diff --git a/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp b/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp index 63d2d3f0e9..7dd15c8a1e 100644 --- a/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp +++ b/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp @@ -9,8 +9,8 @@ //===----------------------------------------------------------------------===// #include "src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.hpp" - #include "mlir/Dialect/Traits.h" +#include "mlir/IR/Threading.h" #include "llvm/ADT/STLExtras.h" #include "src/Dialect/ONNX/ElementsAttr/DisposableElementsAttr.hpp" @@ -849,6 +849,8 @@ ElementsAttr ElementsAttrBuilder::reduce(ElementsAttr elms, if (axes.empty()) return elms; + Type elementType = elms.getElementType(); + MLIRContext *ctx = elementType.getContext(); SmallVector sortedAxes(axes); std::sort(sortedAxes.begin(), sortedAxes.end()); assert( @@ -885,22 +887,74 @@ ElementsAttr ElementsAttrBuilder::reduce(ElementsAttr elms, ShapedType reducedType = type.clone(reducedShape); return fromWideNums(reducedType, [&](MutableArrayRef dstNums) { - // Traverse and populate each element d in dstNums. - for (auto &idxoffs : StridesRange<1>(reducedShape, {reducedStrides})) { - WideNum &d = dstNums[idxoffs.flattenedIndex]; - int64_t srcPos = idxoffs[0]; - // Traverse all the elements that reduce together into d. - // srcNums elements may be repeated if there are zeros in axesStrides. - StridesRange<1> axesRange(axesShape, {axesStrides}); - auto axesIter = axesRange.begin(); - auto axesEnd = axesRange.end(); - assert(axesIter->at(0) == 0 && "initial src offset must be zero"); - d = srcNums.get()[srcPos]; - while (++axesIter != axesEnd) { - int64_t srcOffset = axesIter->at(0); - d = reducer(d, srcNums.get()[srcPos + srcOffset]); + StridesRange<1> sRange(reducedShape, {reducedStrides}); + StridesRange<1> axesRange(axesShape, {axesStrides}); + SmallVector, 4> batch; + for (auto &idxoffs : sRange) + batch.emplace_back(std::make_pair(idxoffs.flattenedIndex, idxoffs[0])); + + auto fetchBatch = [&](size_t threadNumber, bool parallel) { + // retrun all data without spliting for sequential execution. + if (!parallel) + return llvm::make_range(batch.begin(), batch.end()); + // Each thread fetches the same batch size. The leftovers are set in the + // threads with small thread number. + size_t tileSize = floor(batch.size() / ctx->getNumThreads()); + size_t leftovers = batch.size() % ctx->getNumThreads(); + int beginOffset; + if (threadNumber < leftovers) { + // for the first few threads, it is as if the block size is larger by 1. + tileSize++; + beginOffset = threadNumber * tileSize; + } else { + // for the last threads, its as we shift the start by leftovers. + beginOffset = threadNumber * tileSize + leftovers; } - } + int endOffset = beginOffset + tileSize; + return llvm::make_range( + batch.begin() + beginOffset, batch.begin() + endOffset); + }; + + auto work = [&](size_t threadNumber, bool parallel = true) { + auto tile = fetchBatch(threadNumber, parallel); + // Traverse and populate each element d in dstNums. + for (auto b : tile) { + WideNum &d = dstNums[b.first]; + int64_t srcPos = b.second; + // Traverse all the elements that reduce together into d. + // srcNums elements may be repeated if there are zeros in axesStrides. + auto axesIter = axesRange.begin(); + auto axesEnd = axesRange.end(); + assert(axesIter->at(0) == 0 && "initial src offset must be zero"); + d = srcNums.get()[srcPos]; + while (++axesIter != axesEnd) { + int64_t srcOffset = axesIter->at(0); + d = reducer(d, srcNums.get()[srcPos + srcOffset]); + } + } + }; + // Using 'parallelFor()' introduces large overhead. Followings are actual + // measurement results on IBM z16 to decide the 'minCount'. We measured + // 'onnx.ReduceSum()' in 'test/mlir/onnx/onnx_constprop_parallel.mlir' using + // several input size. From these results, we decided to use 2000 as the + // 'minCount'. + // + // inputCounts|Sequential | Parallel with 2 threads + // | (work()) | (parallelFor()) + // | (msec) | (msec) + // -------------------------------------------------- + // 400 | 0.065 | 0.153 + // 800 | 0.115 | 0.164 + // 1200 | 0.175 | 0.201 + // 1600 | 0.226 | 0.228 + // 2000 | 0.282 | 0.258 + // 2400 | 0.336 | 0.284 + constexpr size_t minCount = 2000; + size_t inputCount = batch.size() * axesRange.size(); + if (inputCount < minCount) + work(0, /*parallel*/ false); + else + parallelFor(ctx, 0, ctx->getNumThreads(), work); }); } diff --git a/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.hpp b/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.hpp index