diff --git a/flang/lib/Lower/ConvertConstant.cpp b/flang/lib/Lower/ConvertConstant.cpp --- a/flang/lib/Lower/ConvertConstant.cpp +++ b/flang/lib/Lower/ConvertConstant.cpp @@ -20,6 +20,8 @@ #include "flang/Optimizer/Builder/Complex.h" #include "flang/Optimizer/Builder/Todo.h" +#include + /// Convert string, \p s, to an APFloat value. Recognize and handle Inf and /// NaN strings as well. \p s is assumed to not contain any spaces. static llvm::APFloat consAPFloat(const llvm::fltSemantics &fsem, @@ -48,7 +50,14 @@ const Fortran::evaluate::Scalar> &value, mlir::Type type) { if constexpr (TC == Fortran::common::TypeCategory::Integer) { - return builder.getIntegerAttr(type, value.ToInt64()); + if constexpr (KIND <= 8) + return builder.getIntegerAttr(type, value.ToInt64()); + else { + static_assert(KIND <= 16, "integers with KIND > 16 are not supported"); + return builder.getIntegerAttr( + type, llvm::APInt(KIND * 8, + {value.ToUInt64(), value.SHIFTR(64).ToUInt64()})); + } } else if constexpr (TC == Fortran::common::TypeCategory::Logical) { return builder.getIntegerAttr(type, value.IsTrue()); } else { @@ -66,17 +75,12 @@ /// Helper class to lower an array constant to a global with an MLIR dense /// attribute. /// -/// If we have a rank-1 array of integer, real, or logical, then we can +/// If we have an array of integer, real, or logical, then we can /// create a global array with the dense attribute. /// /// The mlir tensor type can only handle integer, real, or logical. It /// does not currently support nested structures which is required for /// complex. -/// -/// Also, we currently handle just rank-1 since tensor type assumes -/// row major array ordering. We will need to reorder the dimensions -/// in the tensor type to support Fortran's column major array ordering. -/// How to create this tensor type is to be determined. class DenseGlobalBuilder { public: static fir::GlobalOp tryCreating(fir::FirOpBuilder &builder, @@ -124,8 +128,6 @@ &constant) { static_assert(TC != Fortran::common::TypeCategory::Character, "must be numerical or logical"); - if (constant.Rank() != 1) - return; auto attrTc = TC == Fortran::common::TypeCategory::Logical ? Fortran::common::TypeCategory::Integer : TC; @@ -158,12 +160,16 @@ llvm::StringRef globalName, mlir::StringAttr linkage, bool isConst) const { - // Not a rank 1 "trivial" intrinsic constant array, or empty array. + // Not a "trivial" intrinsic constant array, or empty array. if (!attributeElementType || attributes.empty()) return {}; + assert(symTy.isa() && "expecting an array global"); + auto arrTy = symTy.cast(); + llvm::SmallVector tensorShape(arrTy.getShape()); + std::reverse(tensorShape.begin(), tensorShape.end()); auto tensorTy = - mlir::RankedTensorType::get(attributes.size(), attributeElementType); + mlir::RankedTensorType::get(tensorShape, attributeElementType); auto init = mlir::DenseElementsAttr::get(tensorTy, attributes); return builder.createGlobal(loc, symTy, globalName, linkage, init, isConst); } @@ -544,6 +550,13 @@ true, constant); } if (!global) + // If the number of elements of the array is huge, the compilation may + // use a lot of memory and take a very long time to complete. + // Empirical evidence shows that an array with 150000 elements of + // complex type takes roughly 30 seconds to compile and uses 4GB of