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GeezerVison is showing github.com — Fuser/python/python_direct/enum.cpp at main · NVIDIA/Fuser Expand file tree/ Copy pathenum.cppMore file actions 130 lines (117 loc) · 5.4 KB / Copy pathenum.cppFile metadata and controls130 lines (117 loc) · 5.4 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 // clang-format off /* * SPDX-FileCopyrightText: Copyright (c) 2025-present NVIDIA CORPORATION & AFFILIATES. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause */ // clang-format on #include <bindings.h> #include <multidevice/communicator.h> #include <scheduler/matmul_heuristic.h> #include <scheduler/scheduler_types.h> #include <type.h> namespace nvfuser::python { void bindEnums(py::module& nvfuser) { //! DataTypes supported by nvFuser in the FusionDefinition. The python //! DataType maps to the CPP PrimDataType. On the CPP side, there is also a //! DateType enum that includes struct, array, pointer, or opaque datatypes. py::enum_<PrimDataType>(nvfuser, "DataType", py::module_local()) .value("Double", DataType::Double) .value("Float", DataType::Float) .value("Half", DataType::Half) .value("Int", DataType::Int) .value("Int32", DataType::Int32) .value("UInt64", DataType::UInt64) .value("Index", DataType::Index) .value("Bool", DataType::Bool) .value("BFloat16", DataType::BFloat16) .value("Float8_e4m3fn", DataType::Float8_e4m3fn) .value("Float8_e5m2", DataType::Float8_e5m2) .value("Float8_e8m0fnu", DataType::Float8_e8m0fnu) .value("Float4_e2m1fn", DataType::Float4_e2m1fn) .value("Float4_e2m1fn_x2", DataType::Float4_e2m1fn_x2) .value("ComplexFloat", DataType::ComplexFloat) .value("ComplexDouble", DataType::ComplexDouble) .value("Null", DataType::Null); py::enum_<ParallelType>(nvfuser, "ParallelType", py::module_local()) .value("mesh_x", ParallelType::DIDx) .value("mesh_y", ParallelType::DIDy) .value("mesh_z", ParallelType::DIDz) .value("grid_x", ParallelType::BIDx) .value("grid_y", ParallelType::BIDy) .value("grid_z", ParallelType::BIDz) .value("block_x", ParallelType::TIDx) .value("block_y", ParallelType::TIDy) .value("block_z", ParallelType::TIDz) .value("mma", ParallelType::Mma) .value("serial", ParallelType::Serial) .value("tma", ParallelType::Bulk) .value("unroll", ParallelType::Unroll) .value("unswitch", ParallelType::Unswitch) .value("vectorize", ParallelType::Vectorize) .value("stream", ParallelType::Stream); py::enum_<CommunicatorBackend>( nvfuser, "CommunicatorBackend", py::module_local()) .value("nccl", CommunicatorBackend::kNccl) .value("ucc", CommunicatorBackend::kUcc) .value("cuda", CommunicatorBackend::kCuda); py::enum_<SchedulerType>(nvfuser, "SchedulerType", py::module_local()) .value("none", SchedulerType::None) .value("no_op", SchedulerType::NoOp) .value("pointwise", SchedulerType::PointWise) .value("matmul", SchedulerType::Matmul) .value("reduction", SchedulerType::Reduction) .value("inner_persistent", SchedulerType::InnerPersistent) .value("inner_outer_persistent", SchedulerType::InnerOuterPersistent) .value("outer_persistent", SchedulerType::OuterPersistent) .value("transpose", SchedulerType::Transpose) .value("expr_eval", SchedulerType::ExprEval) .value("resize", SchedulerType::Resize); py::enum_<LoadStoreOpType>(nvfuser, "LoadStoreOpType", py::module_local()) .value("set", LoadStoreOpType::Set) .value("load_matrix", LoadStoreOpType::LdMatrix) .value("cp_async", LoadStoreOpType::CpAsync) .value("tma", LoadStoreOpType::CpAsyncBulkTensorTile); py::enum_<MemoryType>(nvfuser, "MemoryType", py::module_local()) .value("tensor", MemoryType::Tensor) .value("local", MemoryType::Local) .value("shared", MemoryType::Shared) .value("global", MemoryType::Global) .value("symmetric", MemoryType::Symmetric); py::enum_<CacheOp>(nvfuser, "CacheOp", py::module_local()) .value("unspecified", CacheOp::Unspecified) .value("all_levels", CacheOp::AllLevels) .value("streaming", CacheOp::Streaming) .value("global", CacheOp::Global); py::enum_<IdMappingMode>(nvfuser, "IdMappingMode") .value("exact", IdMappingMode::EXACT) .value("almost_exact", IdMappingMode::ALMOSTEXACT) .value("broadcast", IdMappingMode::BROADCAST) .value("permissive", IdMappingMode::PERMISSIVE) .value("loop", IdMappingMode::LOOP); py::enum_<MatmulParams::TilingStrategy> tiling_strategy( nvfuser, "MatmulTilingStrategy", py::module_local()); tiling_strategy.value( "one_tile_per_cta", MatmulParams::TilingStrategy::OneTilePerCTA); tiling_strategy.value( "distribute_tiles_across_sms", MatmulParams::TilingStrategy::DistributeTilesAcrossSMs); tiling_strategy.value( "distribute_stages_across_sms", MatmulParams::TilingStrategy::DistributeStagesAcrossSMs); py::enum_<MatmulParams::BufferingLoopLevel> buffering_loop_level( nvfuser, "MatmulBufferingLoopLevel", py::module_local()); buffering_loop_level.value( "cta_tiles", MatmulParams::BufferingLoopLevel::CTATiles); buffering_loop_level.value( "warp_tiles", MatmulParams::BufferingLoopLevel::WarpTiles); py::enum_<MatmulParams::CircularBufferingStrategy> circular_buffering_strategy( nvfuser, "MatmulCircularBufferingStrategy", py::module_local()); circular_buffering_strategy.value( "pipelined", MatmulParams::CircularBufferingStrategy::Pipelined); circular_buffering_strategy.value( "warp_specialized", MatmulParams::CircularBufferingStrategy::WarpSpecialized); } } // namespace nvfuser::python |
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