f7276b6ebb..6242da6139 100644 --- a/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.hpp +++ b/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.hpp @@ -10,6 +10,7 @@ #ifndef ONNX_MLIR_ELEM_ATTR_BUILDER_H #define ONNX_MLIR_ELEM_ATTR_BUILDER_H +#include "mlir/IR/Threading.h" #include "src/Dialect/ONNX/ElementsAttr/BType.hpp" #include "src/Dialect/ONNX/ElementsAttr/DisposableElementsAttr.hpp" @@ -244,10 +245,46 @@ class ElementsAttrBuilder { // Constructs a transformer that changes every element to the result of // applying the given function to the element. template - static inline Transformer functionTransformer(Function fun) { - return [fun = std::move(fun)](llvm::MutableArrayRef data) -> void { - for (WideNum &n : data) - n = fun(n); + inline Transformer functionTransformer(Function fun) { + mlir::MLIRContext *ctx = disposablePool.getContext(); + return [fun = std::move(fun), ctx]( + llvm::MutableArrayRef data) -> void { + auto fetchBatch = [&](size_t threadNumber, bool parallel) { + // retrun all data without spliting for sequential execution. + if (!parallel) + return llvm::make_range(data.begin(), data.end()); + // Each thread fetches the same data size. The leftovers are set in the + // threads with small thread number. + size_t tileSize = floor(data.size() / ctx->getNumThreads()); + size_t leftovers = data.size() % ctx->getNumThreads(); + int beginOffset; + if (threadNumber < leftovers) { + // for the first few threads, it is as if the block size is larger + // by 1. + tileSize++; + beginOffset = threadNumber * tileSize; + } else { + // for the last threads, its as we shift the start by leftovers. + beginOffset = threadNumber * tileSize + leftovers; + } + int endOffset = beginOffset + tileSize; + return llvm::make_range( + data.begin() + beginOffset, data.begin() + endOffset); + }; + + auto work = [&](size_t threadNumber, bool parallel = true) { + auto tile = fetchBatch(threadNumber, parallel); + for (WideNum &n : tile) + n = fun(n); + }; + // Using 'parallelFor()' introduces large overhead. + // To avoid this overhead, call work() directry if input size is less than + // `minCount`. + constexpr size_t minCount = 1000; + if (data.size() < minCount) + work(0, /*parallel*/ false); + else + parallelFor(ctx, 0, ctx->getNumThreads(), work); }; } diff --git a/test/mlir/onnx/onnx_constprop_parallel.mlir b/test/mlir/onnx/onnx_constprop_parallel.mlir new file mode 100644 index 0000000000..41f16cba60 --- /dev/null +++ b/test/mlir/onnx/onnx_constprop_parallel.mlir @@ -0,0 +1,30 @@ +// RUN: onnx-mlir-opt -j 4 --shape-inference --constprop-onnx %s -split-input-file | FileCheck %s + +//===----------------------------------------------------------------------===// +/// Reduce constant prop tests for parallel + +func.func @test_reduce_parallel() -> (tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>) { + %0 = "onnx.Constant"() {value = dense<[ + [ 0, 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, 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931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999] +]> : tensor<4x1000xi32>} : () -> tensor<4x1000xi32> + %1 = "onnx.Constant"() {value = dense<0> : tensor} : () -> tensor + %2 = "onnx.ReduceSum"(%0, %1) : (tensor<4x1000xi32>, tensor) -> tensor<1x1000xi32> + %3 = "onnx.ReduceProd"(%0, %1) : (tensor<4x1000xi32>, tensor) -> tensor<1x1000xi32> + %4 = "onnx.ReduceMin"(%0, %1) : (tensor<4x1000xi32>, tensor) -> tensor<1x1000xi32> + %5 = "onnx.ReduceMax"(%0, %1) : (tensor<4x1000xi32>, tensor) -> tensor<1x1000xi32> + %6 = "onnx.ReduceMean"(%0, %1) : (tensor<4x1000xi32>, tensor) -> tensor<1x1000xi32> + "onnx.Return"(%2, %3, %4, %5, %6) : (tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>) -> () +} + +// CHECK-LABEL: func.func @test_reduce_parallel +// CHECK-SAME: () -> (tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>) { +// CHECK-DAG: [[VAR_0_:%.+]] = onnx.Constant 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: tensor<1x1000xi32> +// CHECK-DAG: [[VAR_1_:%.+]] = onnx.Constant 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: tensor<1x1000xi32> +// CHECK-DAG: [[VAR_2_:%.+]] = onnx.Constant 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: tensor<1x1000xi32> +// CHECK-DAG: [[VAR_3_:%.+]] = onnx.Constant 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: tensor<1x1000xi32> +// CHECK-DAG: [[VAR_4_:%.+]] = onnx.Constant 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: tensor<1x1000xi32> +// CHECK: onnx.Return [[VAR_0_]], [[VAR_1_]], [[VAR_2_]], [[VAR_3_]], [[VAR_4_]] : tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32>, tensor<1x1000xi32> +// CHECK: }