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Co-authored-by: Zihao Ye <[email protected]>
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| cmake_minimum_required(VERSION 3.5) | ||
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| project(featgraph C CXX) | ||
| message(STATUS "Start configuring project ${PROJECT_NAME}") | ||
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| # Find CUDA | ||
| include(../cmake/util/FindCUDA.cmake) | ||
| find_cuda(ON) | ||
| message(STATUS "${CUDA_INCLUDE_DIRS}") | ||
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| add_custom_target( | ||
| featgraph_kernel | ||
| COMMAND python ${CMAKE_CURRENT_SOURCE_DIR}/pack_featgraph.py | ||
| COMMENT "Creating featgraph kernels..." | ||
| ) | ||
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| set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O2 -fPIC") | ||
| file(GLOB FEATGRAPH_SRC | ||
| src/featgraph.cc | ||
| src/tvm_runtime_pack.cc | ||
| ) | ||
| add_library(featgraph_runtime SHARED ${FEATGRAPH_SRC}) | ||
| target_include_directories(featgraph_runtime PRIVATE ${CUDA_INCLUDE_DIRS}) | ||
| target_include_directories(featgraph_runtime PRIVATE "./include") | ||
| target_include_directories(featgraph_runtime PRIVATE "../third_party/tvm/include") | ||
| target_include_directories(featgraph_runtime PRIVATE "../third_party/tvm/3rdparty/dmlc-core/include") | ||
| target_include_directories(featgraph_runtime PRIVATE "../third_party/tvm/3rdparty/dlpack/include") | ||
| target_link_libraries(featgraph_runtime "dl" # dynamic linking | ||
| ${CUDA_CUDART_LIBRARY} | ||
| ${CUDA_CUDA_LIBRARY} | ||
| ${CUDA_NVRTC_LIBRARY}) | ||
| add_dependencies(featgraph_runtime featgraph_kernel) | ||
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| install(TARGETS featgraph_runtime LIBRARY DESTINATION lib) |
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| # FeatGraph-DGL | ||
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| FeatGraph is an efficient backend for Graph Neural Networks based on TVM. | ||
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| - Original repo: https://github.com/amazon-research/FeatGraph | ||
| - SC2020 Paper: https://www.csl.cornell.edu/~zhiruz/pdfs/featgraph-sc2020.pdf | ||
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| This folder contains the code for integrating featgraph kernels to DGL. | ||
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| ## Usage | ||
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| After building DGL with `USE_TVM=ON`, you should be able to run: | ||
| ```bash | ||
| python test.py | ||
| ``` | ||
| to verify correctness. | ||
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| ## Reference | ||
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| - [TVM Tutorial on Deploy TVM Module using C++ API](https://tvm.apache.org/docs/deploy/cpp_deploy.html). | ||
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| /*! | ||
| * Copyright (c) 2020 by Contributors | ||
| * \file featgraph/include/featgraph.h | ||
| * \brief FeatGraph kernel headers. | ||
| */ | ||
| #ifndef FEATGRAPH_H_ | ||
| #define FEATGRAPH_H_ | ||
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| #include <dlpack/dlpack.h> | ||
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| namespace dgl { | ||
| namespace featgraph { | ||
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| /* \brief Load Featgraph module from given path. */ | ||
| void LoadFeatGraphModule(const std::string& path); | ||
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| /* \brief Call Featgraph's SDDMM kernel. */ | ||
| void SDDMMTreeReduction(DLManagedTensor* row, DLManagedTensor* col, | ||
| DLManagedTensor* lhs, DLManagedTensor* rhs, | ||
| DLManagedTensor* out); | ||
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| } // namespace featgraph | ||
| } // namespace dgl | ||
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| #endif // FEATGRAPH_H_ |
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| """ Export featgraph kernels to a shared library. """ | ||
| import tvm | ||
| from sddmm import sddmm_tree_reduction_gpu | ||
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| def get_sddmm_kernels_gpu(idtypes, dtypes): | ||
| """ | ||
| Parameters | ||
| ---------- | ||
| idtypes: List[str] | ||
| Possible index types. | ||
| dtypes: List[str] | ||
| Possible data types. | ||
| Returns | ||
| ------- | ||
| List[IRModule]: | ||
| The list of IRModules. | ||
| """ | ||
| ret = [] | ||
| # SDDMM Tree Reduction | ||
| for dtype in dtypes: | ||
| for idtype in idtypes: | ||
| ret.append(sddmm_tree_reduction_gpu(idtype, dtype)) | ||
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| return ret | ||
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| if __name__ == '__main__': | ||
| binary_path = 'libfeatgraph_kernels.so' | ||
| kernels = [] | ||
| idtypes = ['int32', 'int64'] | ||
| dtypes = ['float16', 'float64', 'float32', 'int32', 'int64'] | ||
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| kernels += get_sddmm_kernels_gpu(idtypes, dtypes) | ||
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| # build kernels and export the module to libfeatgraph_kernels.so | ||
| module = tvm.build(kernels, target='cuda', target_host='llvm') | ||
| module.export_library(binary_path) | ||
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|
