - output_tensors_[i].ndim = output_shapes_[i].size(); - output_tensors_[i].shape = output_shapes_[i].data(); - } - probe_infer_ = true; -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_runner_impl.h b/onnxruntime/core/providers/tvm/tvm_runner_impl.h deleted file mode 100644 index 8c325303673b6..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_runner_impl.h +++ /dev/null @@ -1,126 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_RUNNER_IMPL_H -#define TVM_RUNNER_IMPL_H - -#include -#include -#include - -#include "core/framework/func_api.h" -#include "core/session/onnxruntime_cxx_api.h" - -#include "tvm_common.h" -#include "tvm_ep_options.h" - -namespace onnxruntime { -namespace tvm { - -class RunnerImpl { - public: - RunnerImpl() = delete; - RunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector tensors_outputs, - bool set_output_zero_copy); - virtual ~RunnerImpl() = default; - - virtual common::Status run(Ort::KernelContext& context) { - common::Status res; - if (set_output_zero_copy_) { - res = run_without_output_copying(context); - } else { - res = run_with_output_copying(context); - } - return res; - } - - virtual common::Status run_without_output_copying(Ort::KernelContext& context) { - set_input(context); - connect_output_tensors2ort(context); - set_output_zero_copy(); - run(); - - return Status::OK(); - } - - virtual common::Status run_with_output_copying(Ort::KernelContext& context) { - set_input(context); - connect_output_tensors2ort(context); - run(); - get_outputs(); - - return Status::OK(); - } - - virtual void set_input(Ort::KernelContext& context) = 0; - virtual void connect_output_tensors2ort(Ort::KernelContext& context) = 0; - virtual void set_output_zero_copy() = 0; - virtual void run() = 0; - virtual void get_outputs() = 0; - - protected: - void convert_input_tensors2dl_tensors(Ort::KernelContext& context, - std::vector& dst, - std::vector& dst_inds); - void add_device_type_data2output_tensors(Ort::KernelContext& context); - - protected: - std::shared_ptr mod_; - InputsInfoMap inputs_info_; - TVMTensorShapes output_shapes_; - std::vector output_tensors_; - bool set_output_zero_copy_; -}; - -class GERunnerImpl : public RunnerImpl { - public: - GERunnerImpl() = delete; - GERunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector tensors_outputs, - bool set_output_zero_copy); - virtual ~GERunnerImpl() = default; - - void set_input(Ort::KernelContext& context) final; - void connect_output_tensors2ort(Ort::KernelContext& context) final; - void set_output_zero_copy() final; - void run() final; - void get_outputs() final; -}; - -class VMRunnerImpl : public RunnerImpl { - public: - VMRunnerImpl() = delete; - VMRunnerImpl(const std::shared_ptr& mod, - const InputsInfoMap& inputs_info, - const TVMTensorShapes output_shapes, - const std::vector tensors_outputs, - bool set_output_zero_copy); - virtual ~VMRunnerImpl() = default; - - void set_input(Ort::KernelContext& context) final; - void connect_output_tensors2ort(Ort::KernelContext& context) final; - void set_output_zero_copy() final; - void run() final; - void get_outputs() final; - - private: - void infer_once_to_get_output_shapes(); - - private: - bool probe_infer_ = false; -}; - -std::shared_ptr getTVMRunnerImpl(const std::shared_ptr& mod, - const TvmEPOptions& options, - const InputsInfoMap& inputs_info, - const std::vector output_tensors); - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_TVM_RUNNER_IMPL_H diff --git a/onnxruntime/core/providers/tvm/tvm_so_execution_provider.cc b/onnxruntime/core/providers/tvm/tvm_so_execution_provider.cc deleted file mode 100644 index 029f25d6f292a..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_so_execution_provider.cc +++ /dev/null @@ -1,284 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include -#include -#include - -#include "core/framework/execution_provider.h" -#include "core/framework/tensorprotoutils.h" -#include "core/framework/kernel_registry.h" -#include "core/framework/compute_capability.h" -#include "core/platform/env.h" -#include "core/graph/model.h" - -#include "tvm_so_execution_provider.h" // NOLINT(build/include_subdir) -#include "xpu_data_transfer.h" // NOLINT(build/include_subdir) -#include "tvm_allocator.h" // NOLINT(build/include_subdir) -#include "tvm_utils.h" // NOLINT(build/include_subdir) -#include "tvm_api.h" // NOLINT(build/include_subdir) -#ifdef USE_TVM_HASH -#include "hash_alg/hasher.h" // NOLINT(build/include_subdir) -#endif - -using ONNX_NAMESPACE::TensorShapeProto; - -namespace onnxruntime { -namespace tvm { - -// Information to construct kernel function state. -struct TVMFuncState { - AllocateFunc allocate_func = nullptr; - DestroyFunc release_func = nullptr; - AllocatorHandle allocator = nullptr; - std::shared_ptr compiler = nullptr; -}; - -TvmSoExecutionProvider::TvmSoExecutionProvider(const TvmEPOptions& options) - : IExecutionProvider{kTvmExecutionProvider}, - options_{options} { - // Get environment variables - const Env& env_instance = Env::Default(); - - const std::string dump_subgraphs_env = env_instance.GetEnvironmentVar(env_vars::kDumpSubgraphs); - ORT_ENFORCE(dump_subgraphs_env.empty(), "TVM EP processing shared lib does not support subgraphs"); -} - -std::vector TvmSoExecutionProvider::CreatePreferredAllocators() { - AllocatorCreationInfo default_memory_info = {[](int) { - return std::make_unique(); - }, - 0, false}; - return std::vector{CreateAllocator(default_memory_info)}; -} - -TvmSoExecutionProvider::~TvmSoExecutionProvider() {} - -std::vector> -TvmSoExecutionProvider::GetCapability(const GraphViewer& graph_viewer, - const IKernelLookup& /*kernel_lookup*/) const { - std::vector> result; - if (graph_viewer.IsSubgraph()) { - return result; - } - - const auto& init_tensors = graph_viewer.GetAllInitializedTensors(); - - std::unordered_set required_initializers; - const std::vector& sorted_nodes = graph_viewer.GetNodesInTopologicalOrder(); - std::unique_ptr sub_graph = std::make_unique(); - for (auto& node_idx : sorted_nodes) { - graph_viewer.GetNode(node_idx)->ForEachDef([&required_initializers, &init_tensors](const NodeArg& node_arg, bool is_input) { - if (is_input && init_tensors.count(node_arg.Name())) { - required_initializers.insert(node_arg.Name()); - } }, true); - } - - auto meta_def = std::make_unique<::onnxruntime::IndexedSubGraph::MetaDef>(); - meta_def->name = "TVMStandalone"; - meta_def->domain = "StandaloneTest"; - std::vector inputs; - std::vector outputs; - - for (auto& nodeArgPtr : graph_viewer.GetInputs()) { - inputs.push_back(nodeArgPtr->Name()); - } - - for (auto& name : required_initializers) { - inputs.push_back(name); - } - - for (auto& nodeArgPtr : graph_viewer.GetOutputs()) { - outputs.push_back(nodeArgPtr->Name()); - } - meta_def->inputs = inputs; - meta_def->outputs = outputs; - meta_def->since_version = 1; - meta_def->status = ONNX_NAMESPACE::EXPERIMENTAL; - sub_graph->SetMetaDef(std::move(meta_def)); - sub_graph->nodes = sorted_nodes; - result.push_back( - std::make_unique(std::move(sub_graph))); - return result; -} - -common::Status TvmSoExecutionProvider::Compile(const std::vector& fused_nodes_and_graphs, - std::vector& node_compute_funcs) { - printOptions(); - for (auto& fused_node_graph : fused_nodes_and_graphs) { - const GraphViewer& graph_body_viewer = fused_node_graph.filtered_graph; - const Node& fused_node = fused_node_graph.fused_node; -#ifdef USE_TVM_HASH - if (options_.check_hash) { - ORT_ENFORCE(checkHash(ToUTF8String(fused_node.ModelPath().ToPathString())), - "Hash check shows that used tuning files were not obtained for the given onnx-model"); - } -#endif - const std::string func_name = fused_node.Name(); - - compilers_[func_name] = std::make_shared(); - InputsInfoMap all_input_shapes; - auto mod = compileModel(func_name, graph_body_viewer, all_input_shapes); - - std::vector output_tensors(graph_body_viewer.GetOutputs().size()); - prepareOutputTensors(output_tensors); - - runners_[func_name] = std::make_shared(options_, mod, all_input_shapes, output_tensors); - - // TODO(vvchernov): implement ops checking and mechanism of gracefully passing the responsibility to other EPs - // if the checking fails due to unsupported op(s) - NodeComputeInfo compute_info = prepareComputeInfo(func_name); - - node_compute_funcs.push_back(compute_info); - } - return Status::OK(); -} - -std::unique_ptr TvmSoExecutionProvider::GetDataTransfer() const { - // TODO(vvchernov): target or target host? - if (TvmEPOptionsHelper::checkGPUTarget(options_.target)) { - return std::make_unique(); - } else if (TvmEPOptionsHelper::checkCPUTarget(options_.target)) { - return std::make_unique(); - } else { - ORT_NOT_IMPLEMENTED("TVM GetDataTransfer is not implemented for target ", options_.target); - } -} - -void TvmSoExecutionProvider::printOptions() { - LOGS(*GetLogger(), INFO) << options_; -} - -#ifdef USE_TVM_HASH -bool TvmSoExecutionProvider::checkHash(const std::string& onnx_path) const { - auto hasher = Hasher("sha256"); - std::string onnx_str = readFromFile(onnx_path); - std::string onnx_hash = hasher.hash(onnx_str.c_str(), onnx_str.size()); - onnx_str.clear(); - std::string hash; - if (options_.hash_file_path.empty()) { - // TODO(vvchernov): align hash file name with OctoML team - hash = readFromFile(options_.so_folder + "/hash.txt"); - } else { - hash = readFromFile(options_.hash_file_path); - } - return onnx_hash == hash; -} -#endif - -std::shared_ptr TvmSoExecutionProvider::compileModel(const std::string& func_name, - const GraphViewer& graph_viewer, - InputsInfoMap& all_input_shapes) { - all_input_shapes.clear(); - - TVMTensorShapes input_shapes; - if (options_.freeze_weights) { - setInputShapesForFreezedNN(graph_viewer, input_shapes, all_input_shapes); - } else { - setInputShapesForUnfreezedNN(graph_viewer, input_shapes, all_input_shapes); - } - - std::shared_ptr mod = compilers_[func_name]->operator()(options_, input_shapes); - - return mod; -} - -void TvmSoExecutionProvider::setInputShapesForFreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, - InputsInfoMap& all_input_shapes) { - const std::vector& all_nodes = graph_viewer.GetInputsIncludingInitializers(); - - size_t indx = 0; - for (const auto* node : all_nodes) { - if (!graph_viewer.IsInitializedTensor(node->Name())) { - TensorShapeVector shape = getInputShape(node); - all_input_shapes[indx++] = shape; - input_shapes.emplace_back(shape); - } - } -} - -void TvmSoExecutionProvider::setInputShapesForUnfreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, - InputsInfoMap& all_input_shapes) { - const std::vector& all_nodes = graph_viewer.GetInputsIncludingInitializers(); - - size_t indx = 0; - for (const auto* node : all_nodes) { - TensorShapeVector shape = getInputShape(node); - all_input_shapes[indx++] = shape; - if (!graph_viewer.IsInitializedTensor(node->Name())) { - input_shapes.emplace_back(shape); - } - } -} - -TensorShapeVector TvmSoExecutionProvider::getInputShape(const NodeArg* node) { - TensorShapeVector shape; - const auto& node_name = node->Name(); - if (!options_.input_shapes.empty() && - options_.input_shapes.count(node_name)) { - shape = options_.input_shapes[node_name]; - } else { - shape = convertTensorShape(*node->Shape()); - } - - return shape; -} - -TensorShapeVector TvmSoExecutionProvider::convertTensorShape(const TensorShapeProto& shape_proto) { - TensorShape ort_shape = utils::GetTensorShapeFromTensorShapeProto(shape_proto); - size_t dims = ort_shape.NumDimensions(); - - TensorShapeVector shape(dims); - for (size_t j = 0; j < dims; ++j) { - int64_t dim = int64_t(ort_shape[j]); - ORT_ENFORCE(dim > 0, "Input dimension is not positive value (dim = " + std::to_string(dim) + "). " + - "Please use provider options to setup input_names and input_shapes"); - shape[j] = dim; - } - - return shape; -} - -void TvmSoExecutionProvider::prepareOutputTensors(std::vector& output_tensors) { - for (DLTensor& t : output_tensors) { - // Draft for tensor, correct data is defined during inference - t.strides = nullptr; - t.byte_offset = 0; - t.data = nullptr; - t.ndim = 0; - t.shape = nullptr; - } -} - -NodeComputeInfo TvmSoExecutionProvider::prepareComputeInfo(const std::string& func_name) { - NodeComputeInfo compute_info; - compute_info.create_state_func = std::bind(&TvmSoExecutionProvider::createStateFunc, - this, - std::placeholders::_1, - std::placeholders::_2); - - compute_info.release_state_func = [](FunctionState state) { - if (state) - delete static_cast(state); - }; - - compute_info.compute_func = *runners_[func_name].get(); - - return compute_info; -} - -int TvmSoExecutionProvider::createStateFunc(ComputeContext* context, FunctionState* state) { - auto* state_ptr = new TVMFuncState(); - *state_ptr = {context->allocate_func, - context->release_func, - context->allocator_handle, - compilers_[context->node_name]}; - // TODO(vvchernov): Who and when release state? - *state = state_ptr; - return 0; -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/tvm_so_execution_provider.h b/onnxruntime/core/providers/tvm/tvm_so_execution_provider.h deleted file mode 100644 index d3840f46b5b55..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_so_execution_provider.h +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_SO_EXECUTION_PROVIDER_H_ -#define ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_SO_EXECUTION_PROVIDER_H_ - -#include -#include -#include -#include - -#include "core/common/logging/logging.h" -#include "core/framework/execution_provider.h" -#include - -#include "tvm_compiler.h" // NOLINT(build/include_subdir) -#include "tvm_runner.h" // NOLINT(build/include_subdir) - -namespace onnxruntime { -class Graph; -class NodeArg; -namespace tvm { - -class TvmSoExecutionProvider : public IExecutionProvider { - using Compiler = TVMCompilerBase; - using Compilers = std::unordered_map>; - using Runner = TVMRunner; - using Runners = std::unordered_map>; - - public: - explicit TvmSoExecutionProvider(const TvmEPOptions& options); - virtual ~TvmSoExecutionProvider(); - - std::vector> - GetCapability(const onnxruntime::GraphViewer& graph, - const IKernelLookup& /*kernel_lookup*/) const override; - - common::Status Compile(const std::vector& fused_nodes_and_graphs, - std::vector& node_compute_funcs) override; - std::unique_ptr GetDataTransfer() const override; - std::vector CreatePreferredAllocators() override; - - private: - void printOptions(); -#ifdef USE_TVM_HASH - bool checkHash(const std::string& onnx_path) const; -#endif - std::shared_ptr compileModel(const std::string& func_name, - const GraphViewer& graph_viewer, - InputsInfoMap& inputs_info); // NOLINT - void setInputShapesForFreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, // NOLINT - InputsInfoMap& all_input_shapes); // NOLINT - void setInputShapesForUnfreezedNN(const GraphViewer& graph_viewer, - TVMTensorShapes& input_shapes, // NOLINT - InputsInfoMap& all_input_shapes); // NOLINT - TensorShapeVector getInputShape(const NodeArg* node); - TensorShapeVector convertTensorShape(const ONNX_NAMESPACE::TensorShapeProto& shape_proto); - void prepareOutputTensors(std::vector& output_tensors); // NOLINT - NodeComputeInfo prepareComputeInfo(const std::string& func_name); - int createStateFunc(ComputeContext*, FunctionState*); - - private: - TvmEPOptions options_; - Compilers compilers_; - Runners runners_; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // ONNXRUNTIME_CORE_PROVIDERS_TVM_TVM_SO_EXECUTION_PROVIDER_H_ diff --git a/onnxruntime/core/providers/tvm/tvm_utils.cc b/onnxruntime/core/providers/tvm/tvm_utils.cc deleted file mode 100644 index e0a5b566835c8..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_utils.cc +++ /dev/null @@ -1,31 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_UTILS_H -#define TVM_UTILS_H - -#include -#include - -#include "tvm_utils.h" // NOLINT(build/include_subdir) - -namespace onnxruntime { -namespace tvm { - -std::string readFromFile(const std::string& file_path) { - std::string str; - - std::ifstream t(file_path); - t.seekg(0, std::ios::end); - str.reserve(t.tellg()); - t.seekg(0, std::ios::beg); - - str.assign((std::istreambuf_iterator(t)), - std::istreambuf_iterator()); - return str; -} - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_UTILS_H diff --git a/onnxruntime/core/providers/tvm/tvm_utils.h b/onnxruntime/core/providers/tvm/tvm_utils.h deleted file mode 100644 index de77368c715b9..0000000000000 --- a/onnxruntime/core/providers/tvm/tvm_utils.h +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef TVM_UTILS_H -#define TVM_UTILS_H - -#include - -#include "tvm_common.h" - -#include "core/session/onnxruntime_cxx_api.h" -#include "core/framework/ortdevice.h" -#include "core/common/common.h" - -namespace onnxruntime { -namespace tvm { - -inline DLDataType GetDataType(ONNXTensorElementDataType type) { - switch (type) { - case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8: - return {kDLUInt, 8, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8: - return {kDLInt, 8, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16: - return {kDLUInt, 16, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16: - return {kDLInt, 16, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32: - return {kDLUInt, 32, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: - return {kDLInt, 32, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64: - return {kDLUInt, 64, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64: - return {kDLInt, 64, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16: - return {kDLFloat, 16, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: - return {kDLFloat, 32, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE: - return {kDLFloat, 64, 1}; - case ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL: - return {kDLUInt, 1, 1}; - default: - ORT_NOT_IMPLEMENTED("Unsupported data type"); - } -} - -inline DLDevice GetDLDevice(OrtMemoryInfoDeviceType device_type) { - DLDevice context; - switch (device_type) { - case OrtDevice::CPU: - context = {kDLCPU, 0}; - break; - case OrtDevice::GPU: - context = {kDLVulkan, 0}; - break; - default: - ORT_NOT_IMPLEMENTED("Unsupported device"); - break; - } - return context; -} - -std::string readFromFile(const std::string& file_path); - -} // namespace tvm -} // namespace onnxruntime - -#endif // TVM_UTILS_H diff --git a/onnxruntime/core/providers/tvm/xpu_data_transfer.cc b/onnxruntime/core/providers/tvm/xpu_data_transfer.cc deleted file mode 100644 index 4011dee7b7b7f..0000000000000 --- a/onnxruntime/core/providers/tvm/xpu_data_transfer.cc +++ /dev/null @@ -1,84 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#include "core/framework/tensor.h" - -#include "xpu_data_transfer.h" -#include "tvm_utils.h" - -namespace onnxruntime { -namespace tvm { - -XPUDataTransfer::XPUDataTransfer() { -} - -XPUDataTransfer::~XPUDataTransfer() { -} - -bool XPUDataTransfer::CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const { - return (src_device.Type() == OrtDevice::CPU && dst_device.Type() == OrtDevice::CPU) || - (src_device.Type() == OrtDevice::GPU || dst_device.Type() == OrtDevice::GPU); -} - -common::Status XPUDataTransfer::CopyTensor(const Tensor& src, Tensor& dst) const { - size_t bytes = src.SizeInBytes(); - const void* src_data = src.DataRaw(); - void* dst_data = dst.MutableDataRaw(); - const auto src_device_type = src.Location().device.Type(); - const auto dst_device_type = dst.Location().device.Type(); - - if ((src_device_type == OrtDevice::CPU) && (dst_device_type == OrtDevice::CPU)) { - if (src_data == dst_data) { - // no need copying as both pointers are referring to same piece of memory. - return Status::OK(); - } - memcpy(dst_data, src_data, bytes); - } else { - DLTensor tvm_src, tvm_dst; - DLDataType dl_type{kDLInt, 8, 1}; - std::vector shape{int64_t(bytes)}; - // Construct source DLTensor - tvm_src.device = GetDLDevice(static_cast(src_device_type)); - tvm_src.dtype = dl_type; - tvm_src.strides = nullptr; - tvm_src.byte_offset = 0; - tvm_src.data = const_cast(src_data); - tvm_src.ndim = 1; - tvm_src.shape = shape.data(); - // Construct destination DLTensor - tvm_dst.device = GetDLDevice(static_cast(dst_device_type)); - tvm_dst.dtype = dl_type; - tvm_dst.strides = nullptr; - tvm_dst.byte_offset = 0; - tvm_dst.data = dst_data; - tvm_dst.ndim = 1; - tvm_dst.shape = shape.data(); - // Copying from src to dst - TVMDeviceCopyDataFromTo(&tvm_src, &tvm_dst, nullptr); - } - return Status::OK(); -} - -DLDevice XPUDataTransfer::get_context(const OrtDevice& device) const { - return GetDLDevice(static_cast(device.Type())); -} - -bool TvmCPUDataTransfer::CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const { - return src_device.Type() == OrtDevice::CPU && dst_device.Type() == OrtDevice::CPU; -} - -common::Status TvmCPUDataTransfer::CopyTensor(const Tensor& src, Tensor& dst) const { - const void* src_data = src.DataRaw(); - void* dst_data = dst.MutableDataRaw(); - if (src_data == dst_data) { - // no need copying as both pointers are referring to same piece of memory. - return Status::OK(); - } - // Copying only happens between two same size tensors. - ORT_ENFORCE(src.SizeInBytes() == dst.SizeInBytes()); - memcpy(dst_data, src_data, src.SizeInBytes()); - return Status::OK(); -} - -} // namespace tvm -} // namespace onnxruntime diff --git a/onnxruntime/core/providers/tvm/xpu_data_transfer.h b/onnxruntime/core/providers/tvm/xpu_data_transfer.h deleted file mode 100644 index a2cf55b241bb1..0000000000000 --- a/onnxruntime/core/providers/tvm/xpu_data_transfer.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) Microsoft Corporation. All rights reserved. -// Licensed under the MIT License. - -#ifndef XPU_DATA_TRANSFER -#define XPU_DATA_TRANSFER - -#include "core/framework/data_transfer.h" -#include "tvm_common.h" - -namespace onnxruntime { -namespace tvm { - -class XPUDataTransfer : public IDataTransfer { - public: - XPUDataTransfer(); - ~XPUDataTransfer(); - - bool CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const override; - - // Dumpen MSVC warning about not fully overriding - using IDataTransfer::CopyTensor; - common::Status CopyTensor(const Tensor& src, Tensor& dst) const override; - DLDevice get_context(const OrtDevice& device) const; -}; - -class TvmCPUDataTransfer : public IDataTransfer { - public: - TvmCPUDataTransfer() = default; - // Dampen MSVC warning about not fully overriding CopyTensor - using IDataTransfer::CopyTensor; - bool CanCopy(const OrtDevice& src_device, const OrtDevice& dst_device) const override; - common::Status CopyTensor(const Tensor& src, Tensor& dst) const override; -}; - -} // namespace tvm -} // namespace onnxruntime - -#endif // XPU_DATA_TRANSFER diff --git a/onnxruntime/core/session/provider_registration.cc b/onnxruntime/core/session/provider_registration.cc index 8c512c561ea8c..8bea347c85280 100644 --- a/onnxruntime/core/session/provider_registration.cc +++ b/onnxruntime/core/session/provider_registration.cc @@ -205,15 +205,6 @@ ORT_API_STATUS_IMPL(OrtSessionOptionsAppendExecutionProvider_Nnapi, } #endif -#ifndef USE_TVM -ORT_API_STATUS_IMPL(OrtSessionOptionsAppendExecutionProvider_Tvm, - _In_ OrtSessionOptions* options, _In_ const char* settings) { - ORT_UNUSED_PARAMETER(options); - ORT_UNUSED_PARAMETER(settings); - return CreateNotEnabledStatus("Tvm"); -} -#endif - #ifdef __cplusplus } #endif diff --git a/onnxruntime/python/onnxruntime_pybind_state.cc b/onnxruntime/python/onnxruntime_pybind_state.cc index 4d9583be0ef0f..54accf7ed88f3 100644 --- a/onnxruntime/python/onnxruntime_pybind_state.cc +++ b/onnxruntime/python/onnxruntime_pybind_state.cc @@ -1125,16 +1125,6 @@ std::unique_ptr CreateExecutionProviderInstance( LOGS_DEFAULT(WARNING) << "Failed to create " << type << ". Please refer https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html#requirements to ensure all dependencies are met."; } } -#endif - } else if (type == kTvmExecutionProvider) { -#if USE_TVM - onnxruntime::tvm::TvmEPOptions info{}; - const auto it = provider_options_map.find(type); - if (it != provider_options_map.end()) { - info = onnxruntime::tvm::TvmEPOptionsHelper::FromProviderOptions(it->second); - } - - return onnxruntime::TVMProviderFactoryCreator::Create(info)->CreateProvider(); #endif } else if (type == kVitisAIExecutionProvider) { #ifdef USE_VITISAI diff --git a/onnxruntime/python/onnxruntime_pybind_state_common.h b/onnxruntime/python/onnxruntime_pybind_state_common.h index fa4916f8922f2..b71081bf20efc 100644 --- a/onnxruntime/python/onnxruntime_pybind_state_common.h +++ b/onnxruntime/python/onnxruntime_pybind_state_common.h @@ -24,7 +24,7 @@ struct OrtStatus { char msg[1]; // a null-terminated string }; -#define BACKEND_DEVICE BACKEND_PROC BACKEND_DNNL BACKEND_OPENVINO BACKEND_TVM BACKEND_OPENBLAS BACKEND_MIGRAPHX BACKEND_ACL BACKEND_ARMNN BACKEND_DML BACKEND_CANN BACKEND_WEBGPU +#define BACKEND_DEVICE BACKEND_PROC BACKEND_DNNL BACKEND_OPENVINO BACKEND_OPENBLAS BACKEND_MIGRAPHX BACKEND_ACL BACKEND_ARMNN BACKEND_DML BACKEND_CANN BACKEND_WEBGPU #include "core/session/onnxruntime_cxx_api.h" #include "core/providers/providers.h" #include "core/providers/provider_factory_creators.h" @@ -75,12 +75,6 @@ struct OrtStatus { #define BACKEND_OPENVINO "" #endif -#ifdef USE_TVM -#define BACKEND_TVM "-TVM" -#else -#define BACKEND_TVM "" -#endif - #if USE_OPENBLAS #define BACKEND_OPENBLAS "-OPENBLAS" #else @@ -141,9 +135,6 @@ extern std::string openvino_device_type; } } // namespace onnxruntime #endif -#ifdef USE_TVM -#include "core/providers/tvm/tvm_ep_options.h" -#endif #ifdef USE_ACL #include "core/providers/acl/acl_provider_factory.h" #endif @@ -444,10 +435,6 @@ std::shared_ptr CreateExecutionProviderFactory_MIGrap std::shared_ptr CreateExecutionProviderFactory_MIGraphX(int device_id); std::shared_ptr CreateExecutionProviderFactory_Cuda(const OrtCUDAProviderOptions* params); std::shared_ptr CreateExecutionProviderFactory_Dnnl(const OrtDnnlProviderOptions* params); -#ifdef USE_TVM -std::shared_ptr CreateExecutionProviderFactory_Tvm(const tvm::TvmEPOptions& info); -std::shared_ptr CreateExecutionProviderFactory_Tvm(const char* params); -#endif std::shared_ptr CreateExecutionProviderFactory_ACL(bool enable_fast_math); std::shared_ptr CreateExecutionProviderFactory_ArmNN(int use_arena); std::shared_ptr CreateExecutionProviderFactory_DML(int device_id); diff --git a/onnxruntime/python/providers/tvm/__init__.py b/onnxruntime/python/providers/tvm/__init__.py deleted file mode 100644 index 4bcbc0bfef586..0000000000000 --- a/onnxruntime/python/providers/tvm/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. See License.txt in the project root for -# license information. -# -------------------------------------------------------------------------- -""" -JIT interface implementing packed functions that -import and compile frontend models -""" -from .ort import ANSOR_TYPE, AUTO_TVM_TYPE, onnx_compile # noqa: F401 diff --git a/onnxruntime/python/providers/tvm/extend_python_file.py b/onnxruntime/python/providers/tvm/extend_python_file.py deleted file mode 100644 index 65902619f8150..0000000000000 --- a/onnxruntime/python/providers/tvm/extend_python_file.py +++ /dev/null @@ -1,54 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. See License.txt in the project root for -# license information. -# -------------------------------------------------------------------------- - -import argparse -import textwrap - - -def rewrite_target_file(target): - with open(target, "a") as f: - f.write( - textwrap.dedent( - """ - import warnings - - try: - # This import is necessary in order to delegate the loading of libtvm.so to TVM. - import tvm - except ImportError as e: - warnings.warn( - f"WARNING: Failed to import TVM, libtvm.so was not loaded. More details: {e}" - ) - try: - # Working between the C++ and Python parts in TVM EP is done using the PackedFunc and - # Registry classes. In order to use a Python function in C++ code, it must be registered in - # the global table of functions. Registration is carried out through the JIT interface, - # so it is necessary to call special functions for registration. - # To do this, we need to make the following import. - import onnxruntime.providers.tvm - except ImportError as e: - warnings.warn( - f"WARNING: Failed to register python functions to work with TVM EP. More details: {e}" - ) - """ - ) - ) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--target_file", - type=str, - required=True, - help="Path to the file to be expanded.", - ) - args = parser.parse_args() - rewrite_target_file(args.target_file) - - -if __name__ == "__main__": - main() diff --git a/onnxruntime/python/providers/tvm/ort.py b/onnxruntime/python/providers/tvm/ort.py deleted file mode 100644 index be6d23f39c532..0000000000000 --- a/onnxruntime/python/providers/tvm/ort.py +++ /dev/null @@ -1,140 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. See License.txt in the project root for -# license