RAM, + // on a modern machine. + // It would be nice to add a driver switch to control the array size + // after which flang should not continue to compile. global = builder.createGlobalConstant( loc, arrayTy, globalName, [&](fir::FirOpBuilder &builder) { diff --git a/flang/lib/Lower/ConvertVariable.cpp b/flang/lib/Lower/ConvertVariable.cpp --- a/flang/lib/Lower/ConvertVariable.cpp +++ b/flang/lib/Lower/ConvertVariable.cpp @@ -431,14 +431,9 @@ // If this is an array, check to see if we can use a dense attribute // with a tensor mlir type. This optimization currently only supports - // rank-1 Fortran arrays of integer, real, or logical. The tensor - // type does not support nested structures which are needed for - // complex numbers. - // To get multidimensional arrays to work, we will have to use column major - // array ordering with the tensor type (so it matches column major ordering - // with the Fortran fir.array). By default, tensor types assume row major - // ordering. How to create this tensor type is to be determined. - if (symTy.isa() && sym.Rank() == 1 && + // Fortran arrays of integer, real, or logical. The tensor type does + // not support nested structures which are needed for complex numbers. + if (symTy.isa() && !Fortran::semantics::IsAllocatableOrPointer(sym)) { mlir::Type eleTy = symTy.cast().getEleTy(); if (eleTy.isa()) { diff --git a/flang/test/Lower/array.f90 b/flang/test/Lower/array.f90 --- a/flang/test/Lower/array.f90 +++ b/flang/test/Lower/array.f90 @@ -102,33 +102,25 @@ integer, dimension(10) :: a0 real, dimension(2,3) :: a1 integer, dimension(3,4) :: a2 + integer, dimension(2,3,4) :: a3 a0 = (/1, 2, 3, 3, 3, 3, 3, 3, 3, 3/) a1 = reshape((/3.5, 3.5, 3.5, 3.5, 3.5, 3.5/), shape(a1)) a2 = reshape((/1, 3, 3, 5, 3, 3, 3, 3, 9, 9, 9, 8/), shape(a2)) + a3 = reshape((/1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12/), shape(a3)) end subroutine range ! a0 array constructor ! CHECK: fir.global internal @_QQro.10xi4.{{.*}}(dense<[1, 2, 3, 3, 3, 3, 3, 3, 3, 3]> : tensor<10xi32>) constant : !fir.array<10xi32> ! a1 array constructor -! CHECK: fir.global internal @_QQro.2x3xr4.{{.*}} constant : !fir.array<2x3xf32> { - ! CHECK-DAG: %cst = arith.constant {{.*}} : f32 - ! CHECK: %{{.*}} = fir.insert_on_range %{{[0-9]+}}, %cst from (0, 0) to (1, 2) : +! CHECK: fir.global internal @_QQro.2x3xr4.{{.*}}(dense<3.500000e+00> : tensor<3x2xf32>) constant : !fir.array<2x3xf32> ! a2 array constructor -! CHECK: fir.global internal @_QQro.3x4xi4.{{.*}} constant : !fir.array<3x4xi32> { - ! CHECK-DAG: %[[c1_i32:.*]] = arith.constant 1 : i32 - ! CHECK-DAG: %[[c3_i32:.*]] = arith.constant 3 : i32 - ! CHECK-DAG: %[[c5_i32:.*]] = arith.constant 5 : i32 - ! CHECK-DAG: %[[c8_i32:.*]] = arith.constant 8 : i32 - ! CHECK-DAG: %[[c9_i32:.*]] = arith.constant 9 : i32 - ! CHECK: %[[r1:.*]] = fir.insert_value %{{.*}}, %{{.*}}, [0 : index, 0 : index] : - ! CHECK: %[[r2:.*]] = fir.insert_on_range %[[r1]], %[[c3_i32]] from (1, 0) to (2, 0) : - ! CHECK: %[[r3:.*]] = fir.insert_value %[[r2]], %{{.*}}, [0 : index, 1 : index] : - ! CHECK: %[[r4:.*]] = fir.insert_on_range %[[r3]], %[[c3_i32]] from (1, 1) to (1, 2) : - ! CHECK: %[[r5:.*]] = fir.insert_on_range %[[r4]], %[[c9_i32]] from (2, 2) to (1, 3) : - ! CHECK: %[[r6:.*]] = fir.insert_value %[[r5]], %{{.