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| """ The compute function and schedules for SDDMM kernels written in TVM. """ | ||
| import tvm | ||
| from tvm import te | ||
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| def sddmm_tree_reduction_gpu(idx_type, feat_type): | ||
| """ SDDMM kernels on GPU optimized with Tree Reduction. | ||
| Parameters | ||
| ---------- | ||
| idx_type : str | ||
| The data type for indexing tensors. | ||
| feat_type : str | ||
| The data type of feature tensor. | ||
| Returns | ||
| ------- | ||
| IRModule | ||
| The result IRModule. | ||
| """ | ||
| # define vars and placeholders | ||
| nnz = te.var('nnz', idx_type) | ||
| num_rows = te.var('num_rows', idx_type) | ||
| num_cols = te.var('num_cols', idx_type) | ||
| H = te.var('num_heads', idx_type) | ||
| D = te.var('feat_len', idx_type) | ||
| row = te.placeholder((nnz,), idx_type, 'row') | ||
| col = te.placeholder((nnz,), idx_type, 'col') | ||
| ufeat = te.placeholder((num_rows, H, D), feat_type, 'ufeat') | ||
| vfeat = te.placeholder((num_cols, H, D), feat_type, 'vfeat') | ||
| # define edge computation function | ||
| def edge_func(eid, h, i): | ||
| k = te.reduce_axis((0, D), name='k') | ||
| return te.sum(ufeat[row[eid], h, k] * vfeat[col[eid], h, k], axis=k) | ||
| out = te.compute((nnz, H, tvm.tir.IntImm(idx_type, 1)), edge_func, name='out') | ||
| # define schedules | ||
| sched = te.create_schedule(out.op) | ||
| edge_axis, head_axis, _ = out.op.axis | ||
| reduce_axis = out.op.reduce_axis[0] | ||
| _, red_inner = sched[out].split(reduce_axis, factor=32) | ||
| edge_outer, edge_inner = sched[out].split(edge_axis, factor=32) | ||
| sched[out].bind(red_inner, te.thread_axis('threadIdx.x')) | ||
| sched[out].bind(edge_inner, te.thread_axis('threadIdx.y')) | ||
| sched[out].bind(edge_outer, te.thread_axis('blockIdx.x')) | ||
| sched[out].bind(head_axis, te.thread_axis('blockIdx.y')) | ||
| return tvm.lower(sched, [row, col, ufeat, vfeat, out], | ||
| name='SDDMMTreeReduction_{}_{}'.format(idx_type, feat_type)) | ||
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| if __name__ == '__main__': | ||
| kernel0 = sddmm_tree_reduction_gpu('int32', 'float32') | ||
| print(kernel0) | ||
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| /*! | ||
| * Copyright (c) 2020 by Contributors | ||
| * \file featgraph/src/featgraph.cc | ||
| * \brief FeatGraph kernels. | ||
| */ | ||
| #include <tvm/runtime/module.h> | ||
| #include <tvm/runtime/packed_func.h> | ||
| #include <tvm/runtime/registry.h> | ||
| #include <dmlc/logging.h> | ||
| #include <featgraph.h> | ||
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| namespace dgl { | ||
| namespace featgraph { | ||
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| /* \brief Singleton that loads the featgraph module. */ | ||
| class FeatGraphModule { | ||
| public: | ||
| static FeatGraphModule* Global() { | ||
| static FeatGraphModule inst; | ||
| return &inst; | ||
| } | ||
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| void Load(const std::string& path) { | ||
| mod = tvm::runtime::Module::LoadFromFile(path); | ||
| } | ||
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| inline tvm::runtime::ModuleNode* Get() { | ||
| auto ret = mod.operator->(); | ||
| if (!ret) { | ||
| LOG(FATAL) << "FeatGraph module have not been loaded. " | ||
| << "Please set path of featgraph shared library."; | ||
| } | ||
| return ret; | ||
| } | ||
| private: | ||
| tvm::runtime::Module mod; | ||
| FeatGraphModule() {} | ||
| }; | ||
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| /* \brief Load Featgraph module from given path. */ | ||
| void LoadFeatGraphModule(const std::string& path) { | ||
| FeatGraphModule::Global()->Load(path); | ||
| } | ||
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| /* \brief Convert DLDataType to string. */ | ||
| inline std::string DTypeAsStr(const DLDataType& t) { | ||
| switch(t.code) { | ||
| case 0U: return "int" + std::to_string(t.bits); | ||
| case 1U: return "uint" + std::to_string(t.bits); | ||
| case 2U: return "float" + std::to_string(t.bits); | ||
| case 3U: return "bfloat" + std::to_string(t.bits); | ||
| default: LOG(FATAL) << "Type code " << t.code << " not recognized"; | ||
| } | ||
| } | ||
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| /* \brief Get operator filename. */ | ||
| inline std::string GetOperatorName( | ||
| const std::string& base_name, | ||
| const DLDataType& dtype, | ||
| const DLDataType& idtype) { | ||
| return base_name + "_" + DTypeAsStr(dtype) + "_" + DTypeAsStr(idtype); | ||
| } | ||
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| /* \brief Call FeatGraph's SDDMM kernel. */ | ||
| void SDDMMTreeReduction(DLManagedTensor* row, DLManagedTensor* col, | ||
| DLManagedTensor* lhs, DLManagedTensor* rhs, | ||
| DLManagedTensor* out) { | ||
| tvm::runtime::ModuleNode* mod = FeatGraphModule::Global()->Get(); | ||
| std::string f_name = GetOperatorName("SDDMMTreeReduction", | ||
| (row->dl_tensor).dtype, | ||
| (lhs->dl_tensor).dtype); | ||
| tvm::runtime::PackedFunc f = mod->GetFunction(f_name); | ||
| if (f != nullptr) | ||
| f(row, col, lhs, rhs, out); | ||
| } | ||
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| } // namespace featgraph | ||
| } // namespace dgl |
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