information. -# -------------------------------------------------------------------------- - -import collections -import copy -import logging -import os - -import onnx -import tvm -from tvm import auto_scheduler, autotvm, relay -from tvm.contrib import graph_executor -from tvm.relay import vm - -log = logging.getLogger("tvm_ep") - -ANSOR_TYPE = "Ansor" -AUTO_TVM_TYPE = "AutoTVM" - - -@tvm.register_func("tvm_onnx_import_and_compile") -def onnx_compile( - model_string, - model_path, - executor, - target, - target_host, - opt_level, - opset, - freeze_params, - input_shapes, - nhwc=False, - tuning_logfile="", - tuning_type=AUTO_TVM_TYPE, -): - def get_tvm_executor(irmod, executor, target, params): - if executor == "vm": - log.info("Build TVM virtual machine") - lib = vm.compile( - copy.deepcopy(irmod), - target, - params=params, - ) - elif executor == "graph": - log.info("Build TVM graph executor") - lib = relay.build(irmod, target=target, params=params) - else: - log.error(f'Executor type {executor} is unsupported. Only "vm" and "graph" types are supported') - return None - return lib - - model = onnx.load_model_from_string(bytes(model_string)) - if model_path: - base_dir = os.path.dirname(os.path.abspath(model_path)) - onnx.load_external_data_for_model(model, base_dir) - - # Collect only feed input names from all input names - all_input_names = [node.name for node in model.graph.input] - all_initializer = [node.name for node in model.graph.initializer] - net_feed_input_names = list(set(all_input_names) - set(all_initializer)) - - # Match names and input shapes - all_input_mapping = [(name, shape) for (name, shape) in zip(all_input_names, input_shapes)] - # Using an ordereddict maintains input ordering. - shape_dict = collections.OrderedDict(all_input_mapping) - # Get only feed input pairs - feed_shape_dict = {} - for name in net_feed_input_names: - feed_shape_dict[name] = shape_dict[name] - - irmod, params = relay.frontend.from_onnx(model, feed_shape_dict, opset=opset, freeze_params=freeze_params) - irmod = relay.transform.DynamicToStatic()(irmod) - - # Tuning file can be set by client through ep options - if not tuning_logfile: - tuning_logfile = os.getenv("AUTOTVM_TUNING_LOG") - lib = None - tvm_target = tvm.target.Target(target, host=target_host) - if tuning_logfile: - if tuning_type == ANSOR_TYPE: - desired_layouts = { - "nn.conv2d": ["NHWC", "default"], - "nn.conv2d_transpose": ["NHWC", "default"], - "nn.upsampling": ["NHWC", "default"], - "vision.roi_align": ["NHWC", "default"], - } - log.info("Use tuning file from %s: %s", ANSOR_TYPE, tuning_logfile) - with auto_scheduler.ApplyHistoryBest(tuning_logfile): # noqa: SIM117 - with tvm.transform.PassContext( - opt_level=opt_level, - config={ - "relay.backend.use_auto_scheduler": True, - "relay.FuseOps.max_depth": 30, - }, - ): - if nhwc: - seq = tvm.transform.Sequential( - [ - relay.transform.InferType(), - relay.transform.ConvertLayout(desired_layouts), - relay.transform.EliminateCommonSubexpr(), - relay.transform.FoldConstant(), - ] - ) - irmod = seq(irmod) - lib = get_tvm_executor(irmod, executor, tvm_target, params) - elif tuning_type == AUTO_TVM_TYPE: - with relay.build_config(opt_level=opt_level): - log.info("Use tuning file from %s: %s", AUTO_TVM_TYPE, tuning_logfile) - with autotvm.apply_history_best(tuning_logfile): - lib = get_tvm_executor(irmod, executor, tvm_target, params) - else: - log.error( - f"Tuning log type {tuning_type} is unsupported. " - f"Only {ANSOR_TYPE} and {AUTO_TVM_TYPE} types are supported" - ) - return None - else: - with tvm.transform.PassContext(opt_level=opt_level): - lib = get_tvm_executor(irmod, executor, tvm_target, params) - - if lib is None: - return None - - ctx = tvm.device(target, 0) - if executor == "vm": - m = tvm.runtime.vm.VirtualMachine(lib, ctx) - elif executor == "graph": - m = graph_executor.GraphModule(lib["default"](ctx)) - else: - print( - f"ERROR: Executor type {executor} is unsupported. ", - 'Only "vm" and "graph" types are supported', - ) - return None - - return m.module diff --git a/onnxruntime/test/framework/function_test.cc b/onnxruntime/test/framework/function_test.cc index fa3545ef27d72..180a75a64c10e 100644 --- a/onnxruntime/test/framework/function_test.cc +++ b/onnxruntime/test/framework/function_test.cc @@ -580,13 +580,7 @@ TEST(FunctionTest, TestInlinedLocalFunctionNotRemoved) { // myfun is not removed because it was claimed by InternalTestingEP model_proto = session_object.GetModel().ToProto(); -#ifdef USE_TVM - // TVM EP takes the whole graph and optimizes it within its own framework. - // It does not retain the original graph. - ASSERT_EQ(0, model_proto.functions_size()); -#else ASSERT_EQ(1, model_proto.functions_size()); -#endif } TEST(FunctionTest, TestInlinedFunctionDoesNotReserrectNonExistingArgs) { diff --git a/onnxruntime/test/platform/windows/stacktrace_test.cc b/onnxruntime/test/platform/windows/stacktrace_test.cc index de09dbcf270a9..9b1840f4b5d65 100644 --- a/onnxruntime/test/platform/windows/stacktrace_test.cc +++ b/onnxruntime/test/platform/windows/stacktrace_test.cc @@ -14,7 +14,6 @@ namespace onnxruntime { namespace test { using namespace ::testing; -// TVM is not working with StackTrace now. #if !defined(ORT_NO_EXCEPTIONS) TEST(StacktraceTests, BasicTests) { auto result = ::onnxruntime::GetStackTrace(); diff --git a/onnxruntime/test/python/onnxruntime_test_python_tvm.py b/onnxruntime/test/python/onnxruntime_test_python_tvm.py deleted file mode 100644 index 0080bf53520f2..0000000000000 --- a/onnxruntime/test/python/onnxruntime_test_python_tvm.py +++ /dev/null @@ -1,242 +0,0 @@ -# ------------------------------------------------------------------------- -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License. See License.txt in the project root for -# license information. -# -------------------------------------------------------------------------- -""" -Module for unit testing of TVM EP -""" - -import os -import sys -import tempfile -import unittest -from typing import Any, AnyStr, Dict, List, Tuple - -import numpy -import tvm -from numpy.testing import assert_almost_equal -from onnx import ModelProto, TensorProto, mapping -from onnx.helper import make_graph, make_model, make_node, make_tensor_value_info - -import onnxruntime - -numpy.random.seed(32) - - -def is_windows(): - """ - Function to determine the Windows system - """ - return sys.platform.startswith("win") - - -def get_model_with_dynamic_shapes() -> ModelProto: - """ - Create model with Dynamic Shapes - """ - x = make_tensor_value_info("X", TensorProto.FLOAT, [None, None]) # pylint: disable=invalid-name, no-member - a = make_tensor_value_info("A", TensorProto.FLOAT, [None, None]) # pylint: disable=invalid-name, no-member - b = make_tensor_value_info("B", TensorProto.FLOAT, [None, None]) # pylint: disable=invalid-name, no-member - y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None]) # pylint: disable=invalid-name, no-member - node1 = make_node("MatMul", ["X", "A"], ["XA"]) - node2 = make_node("Add", ["XA", "B"], ["Y"]) - graph = make_graph([node1, node2], "lr", [x, a, b], [y]) - onnx_model = make_model(graph) - return onnx_model - - -def get_model_with_fixed_shapes() -> ModelProto: - """ - Create model with Static Shapes - """ - - def change_input_shape(model: ModelProto, ind: int, shape: Tuple) -> None: - """ - Function to change the input form - """ - dims = model.graph.input[ind].type.tensor_type.shape.dim - assert len(dims) == len(shape), "Input rank and new shape rank do not match." - for i, new_dim in enumerate(shape): - model.graph.input[ind].type.tensor_type.shape.dim[i].dim_value = new_dim - - dynamic_model = get_model_with_dynamic_shapes() - change_input_shape(dynamic_model, 0, (1, 2)) # X - change_input_shape(dynamic_model, 1, (2, 2)) # A - change_input_shape(dynamic_model, 2, (1, 2)) # B - return dynamic_model - - -def get_input_data_for_model_with_dynamic_shapes() -> Dict[AnyStr, numpy.ndarray]: - """ - Create input data for model with dynamic shapes - """ - a = numpy.random.randn(2, 2).astype(numpy.float32) # pylint: disable=invalid-name - b = numpy.random.randn(1, 2).astype(numpy.float32) # pylint: disable=invalid-name - x = numpy.random.randn(1, 2).astype(numpy.float32) # pylint: disable=invalid-name - data = {"A": a, "B": b, "X": x} - return data - - -def get_input_data_for_model_with_fixed_shapes(onnx_model: ModelProto) -> Dict[AnyStr, numpy.ndarray]: - """ - Create input data for model with static shapes - """ - - def get_onnx_input_names(model: ModelProto) -> List[AnyStr]: - inputs = [node.name for node in model.graph.input] - initializer = [node.name for node in model.graph.initializer] - inputs = list(set(inputs) - set(initializer)) - return sorted(inputs) - - def get_onnx_input_types(model: ModelProto) -> List[numpy.dtype]: - input_names = get_onnx_input_names(model) - return [ - mapping.TENSOR_TYPE_TO_NP_TYPE[node.type.tensor_type.elem_type] - for node in sorted(model.graph.input, key=lambda node: node.name) - if node.name in input_names - ] - - def get_onnx_input_shapes(model: ModelProto) -> List[List[int]]: - input_names = get_onnx_input_names(model) - return [ - [dv.dim_value for dv in node.type.tensor_type.shape.dim] - for node in sorted(model.graph.input, key=lambda node: node.name) - if node.name in input_names - ] - - input_names = get_onnx_input_names(onnx_model) - input_shapes = get_onnx_input_shapes(onnx_model) - input_types = get_onnx_input_types(onnx_model) - assert len(input_names) == len(input_types) == len(input_shapes) - random_inputs = [numpy.random.uniform(size=shape).astype(dtype) for shape, dtype in zip(input_shapes, input_types)] - return