*}}, [2 : index, 3 : index] : +! CHECK: fir.global internal @_QQro.3x4xi4.{{.*}}(dense<{{\[\[1, 3, 3], \[5, 3, 3], \[3, 3, 9], \[9, 9, 8]]}}> : tensor<4x3xi32>) constant : !fir.array<3x4xi32> + +! a3 array constructor +! CHECK: fir.global internal @_QQro.2x3x4xi4.{{.*}}(dense<{{\[\[\[1, 1], \[2, 2], \[3, 3]], \[\[4, 4], \[5, 5], \[6, 6]], \[\[7, 7], \[8, 8], \[9, 9]], \[\[10, 10], \[11, 11], \[12, 12]]]}}> : tensor<4x3x2xi32>) constant : !fir.array<2x3x4xi32> ! CHECK-LABEL rangeGlobal subroutine rangeGlobal() @@ -137,6 +129,15 @@ end subroutine rangeGlobal +! CHECK-LABEL hugeGlobal +subroutine hugeGlobal() + integer, parameter :: D = 500 + integer, dimension(D, D) :: a + +! CHECK: fir.global internal @_QQro.500x500xi4.{{.*}}(dense<{{.*}}> : tensor<500x500xi32>) constant : !fir.array<500x500xi32> + a = reshape((/(i, i = 1, D * D)/), shape(a)) +end subroutine hugeGlobal + block data real(selected_real_kind(6)) :: x(5,5) common /block/ x diff --git a/flang/test/Lower/dense-array-any-rank.f90 b/flang/test/Lower/dense-array-any-rank.f90 new file mode 100644 --- /dev/null +++ b/flang/test/Lower/dense-array-any-rank.f90 @@ -0,0 +1,25 @@ +! RUN: bbc -emit-fir -o - %s | FileCheck --check-prefixes="CHECK-FIR" %s +! RUN: %flang_fc1 -emit-llvm -o - %s | FileCheck --check-prefixes="CHECK-LLVMIR" %s + +! CHECK-LABEL: test +subroutine test() + integer, dimension(10) :: a1 + integer, dimension(3,4) :: a2 + integer, dimension(2,3,4) :: a3 + + a1 = (/1, 2, 3, 4, 5, 6, 7, 8, 9, 10/) + a2 = reshape((/11, 12, 13, 21, 22, 23, 31, 32, 33, 41, 42, 43/), shape(a2)) + a3 = reshape((/111, 112, 121, 122, 131, 132, 211, 212, 221, 222, 231, 232, 311, 312, 321, 322, 331, 332, 411, 412, 421, 422, 431, 432/), shape(a3)) +end subroutine + +! a1 array constructor +! CHECK-FIR: fir.global internal @_QQro.10xi4.{{.*}}(dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]> : tensor<10xi32>) constant : !fir.array<10xi32> +! CHECK-LLVMIR: @_QQro.10xi4.0 = internal constant [10 x i32] [i32 1, i32 2, i32 3, i32 4, i32 5, i32 6, i32 7, i32 8, i32 9, i32 10] + +! a2 array constructor +! CHECK-FIR: fir.global internal @_QQro.3x4xi4.{{.*}}(dense<{{\[\[11, 12, 13], \[21, 22, 23], \[31, 32, 33], \[41, 42, 43]]}}> : tensor<4x3xi32>) constant : !fir.array<3x4xi32> +! CHECK-LLVMIR: @_QQro.3x4xi4.1 = internal constant [4 x [3 x i32]] {{\[\[3 x i32] \[i32 11, i32 12, i32 13], \[3 x i32] \[i32 21, i32 22, i32 23], \[3 x i32] \[i32 31, i32 32, i32 33], \[3 x i32] \[i32 41, i32 42, i32 43]]}} + +! a3 array constructor +! CHECK-FIR: fir.global internal @_QQro.2x3x4xi4.{{.*}}(dense<{{\[\[\[111, 112], \[121, 122], \[131, 132]], \[\[211, 212], \[221, 222], \[231, 232]], \[\[311, 312], \[321, 322], \[331, 332]], \[\[411, 412], \[421, 422], \[431, 432]]]}}> : tensor<4x3x2xi32>) constant : !fir.array<2x3x4xi32> +! CHECK-LLVMIR: @_QQro.2x3x4xi4.2 = internal constant [4 x [3 x [2 x i32]]] {{\[\[3 x \[2 x i32]] \[\[2 x i32] \[i32 111, i32 112], \[2 x i32] \[i32 121, i32 122], \[2 x i32] \[i32 131, i32 132]], \[3 x \[2 x i32]] \[\[2 x i32] \[i32 211, i32 212], \[2 x i32] \[i32 221, i32 222], \[2 x i32] \[i32 231, i32 232]], \[3 x \[2 x i32]] \[\[2 x i32] \[i32 311, i32 312], \[2 x i32] \[i32 321, i32 322], \[2 x i32] \[i32 331, i32 332]], \[3 x \[2 x i32]] \[\[2 x i32] \[i32 411, i32 412], \[2 x i32] \[i32 421, i32 422], \[2 x i32] \[i32 431, i32 432]]]}}