dict(zip(input_names, random_inputs)) - - -def get_input_names_and_shapes(data: Dict[AnyStr, numpy.ndarray]) -> Tuple[List[AnyStr], List[AnyStr]]: - """ - Create text representations for model input names and shapes - """ - keys = list(data.keys()) - values = [data[key] for key in keys] - return ( - list(data.keys()), - [str(value.shape).replace(",", "").replace("(", "[").replace(")", "]") for value in values], - ) - - -def get_cpu_output(onnx_model: ModelProto, data: Dict[AnyStr, numpy.ndarray]) -> List[numpy.ndarray]: - """ - Run inference with CPUExecutionProvider - """ - # pylint: disable=no-member - sess = onnxruntime.InferenceSession( - onnx_model.SerializeToString(), - providers=["CPUExecutionProvider"], - ) - output = sess.run(None, data) - return output - - -def get_tvm_output( - onnx_model: ModelProto, data: Dict[AnyStr, numpy.ndarray], provider_options: Dict[AnyStr, Any] -) -> List[numpy.ndarray]: - """ - Run inference with TVMExecutionProvider - """ - session_options = onnxruntime.SessionOptions() # pylint: disable=no-member - session_options.log_severity_level = 0 - session_options.log_verbosity_level = 0 - # pylint: disable=no-member - session_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL - - sess = onnxruntime.InferenceSession( - onnx_model.SerializeToString(), - session_options, - providers=["TvmExecutionProvider"], - provider_options=[provider_options], - ) - - output = sess.run(None, data) - return output - - -# pylint: disable=no-member -def compile_virtual_machine(model: ModelProto, target_str: AnyStr) -> tvm.runtime.vm.Executable: - """ - Compile ONNX model using VirtualMachine - """ - ir_mod, _ = tvm.relay.frontend.from_onnx( - model, - opset=model.opset_import[0].version, - freeze_params=True, - ) - target = tvm.target.Target(target=target_str, host=target_str) - return tvm.relay.backend.vm.compile(ir_mod, target) - - -def serialize_virtual_machine(vm_exec: tvm.runtime.vm.Executable) -> AnyStr: - """ - Serialize VirtualMachine - """ - temp_directory = tempfile.mkdtemp() - path_consts = os.path.join(temp_directory, "consts") - vm_exec.move_late_bound_consts(path_consts, byte_limit=256) - lib_path = os.path.join(temp_directory, f"model.{'dll' if is_windows() else 'so'}") - code_path = os.path.join(temp_directory, "model.ro") - code, lib = vm_exec.save() - lib.export_library(lib_path) - with open(code_path, "wb") as code_file: - code_file.write(code) - return temp_directory - - -class TestTVM(unittest.TestCase): - """ - Unit tests for TVM EP - """ - - @staticmethod - def test_accuracy_for_model_with_dynamic_shapes(): - """ - Accuracy test for model with dynamic shapes - """ - onnx_model = get_model_with_dynamic_shapes() - data = get_input_data_for_model_with_dynamic_shapes() - - cpu_output = get_cpu_output(onnx_model, data) - names, shapes = get_input_names_and_shapes(data) - provider_options = dict( - target="llvm", - input_names=" ".join(names), - input_shapes=" ".join(shapes), - ) - tvm_output = get_tvm_output(onnx_model, data, provider_options) - - assert_almost_equal(cpu_output, tvm_output, decimal=5) - - @staticmethod - def test_accuracy_for_tvm_so(): - """ - Accuracy test for TVMso Ep - """ - onnx_model = get_model_with_fixed_shapes() - data = get_input_data_for_model_with_fixed_shapes(onnx_model) - - cpu_output = get_cpu_output(onnx_model, data) - - compiled_vm_exec = compile_virtual_machine(onnx_model, target_str="llvm") - so_folder = serialize_virtual_machine(compiled_vm_exec) - provider_options = dict( - target="llvm", - so_folder=so_folder, - ) - tvm_output = get_tvm_output(onnx_model, data, provider_options) - - assert_almost_equal(cpu_output, tvm_output, decimal=5) - - -if __name__ == "__main__": - if "TvmExecutionProvider" not in onnxruntime.get_available_providers(): - raise AssertionError(f"Unable to find 'TvmExecutionProvider' in {onnxruntime.get_available_providers()}") - unittest.main() diff --git a/onnxruntime/test/util/default_providers.cc b/onnxruntime/test/util/default_providers.cc index cb53db42304be..3519c5d72c060 100644 --- a/onnxruntime/test/util/default_providers.cc +++ b/onnxruntime/test/util/default_providers.cc @@ -188,14 +188,6 @@ std::unique_ptr DnnlExecutionProviderWithOptions(const OrtDn return nullptr; } -// std::unique_ptr DefaultTvmExecutionProvider() { -// #ifdef USE_TVM -// return TVMProviderFactoryCreator::Create("")->CreateProvider(); -// #else -// return nullptr; -// #endif -// } - std::unique_ptr DefaultNnapiExecutionProvider() { // The NNAPI EP uses a stub implementation on non-Android platforms so cannot be used to execute a model. // Manually append an NNAPI EP instance to the session to unit test the GetCapability and Compile implementation. diff --git a/onnxruntime/test/util/include/default_providers.h b/onnxruntime/test/util/include/default_providers.h index ed95bf67f1ffb..9b44150d972db 100644 --- a/onnxruntime/test/util/include/default_providers.h +++ b/onnxruntime/test/util/include/default_providers.h @@ -20,7 +20,6 @@ std::shared_ptr CreateExecutionProviderFactory_Dnnl(c std::shared_ptr CreateExecutionProviderFactory_MIGraphX(const OrtMIGraphXProviderOptions* params); std::shared_ptr CreateExecutionProviderFactory_Nnapi( uint32_t flags, const optional& partitioning_stop_ops_list); -// std::shared_ptr CreateExecutionProviderFactory_Tvm(const char*); std::shared_ptr CreateExecutionProviderFactory_VSINPU(); std::shared_ptr CreateExecutionProviderFactory_Rknpu(); std::shared_ptr CreateExecutionProviderFactory_Rocm(const OrtROCMProviderOptions* provider_options); diff --git a/onnxruntime/test/util/include/providers.h b/onnxruntime/test/util/include/providers.h index a73b237ae10df..01be1a444646b 100644 --- a/onnxruntime/test/util/include/providers.h +++ b/onnxruntime/test/util/include/providers.h @@ -7,9 +7,6 @@ #ifdef USE_DNNL #include "core/providers/dnnl/dnnl_provider_factory.h" #endif -#ifdef USE_TVM -#include "core/providers/tvm/tvm_provider_factory.h" -#endif #ifdef USE_OPENVINO #include "core/providers/openvino/openvino_provider_factory.h" #endif diff --git a/tools/ci_build/build.py b/tools/ci_build/build.py index 24dc6124d4a89..aa1198102f978 100644 --- a/tools/ci_build/build.py +++ b/tools/ci_build/build.py @@ -584,13 +584,7 @@ def convert_arg_line_to_args(self, arg_line): parser.add_argument("--use_preinstalled_eigen", action="store_true", help="Use pre-installed Eigen.") parser.add_argument("--eigen_path", help="Path to pre-installed Eigen.") parser.add_argument("--enable_msinternal", action="store_true", help="Enable for Microsoft internal builds only.") - parser.add_argument("--llvm_path", help="Path to llvm dir") parser.add_argument("--use_vitisai", action="store_true", help="Build with Vitis-AI") - parser.add_argument("--use_tvm", action="store_true", help="Build with TVM") - parser.add_argument("--tvm_cuda_runtime", action="store_true", default=False, help="Build TVM with CUDA support") - parser.add_argument( - "--use_tvm_hash", action="store_true", help="Build ipp-crypto for hash generation. It is used by TVM EP only" - ) parser.add_argument("--use_tensorrt", action="store_true", help="Build with TensorRT") parser.add_argument( "--use_tensorrt_builtin_parser", action="store_true", default=True, help="Use TensorRT builtin parser" @@ -602,12 +596,6 @@ def convert_arg_line_to_args(self, arg_line): parser.add_argument("--migraphx_home", help="Path to MIGraphX installation dir") parser.add_argument("--use_full_protobuf", action="store_true", help="Use the full protobuf library") - parser.add_argument( - "--llvm_config", - type=str, - default="", - help="Path to llvm-config.exe for LLVM built from sources. It is strongly needed for build on Windows", - ) parser.add_argument( "--skip_onnx_tests", action="store_true", @@ -1031,16 +1019,11 @@ def generate_build_tree( "-Donnxruntime_USE_NNAPI_BUILTIN=" + ("ON" if args.use_nnapi else "OFF"), "-Donnxruntime_USE_VSINPU=" + ("ON" if args.use_vsinpu else "OFF"), "-Donnxruntime_USE_RKNPU=" + ("ON" if args.use_rknpu else "OFF"), - "-Donnxruntime_USE_LLVM=" + ("ON" if args.use_tvm else "OFF"), "-Donnxruntime_ENABLE_MICROSOFT_INTERNAL=" + ("ON" if args.enable_msinternal else "OFF"), "-Donnxruntime_USE_VITISAI=" + ("ON" if args.use_vitisai else "OFF"), "-Donnxruntime_USE_TENSORRT=" + ("ON" if args.use_tensorrt else "OFF"), "-Donnxruntime_USE_TENSORRT_BUILTIN_PARSER=" + ("ON" if args.use_tensorrt_builtin_parser and not args.use_tensorrt_oss_parser else "OFF"), - # set vars for TVM - "-Donnxruntime_USE_TVM=" + ("ON" if args.use_tvm else "OFF"), - "-Donnxruntime_TVM_CUDA_RUNTIME=" + ("ON" if args.use_tvm and args.tvm_cuda_runtime else "OFF"), - "-Donnxruntime_TVM_USE_HASH=" + ("ON" if args.use_tvm_hash else "OFF"), # set vars for migraphx "-Donnxruntime_USE_MIGRAPHX=" + ("ON" if args.use_migraphx else "OFF"), "-Donnxruntime_DISABLE_CONTRIB_OPS=" + ("ON" if args.disable_contrib_ops else "OFF"), @@ -1172,8 +1155,6 @@ def generate_build_tree( cmake_args.append("-Donnxruntime_ROCM_VERSION=" + args.rocm_version) if args.use_tensorrt: cmake_args.append("-Donnxruntime_TENSORRT_HOME=" + tensorrt_home) - if args.llvm_config: - cmake_args.append("-Donnxruntime_TVM_USE_LLVM=" + args.llvm_config) if args.use_cuda: add_default_definition(cmake_extra_defines, "onnxruntime_USE_CUDA", "ON") @@ -1256,9 +1237,6 @@ def generate_build_tree( if args.use_full_protobuf or args.use_openvino or args.use_vitisai or args.gen_doc: cmake_args += ["-Donnxruntime_USE_FULL_PROTOBUF=ON", "-DProtobuf_USE_STATIC_LIBS=ON"] - if args.use_tvm and args.llvm_path is not None: - cmake_args += [f"-DLLVM_DIR={args.llvm_path}"] - if args.use_cuda and not is_windows(): nvml_stub_path = cuda_home + "/lib64/stubs" cmake_args += ["-DCUDA_CUDA_LIBRARY=" + nvml_stub_path] @@ -1659,16 +1637,6 @@ def generate_build_tree( cxxflags = cflags.copy() config_build_dir = get_config_build_dir(build_dir, config) os.makedirs(config_build_dir, exist_ok=True) - if args.use_tvm: - os.environ["PATH"] = ( - os.path.join(config_build_dir, "_deps", "tvm-build") - + os.pathsep - + os.path.join(config_build_dir, "_deps", "tvm-src") - + os.pathsep - + os.path.dirname(sys.executable) - + os.pathsep - + os.environ["PATH"] - ) preinstalled_dir = Path(build_dir) / config temp_cmake_args = cmake_args.copy() if cflags is not None and cxxflags is not None and len(cflags) != 0 and len(cxxflags) != 0: @@ -2097,8 +2065,6 @@ def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs): if args.enable_pybind: python_path = None - if args.use_tvm: - python_path = str((Path(build_dir) / config / "_deps" / "tvm-src" / "python").resolve()) # Disable python tests in a reduced build as we don't know which ops have been included and which # models can run. @@ -2221,17 +2187,6 @@ def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs): run_subprocess([sys.executable, "onnxruntime_test_python_keras.py"], cwd=cwd, dll_path=dll_path) -def tvm_run_python_tests(build_dir, configs): - for config in configs: - cwd = get_config_build_dir(build_dir, config) - if is_windows(): - cwd = os.path.join(cwd, config) - python_path = os.path.join(build_dir, config, "_deps", "tvm-src", "python") - run_subprocess( - [sys.executable, "onnxruntime_test_python_tvm.py"], cwd=cwd, python_path=os.path.abspath(python_path) - ) - - def run_nodejs_tests(nodejs_binding_dir): args = ["npm", "test", "--", "--timeout=90000"] if is_windows(): @@ -2251,7 +2206,6 @@ def build_python_wheel( use_dnnl, use_tensorrt, use_openvino, - use_tvm, use_vitisai, use_acl, use_armnn, @@ -2304,8 +2258,6 @@ def build_python_wheel( args.append("--use_openvino") elif use_dnnl: args.append("--use_dnnl") - elif use_tvm: - args.append("--use_tvm") elif use_vitisai: args.append("--use_vitisai") elif use_acl: @@ -2334,7 +2286,6 @@ def build_nuget_package( use_openvino, use_tensorrt, use_dnnl, - use_tvm, use_winml, use_qnn, enable_training_apis, @@ -2381,9 +2332,6 @@ def build_nuget_package( package_name = "/p:OrtPackageId=Microsoft.ML.OnnxRuntime.Gpu" elif use_rocm: package_name = "/p:OrtPackageId=Microsoft.ML.OnnxRuntime.ROCm" - elif use_tvm: - execution_provider = "/p:ExecutionProvider=tvm" - package_name = "/p:OrtPackageId=Microsoft.ML.OnnxRuntime.Tvm" elif use_qnn: execution_provider = "/p:ExecutionProvider=qnn" package_name = "/p:OrtPackageId=Microsoft.ML.OnnxRuntime.QNN" @@ -2625,7 +2573,7 @@ def main(): if args.use_tensorrt: args.use_cuda = True - if args.build_wheel or args.gen_doc or args.use_tvm or args.enable_training: + if args.build_wheel or args.gen_doc or args.enable_training: args.enable_pybind = True if ( @@ -2907,12 +2855,6 @@ def main(): run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs) - # TODO(agladyshev): - # to support Windows, we need to update .github/workflows/windows.yml - # and add to the PATH variable the following value: C:Program Files\LLVM\bin - if args.enable_pybind and args.use_tvm and not is_windows(): - tvm_run_python_tests(build_dir, configs) - # run node.js binding tests if args.build_nodejs and not args.skip_nodejs_tests: nodejs_binding_dir = os.path.normpath(os.path.join(source_dir, "js", "node")) @@ -2940,7 +2882,6 @@ def main(): args.use_dnnl, args.use_tensorrt, args.use_openvino, - args.use_tvm, args.use_vitisai, args.use_acl, args.use_armnn, @@ -2968,7 +2909,6 @@ def main(): args.use_openvino, args.use_tensorrt, args.use_dnnl, - args.use_tvm, args.use_winml, args.use_qnn, args.enable_training_apis, diff --git a/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml b/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml deleted file mode 100644 index 5f073433265fa..0000000000000 --- a/tools/ci_build/github/azure-pipelines/templates/orttraining-linux-gpu-test-ci-pipeline.yml +++ /dev/null @@ -1,41 +0,0 @@ -parameters: -- name: DockerImageTag - type: string -- name: BuildConfig - type: string - -steps: - -- template: jobs/download_training_test_data.yml - - # Entry point for all ORTModule tests - # The onnxruntime folder is deleted in the build directory - # to enforce use of the onnxruntime wheel - # Uninstall orttraining requirements.txt and install ortmodule requirements.txt before running tests. -- script: | - docker run \ - --gpus all \ - --shm-size=1024m \ - --rm \ - --volume $(Build.SourcesDirectory):/onnxruntime_src \ - --volume $(Build.BinariesDirectory)/${{ parameters.BuildConfig }}:/build \ - --volume $(Agent.TempDirectory)/mnist:/mnist \ - ${{ parameters.DockerImageTag }} \ - bash -c "rm -rf /build/onnxruntime/ && python3 -m pip show torch && python3 -m pip install torch==2.3.1+cu118 --index-url https://download.pytorch.org/whl/cu118 && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_tests.py --mnist /mnist --bert_data /bert_data/hf_data/glue_data/CoLA/original/raw' --cwd /build" \ - displayName: 'Run orttraining_ortmodule_tests.py' - condition: succeededOrFailed() - timeoutInMinutes: 60 - -# Entry point for all ort training api tests -- script: | - docker run \ - --gpus all \ - --shm-size=1024m \ - --rm \ - --volume $(Build.SourcesDirectory):/onnxruntime_src \ - --volume $(Build.BinariesDirectory)/${{ parameters.BuildConfig }}:/build \ - ${{ parameters.DockerImageTag }} \ - bash -c "rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m pip install torch==2.3.1+cu118 --index-url https://download.pytorch.org/whl/cu118 && /build/launch_test.py --cmd_line_with_args 'python orttraining_test_ort_apis.py --cwd /build' --cwd /build" \ - displayName: 'Run ORT Training APIs Tests' - condition: succeededOrFailed() - timeoutInMinutes: 120 diff --git a/tools/ci_build/github/azure-pipelines/templates/py-packaging-training-cuda-stage-steps.yml b/tools/ci_build/github/azure-pipelines/templates/py-packaging-training-cuda-stage-steps.yml deleted file mode 100644 index fc163d17e44a9..0000000000000 --- a/tools/ci_build/github/azure-pipelines/templates/py-packaging-training-cuda-stage-steps.yml +++ /dev/null @@ -1,209 +0,0 @@ -parameters: - build_py_parameters: '' - torch_version: '' - opset_version: '' - cuda_version: '' - cmake_cuda_architectures: '' - docker_file: '' - upload_wheel: '' - debug_build: '' - python_version: '' - stage_name: '' - SpecificArtifact: false - BuildId: '0' - build_pool_name: '' - -stages: - - stage: Build_${{ parameters.stage_name }} - variables: - - name: isMain - value: ${{ or(eq(variables['Build.SourceBranch'], 'refs/heads/main'), startsWith(variables['Build.SourceBranch'], 'refs/heads/rel-')) }} - - name: finalStorage - ${{ if eq(variables['isMain'], 'true') }}: - value: '--final_storage' - ${{ else }}: - value: '' - - name: buildConfig - ${{ if eq(parameters['debug_build'], 'true') }}: - value: 'Debug' - ${{ else }}: - value: 'Release' - - name: PythonVersion - value: ${{ parameters.python_version }} - - name: Repository - value: onnxruntimetraininggpubuild_cu${{ replace(parameters.cuda_version, '.', '') }}_py${{ replace(parameters.python_version, '.', '') }} - dependsOn: [] - - jobs: - - job: Build - pool: ${{ parameters.build_pool_name }} - timeoutInMinutes: 180 - steps: - - task: mspremier.PostBuildCleanup.PostBuildCleanup-task.PostBuildCleanup@3 - displayName: 'Clean Agent Directories' - condition: always() - - - task: CmdLine@2 - displayName: 'check variables' - inputs: - script: | - echo "Branch is "${{ variables['Build.SourceBranch'] }} && \ - echo "isMain is "${{ variables['isMain'] }} && \ - echo "final_storage is "${{ variables['finalStorage'] }} - - - checkout: self - clean: true - submodules: recursive - - - template: set-python-manylinux-variables-step.yml - - - template: get-docker-image-steps.yml - parameters: - Dockerfile: tools/ci_build/github/linux/docker/${{ parameters.docker_file }} - Context: tools/ci_build/github/linux/docker - DockerBuildArgs: >- - --build-arg TORCH_VERSION=${{ parameters.torch_version }} - --build-arg OPSET_VERSION=${{ parameters.opset_version }} - --build-arg PYTHON_VERSION=${{ parameters.python_version }} - --build-arg INSTALL_DEPS_EXTRA_ARGS=-tu - --build-arg BUILD_UID=$(id -u) - Repository: $(Repository) - - - task: CmdLine@2 - displayName: 'build onnxruntime' - inputs: - script: | - set -e -x - mkdir -p $HOME/.onnx - docker run --rm \ - --volume /data/onnx:/data/onnx:ro \ - --volume $(Build.SourcesDirectory):/onnxruntime_src \ - --volume $(Build.BinariesDirectory):/build \ - --volume /data/models:/build/models:ro \ - --volume $HOME/.onnx:/home/onnxruntimedev/.onnx \ - -e NIGHTLY_BUILD \ - -e DEFAULT_TRAINING_PACKAGE_DEVICE \ - -e BUILD_BUILDNUMBER \ - -e ORT_DISABLE_PYTHON_PACKAGE_LOCAL_VERSION \ - $(Repository) \ - $(PythonManylinuxDir)/bin/python3 /onnxruntime_src/tools/ci_build/build.py \ - --build_dir /build \ - --config ${{ variables['buildConfig'] }} \ - --skip_submodule_sync \ - --parallel --use_binskim_compliant_compile_flags \ - --build_wheel \ - --enable_onnx_tests \ - ${{ parameters.build_py_parameters }} \ - --cmake_extra_defines 'CMAKE_CUDA_ARCHITECTURES=${{ parameters.cmake_cuda_architectures }}' onnxruntime_BUILD_UNIT_TESTS=OFF \ - --use_cuda --cuda_version=${{ parameters.cuda_version }} --cuda_home=/usr/local/cuda-${{ parameters.cuda_version }} --cudnn_home=/usr/local/cuda-${{ parameters.cuda_version }}; - workingDirectory: $(Build.SourcesDirectory) - - - task: CopyFiles@2 - displayName: 'Copy Python Wheel to: $(Build.ArtifactStagingDirectory)' - inputs: - SourceFolder: '$(Build.BinariesDirectory)' - Contents: "${{ variables['buildConfig'] }}/dist/*.whl" - TargetFolder: '$(Build.ArtifactStagingDirectory)' - - - task: PublishBuildArtifacts@1 - displayName: 'Publish Artifact: ONNXRuntime python wheel and documentation' - inputs: - ArtifactName: "onnxruntime_gpu_${{ variables['buildConfig'] }}_${{ parameters.python_version }}" - - - template: component-governance-component-detection-steps.yml - parameters: - condition: 'succeeded' - - - template: clean-agent-build-directory-step.yml - - - stage: Test_${{ parameters.stage_name }} - variables: - - name: isMain - value: ${{ or(eq(variables['Build.SourceBranch'], 'refs/heads/main'), startsWith(variables['Build.SourceBranch'], 'refs/heads/rel-')) }} - - name: finalStorage - ${{ if eq(variables['isMain'], 'true') }}: - value: '--final_storage' - ${{ else }}: - value: '' - - name: buildConfig - ${{ if eq(parameters['debug_build'], 'true') }}: - value: 'Debug' - ${{ else }}: - value: 'Release' - - name: PythonVersion - value: ${{ parameters.python_version }} - - name: Repository - value: onnxruntimetraininggpubuild_cu${{ replace(parameters.cuda_version, '.', '') }}_py${{ replace(parameters.python_version, '.', '') }} - - name: UploadWheel - value: ${{ parameters.upload_wheel }} - dependsOn: Build_${{ parameters.stage_name }} - jobs: - - job: Test_GPU - pool: Onnxruntime-Linux-GPU - steps: - - task: mspremier.PostBuildCleanup.PostBuildCleanup-task.PostBuildCleanup@3 - displayName: 'Clean Agent Directories' - condition: always() - - - checkout: self - clean: true - submodules: none - - - template: jobs/download_training_test_data.yml - - - template: set-python-manylinux-variables-step.yml - - - template: flex-downloadPipelineArtifact.yml - parameters: - ArtifactName: "onnxruntime_gpu_${{ variables['buildConfig'] }}_${{ parameters.python_version }}" - StepName: 'Download Pipeline Artifact - Linux Training Build' - TargetPath: '$(Build.ArtifactStagingDirectory)' - SpecificArtifact: ${{ parameters.SpecificArtifact }} - BuildId: ${{ parameters.BuildId }} - - - script: | - set -e -x - whlfilename=$(ls $(Build.ArtifactStagingDirectory)/Release/dist/*.whl | head -n 1) ; \ - echo $whlfilename ; du -sh $whlfilename ; \ - (( $(wc -c < "$whlfilename") - 400*1024*1024 < 0 )) || ( echo 'Wheel size bigger than 400M'; exit 1) - displayName: 'Check wheel size' - continueOnError: true - - - template: get-docker-image-steps.yml - parameters: - Dockerfile: tools/ci_build/github/linux/docker/${{ parameters.docker_file }} - Context: tools/ci_build/github/linux/docker - DockerBuildArgs: >- - --build-arg TORCH_VERSION=${{ parameters.torch_version }} - --build-arg OPSET_VERSION=${{ parameters.opset_version }} - --build-arg PYTHON_VERSION=${{ parameters.python_version }} - --build-arg INSTALL_DEPS_EXTRA_ARGS=-tu - --build-arg BUILD_UID=$(id -u) - Repository: $(Repository) - - - task: CmdLine@2 - displayName: 'test ortmodule' - inputs: - script: | - set -ex ; \ - whlfilename=$(ls $(Build.ArtifactStagingDirectory)/Release/dist/*.whl | head -n 1) ; \ - echo $whlfilename ; \ - basefilename=$(basename $whlfilename) ; \ - docker run --rm \ - --gpus all \ - -e NVIDIA_VISIBLE_DEVICES=all \ - --volume $(Build.ArtifactStagingDirectory):/build \ - --volume $(Agent.TempDirectory)/MNIST:/mnist \ - $(Repository) \ - bash -c " $(PythonManylinuxDir)/bin/python3 -m pip install /build/Release/dist/$basefilename && $(PythonManylinuxDir)/bin/python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install " ; - workingDirectory: $(Build.SourcesDirectory) - - - task: CmdLine@2 - displayName: 'Upload wheel' - condition: and(succeeded(), and(eq(variables['UploadWheel'], 'yes'), ne(variables['ORT_DISABLE_PYTHON_PACKAGE_LOCAL_VERSION'], 'true'))) - inputs: - script: | - set -e -x - whlfilename=$(ls $(Build.ArtifactStagingDirectory)/Release/dist/*.whl | head -n 1) ; \ - python3 tools/ci_build/upload_python_package_to_azure_storage.py \ - --python_wheel_path $whlfilename ${{ variables['finalStorage'] }} diff --git a/tools/ci_build/github/linux/docker/scripts/install_os_deps.sh b/tools/ci_build/github/linux/docker/scripts/install_os_deps.sh index 7f3160371aa24..87b9b960b7ebc 100755 --- a/tools/ci_build/github/linux/docker/scripts/install_os_deps.sh +++ b/tools/ci_build/github/linux/docker/scripts/install_os_deps.sh @@ -12,7 +12,6 @@ d) DEVICE_TYPE=${OPTARG};; v) echo "Cuda version is no longer accepted as an input to this script. Ignoring the input argument -v.";; t) echo "Installing python training dependencies argument is no longer accepted as an input to this script. Ignoring the input argument -t.";; m) INSTALL_DEPS_DISTRIBUTED_SETUP=true;; -u) echo "Installing ortmodule python dependencies argument is no longer accepted as an input to this script. Ignoring the input argument -u.";; r) echo "Installing ROCM python dependencies argument is no longer accepted as an input to this script. Ignoring the input argument -r.";; esac done diff --git a/tools/ci_build/github/linux/docker/scripts/install_python_deps.sh b/tools/ci_build/github/linux/docker/scripts/install_python_deps.sh index 1ac1d226deec6..2d7acd1f701ff 100755 --- a/tools/ci_build/github/linux/docker/scripts/install_python_deps.sh +++ b/tools/ci_build/github/linux/docker/scripts/install_python_deps.sh @@ -3,7 +3,6 @@ set -e -x INSTALL_DEPS_TRAINING=false INSTALL_DEPS_DISTRIBUTED_SETUP=false -ORTMODULE_BUILD=false TARGET_ROCM=false CU_VER="11.8" TORCH_VERSION='2.0.0' @@ -18,7 +17,6 @@ d) DEVICE_TYPE=${OPTARG};; v) CU_VER=${OPTARG};; t) INSTALL_DEPS_TRAINING=true;; m) INSTALL_DEPS_DISTRIBUTED_SETUP=true;; -u) ORTMODULE_BUILD=true;; r) TARGET_ROCM=true;; c) USE_CONDA=true;; esac @@ -55,17 +53,3 @@ fi export ONNX_ML=1 export CMAKE_ARGS="-DONNX_GEN_PB_TYPE_STUBS=OFF -DONNX_WERROR=OFF" ${PYTHON_EXE} -m pip install -r ${0/%install_python_deps\.sh/requirements\.txt} -if [ $DEVICE_TYPE = "gpu" ]; then - if [[ $INSTALL_DEPS_TRAINING = true ]]; then - if [[ $ORTMODULE_BUILD = false ]]; then - ${PYTHON_EXE} -m pip install -r ${0/%install_python_deps.sh/training\/requirements.txt} - else - if [[ $TARGET_ROCM = false ]]; then - ${PYTHON_EXE} -m pip install -r ${0/%install_python_deps.sh/training\/ortmodule\/stage1\/requirements_torch${TORCH_VERSION}_cu${CU_VER}\/requirements.txt} - ${PYTHON_EXE} -m pip install -r ${0/%install_python_deps.sh/training\/ortmodule\/stage2\/requirements.txt} - else - ${PYTHON_EXE} -m pip install -r ${0/%install_python_deps.sh/training\/ortmodule\/stage1\/requirements_rocm\/requirements.txt} - fi - fi - fi -fi diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_rocm/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_rocm/requirements.txt deleted file mode 100644 index 051f42dac335d..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_rocm/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -numpy==1.21.6 ; python_version < '3.9' -numpy==2.1.2 ; python_version >= '3.9' diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.0.0_cu11.8/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.0.0_cu11.8/requirements.txt deleted file mode 100644 index b3b2651c8d26d..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.0.0_cu11.8/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ ---pre --f https://download.pytorch.org/whl/torch_stable.html -torch==2.0.0+cu118 -torchvision==0.15.1+cu118 -torchtext==0.15.1 -# TODO(bmeswani): packaging 22.0 removes support for LegacyVersion leading to errors because transformers 4.4.2 uses LegacyVersion -packaging==21.3 -setuptools>=68.2.2 diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.1.0_cu12.2/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.1.0_cu12.2/requirements.txt deleted file mode 100644 index 152a17db90366..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch2.1.0_cu12.2/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ ---pre --f https://download.pytorch.org/whl/torch_stable.html -torch==2.1.0+cu121 -torchvision==0.16.0+cu121 -torchtext==0.16.0 -packaging==23.1 -setuptools>=68.2.2 diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_cpu/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_cpu/requirements.txt deleted file mode 100644 index 846f8c15b257d..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_cpu/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ --f https://download.pytorch.org/whl/torch_stable.html -torch==2.3.0+cpu -setuptools>=68.2.2 diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_nightly/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_nightly/requirements.txt deleted file mode 100644 index 01fa7b0ff956e..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_nightly/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -scikit-learn -packaging==21.3 -transformers==v4.36.0 -accelerate==0.25.0 -wget diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/torch_eager_cpu/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/torch_eager_cpu/requirements.txt deleted file mode 100644 index 6346c54decf9c..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/torch_eager_cpu/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ ---pre --f https://download.pytorch.org/whl/torch_stable.html -torch==2.2.0 -setuptools>=68.2.2 -cerberus -h5py -scikit-learn -numpy==1.21.6 ; python_version < '3.9' -numpy==2.1.2 ; python_version >= '3.9' -pandas -parameterized diff --git a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage2/requirements.txt b/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage2/requirements.txt deleted file mode 100644 index dd86b32f88c76..0000000000000 --- a/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage2/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -pandas -scikit-learn -numpy==1.21.6 ; python_version < '3.9' -numpy==2.1.2 ; python_version >= '3.9' -transformers==v4.36.0 -accelerate==0.25.0 -rsa==4.9 -tensorboard==2.13.0 -h5py -wget -pytorch-lightning==2.3.3 -deepspeed==0.9.0 -fairscale==0.4.6 -parameterized>=0.8.1 -pydantic<2.0.0 diff --git a/tools/ci_build/github/linux/run_dockerbuild.sh b/tools/ci_build/github/linux/run_dockerbuild.sh index 9944861f519f4..6618810c77f6d 100755 --- a/tools/ci_build/github/linux/run_dockerbuild.sh +++ b/tools/ci_build/github/linux/run_dockerbuild.sh @@ -15,10 +15,6 @@ BUILD_DIR=$BUILD_BINARIESDIRECTORY YOCTO_VERSION="4.19" #Training only INSTALL_DEPS_DISTRIBUTED_SETUP=false -#Training only -ORTMODULE_BUILD=false -#Training only -USE_CONDA=false ALLOW_RELEASED_ONNX_OPSET_ONLY_ENV="ALLOW_RELEASED_ONNX_OPSET_ONLY="$ALLOW_RELEASED_ONNX_OPSET_ONLY echo "ALLOW_RELEASED_ONNX_OPSET_ONLY environment variable is set as $ALLOW_RELEASED_ONNX_OPSET_ONLY_ENV" @@ -44,10 +40,6 @@ t) EXTRA_IMAGE_TAG=${OPTARG};; i) IMAGE_CACHE_CONTAINER_REGISTRY_NAME=${OPTARG};; # install distributed setup dependencies m) INSTALL_DEPS_DISTRIBUTED_SETUP=true;; -# install ortmodule specific dependencies -u) ORTMODULE_BUILD=true;; -# install and use conda -e) USE_CONDA=true;; *) echo "Invalid option";; esac done @@ -82,24 +74,6 @@ if [ $BUILD_OS = "yocto" ]; then $GET_DOCKER_IMAGE_CMD --repository "onnxruntime-$IMAGE" \ --docker-build-args="--build-arg TOOL_CHAIN=$TOOL_CHAIN_SCRIPT --build-arg BUILD_USER=onnxruntimedev --build-arg BUILD_UID=$(id -u) --build-arg PYTHON_VERSION=${PYTHON_VER}" \ --dockerfile $DOCKER_FILE --context . -elif [ $BUILD_DEVICE = "gpu" ]; then - # This code path is only for training. Inferecing pipeline uses CentOS - IMAGE="$BUILD_OS-gpu_training" - # Current build script doesn't support building shared lib with Python dependency. To enable building with PythonOp, - # We need to avoid `--no-undefined` when building shared lib (Otherwise, CIs will report `undefined symbols`), but removing that would bring some other concerns. - # Plus the fact training did not need build shared library, we disable the --build_shared_lib for training CIs. - NEED_BUILD_SHARED_LIB=false - INSTALL_DEPS_EXTRA_ARGS="${INSTALL_DEPS_EXTRA_ARGS} -t" - if [[ $INSTALL_DEPS_DISTRIBUTED_SETUP = true ]]; then - INSTALL_DEPS_EXTRA_ARGS="${INSTALL_DEPS_EXTRA_ARGS} -m" - fi - if [[ $ORTMODULE_BUILD = true ]]; then - INSTALL_DEPS_EXTRA_ARGS="${INSTALL_DEPS_EXTRA_ARGS} -u" - fi - INSTALL_DEPS_EXTRA_ARGS="${INSTALL_DEPS_EXTRA_ARGS} -v 11.8" - $GET_DOCKER_IMAGE_CMD --repository "onnxruntime-$IMAGE" \ - --docker-build-args="--build-arg BASEIMAGE=nvcr.io/nvidia/cuda:11.8.0-cudnn8-devel-${BUILD_OS} --build-arg BUILD_USER=onnxruntimedev --build-arg BUILD_UID=$(id -u) --build-arg PYTHON_VERSION=${PYTHON_VER} --build-arg INSTALL_DEPS_EXTRA_ARGS=\"${INSTALL_DEPS_EXTRA_ARGS}\" --build-arg USE_CONDA=${USE_CONDA} --network=host" \ - --dockerfile Dockerfile.ubuntu_gpu_training --context . elif [[ $BUILD_DEVICE = "openvino"* ]]; then BUILD_ARGS="--build-arg BUILD_USER=onnxruntimedev --build-arg BUILD_UID=$(id -u) --build-arg PYTHON_VERSION=${PYTHON_VER} --build-arg OPENVINO_VERSION=${OPENVINO_VERSION} --build-arg UBUNTU_VERSION=${UBUNTU_VERSION}" IMAGE="$BUILD_OS-openvino" diff --git a/tools/ci_build/github/pai/pai_huggingface_bert_large_test.sh b/tools/ci_build/github/pai/pai_huggingface_bert_large_test.sh deleted file mode 100755 index fb4dbeb2e73d3..0000000000000 --- a/tools/ci_build/github/pai/pai_huggingface_bert_large_test.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash - -set -ex - -usage() { echo "Usage: $0 [-v ]" 1>&2; exit 1; } - -while getopts "v:" parameter_Option -do case "${parameter_Option}" -in -v) ROCM_VERSION=${OPTARG};; -*) usage ;; -esac -done - -MI200_DEVICE_NUMBERS=$(rocm-smi --showproductname | grep -c "MI250" | xargs) - -if [ "$MI200_DEVICE_NUMBERS" -gt "0" ]; then - RESULT_FILE=ci-mi200.huggingface.bert-large-rocm${ROCM_VERSION}.json -else - RESULT_FILE=ci-mi100.huggingface.bert-large-rocm${ROCM_VERSION}.json -fi - -python \ - /stage/huggingface-transformers/examples/pytorch/language-modeling/run_mlm.py \ - --model_name_or_path bert-large-uncased \ - --dataset_name wikitext \ - --dataset_config_name wikitext-2-raw-v1 \ - --do_train \ - --max_steps 260 \ - --logging_steps 20 \ - --output_dir ./test-mlm-bbu \ - --overwrite_output_dir \ - --per_device_train_batch_size 8 \ - --fp16 \ - --dataloader_num_workers 1 \ - --ort \ - --skip_memory_metrics - -cat ci-pipeline-actual.json - -python /onnxruntime_src/orttraining/tools/ci_test/compare_huggingface.py \ - ci-pipeline-actual.json \ - /onnxruntime_src/orttraining/tools/ci_test/results/"$RESULT_FILE" diff --git a/tools/ci_build/set-trigger-rules.py b/tools/ci_build/set-trigger-rules.py index 0e9cd514d8aa5..b46d1e2559e46 100644 --- a/tools/ci_build/set-trigger-rules.py +++ b/tools/ci_build/set-trigger-rules.py @@ -30,14 +30,9 @@ "mac-ios-ci-pipeline.yml", "mac-ios-packaging-pipeline.yml", "mac-react-native-ci-pipeline.yml", - "orttraining-linux-ci-pipeline.yml", - "orttraining-linux-gpu-ci-pipeline.yml", - "orttraining-linux-gpu-ortmodule-distributed-test-ci-pipeline.yml", - "orttraining-mac-ci-pipeline.yml", "win-ci-pipeline.yml", "win-gpu-dml-ci-pipeline.yml", "win-gpu-cuda-ci-pipeline.yml", - "win-gpu-training-ci-pipeline.yml", "win-gpu-doc-gen-ci-pipeline.yml", "win-gpu-tensorrt-ci-pipeline.yml", "win-gpu-webgpu-ci-pipeline.yml", diff --git a/tools/nuget/generate_nuspec_for_native_nuget.py b/tools/nuget/generate_nuspec_for_native_nuget.py index ff6556b1fd31a..ba125f4e2d980 100644 --- a/tools/nuget/generate_nuspec_for_native_nuget.py +++ b/tools/nuget/generate_nuspec_for_native_nuget.py @@ -138,7 +138,7 @@ def parse_arguments(): required=False, default="None", type=str, - choices=["cuda", "dnnl", "openvino", "tensorrt", "snpe", "tvm", "qnn", "None"], + choices=["cuda", "dnnl", "openvino", "tensorrt", "snpe", "qnn", "None"], help="The selected execution provider for this build.", ) parser.add_argument("--sdk_info", required=False, default="", type=str, help="dependency SDK information.") @@ -375,13 +375,11 @@ def generate_files(line_list, args): "mklml": "mklml.dll", "openmp": "libiomp5md.dll", "dnnl": "dnnl.dll", - "tvm": "tvm.dll", "providers_shared_lib": "onnxruntime_providers_shared.dll", "dnnl_ep_shared_lib": "onnxruntime_providers_dnnl.dll", "tensorrt_ep_shared_lib": "onnxruntime_providers_tensorrt.dll", "openvino_ep_shared_lib": "onnxruntime_providers_openvino.dll", "cuda_ep_shared_lib": "onnxruntime_providers_cuda.dll", - "tvm_ep_shared_lib": "onnxruntime_providers_tvm.lib", "onnxruntime_perf_test": "onnxruntime_perf_test.exe", "onnx_test_runner": "onnx_test_runner.exe", } @@ -394,7 +392,6 @@ def generate_files(line_list, args): "mklml_1": "libmklml_gnu.so", "openmp": "libiomp5.so", "dnnl": "libdnnl.so.1", - "tvm": "libtvm.so.0.5.1", "providers_shared_lib": "libonnxruntime_providers_shared.so", "dnnl_ep_shared_lib": "libonnxruntime_providers_dnnl.so", "tensorrt_ep_shared_lib": "libonnxruntime_providers_tensorrt.so", @@ -456,14 +453,6 @@ def generate_files(line_list, args): + '" target="build\\native\\include" />' ) - if args.execution_provider == "tvm": - files_list.append( - "' - ) - if args.execution_provider == "openvino": files_list.append( "' ) - if args.execution_provider == "tvm": - files_list.append( - "' - ) - files_list.append( - "' - ) - - tvm_build_path = os.path.join(args.ort_build_path, args.build_config, "_deps", "tvm-build") - if is_windows(): - files_list.append( - "' - ) - else: - # TODO(agladyshev): Add support for Linux. - raise RuntimeError("Now only Windows is supported for TVM EP.") - if args.execution_provider == "rocm" or is_rocm_gpu_package and not is_ado_packaging_build: files_list.append( "" ) - # Process tvm dependency - if os.path.exists(os.path.join(args.native_build_path, nuget_dependencies["tvm"])): - files_list.append( - "" - ) - # Some tools to be packaged in nightly debug build only, should not be released # These are copied to the runtimes folder for convenience of loading with the dlls # NOTE: nuget gives a spurious error on linux if these aren't in a separate directory to the library so diff --git a/tools/scripts/python_test.sh b/tools/scripts/python_test.sh index d12f6e6d33772..53d350cf30611 100755 --- a/tools/scripts/python_test.sh +++ b/tools/scripts/python_test.sh @@ -13,9 +13,6 @@ echo Install Python Deps cp $src_dir/tools/ci_build/github/linux/docker/scripts/manylinux/requirements.txt $build_dir/requirements.txt python3 -m pip install -r $build_dir/requirements.txt -mkdir -p $build_dir/requirements_torch_cpu/ -cp $src_dir/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch_cpu/requirements.txt $build_dir/requirements_torch_cpu/requirements.txt -python3 -m pip install -r $build_dir/requirements_torch_cpu/requirements.txt python3 -m pip list | grep onnx echo Install $config python package @@ -23,6 +20,5 @@ rm -rf $build_dir/$config/onnxruntime $build_dir/$config/pybind11 python3 -m pip install $build_dir/$config/dist/*.whl echo Run $config unit tests -pushd $build_dir/$config/ -python3 $src_dir/tools/ci_build/build.py --build_dir $build_dir --cmake_generator Ninja --config $config --test --skip_submodule_sync --build_shared_lib --parallel --use_binskim_compliant_compile_flags --build_wheel --enable_onnx_tests --enable_transformers_tool_test --ctest_path "" -popd +cd $build_dir/$config/ +python3 $src_dir/tools/ci_build/build.py --build_dir $build_dir --cmake_generator Ninja --config $config --test --skip_submodule_sync --build_shared_lib --parallel --use_binskim_compliant_compile_flags --build_wheel --enable_onnx_tests --enable_transformers_tool_test