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Reland "[flang] Handle array constants of any rank"
Fixes gfortran test-suite regression. Differential Revision: https://reviews.llvm.org/D150686
This commit is contained in:
Leandro Lupori
parent
18a920978f
commit
556483fe0b
@ -20,6 +20,8 @@
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#include "flang/Optimizer/Builder/Complex.h"
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#include "flang/Optimizer/Builder/Todo.h"
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#include <algorithm>
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/// Convert string, \p s, to an APFloat value. Recognize and handle Inf and
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/// NaN strings as well. \p s is assumed to not contain any spaces.
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static llvm::APFloat consAPFloat(const llvm::fltSemantics &fsem,
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@ -48,7 +50,14 @@ static mlir::Attribute convertToAttribute(
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const Fortran::evaluate::Scalar<Fortran::evaluate::Type<TC, KIND>> &value,
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mlir::Type type) {
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if constexpr (TC == Fortran::common::TypeCategory::Integer) {
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return builder.getIntegerAttr(type, value.ToInt64());
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if constexpr (KIND <= 8)
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return builder.getIntegerAttr(type, value.ToInt64());
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else {
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static_assert(KIND <= 16, "integers with KIND > 16 are not supported");
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return builder.getIntegerAttr(
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type, llvm::APInt(KIND * 8,
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{value.ToUInt64(), value.SHIFTR(64).ToUInt64()}));
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}
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} else if constexpr (TC == Fortran::common::TypeCategory::Logical) {
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return builder.getIntegerAttr(type, value.IsTrue());
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} else {
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@ -66,17 +75,12 @@ namespace {
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/// Helper class to lower an array constant to a global with an MLIR dense
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/// attribute.
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///
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/// If we have a rank-1 array of integer, real, or logical, then we can
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/// If we have an array of integer, real, or logical, then we can
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/// create a global array with the dense attribute.
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///
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/// The mlir tensor type can only handle integer, real, or logical. It
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/// does not currently support nested structures which is required for
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/// complex.
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///
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/// Also, we currently handle just rank-1 since tensor type assumes
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/// row major array ordering. We will need to reorder the dimensions
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/// in the tensor type to support Fortran's column major array ordering.
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/// How to create this tensor type is to be determined.
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class DenseGlobalBuilder {
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public:
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static fir::GlobalOp tryCreating(fir::FirOpBuilder &builder,
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@ -124,8 +128,6 @@ private:
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&constant) {
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static_assert(TC != Fortran::common::TypeCategory::Character,
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"must be numerical or logical");
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if (constant.Rank() != 1)
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return;
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auto attrTc = TC == Fortran::common::TypeCategory::Logical
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? Fortran::common::TypeCategory::Integer
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: TC;
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@ -158,12 +160,16 @@ private:
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llvm::StringRef globalName,
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mlir::StringAttr linkage,
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bool isConst) const {
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// Not a rank 1 "trivial" intrinsic constant array, or empty array.
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// Not a "trivial" intrinsic constant array, or empty array.
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if (!attributeElementType || attributes.empty())
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return {};
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assert(symTy.isa<fir::SequenceType>() && "expecting an array global");
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auto arrTy = symTy.cast<fir::SequenceType>();
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llvm::SmallVector<int64_t> tensorShape(arrTy.getShape());
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std::reverse(tensorShape.begin(), tensorShape.end());
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auto tensorTy =
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mlir::RankedTensorType::get(attributes.size(), attributeElementType);
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mlir::RankedTensorType::get(tensorShape, attributeElementType);
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auto init = mlir::DenseElementsAttr::get(tensorTy, attributes);
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return builder.createGlobal(loc, symTy, globalName, linkage, init, isConst);
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}
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@ -544,6 +550,13 @@ genOutlineArrayLit(Fortran::lower::AbstractConverter &converter,
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true, constant);
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}
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if (!global)
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// If the number of elements of the array is huge, the compilation may
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// use a lot of memory and take a very long time to complete.
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// Empirical evidence shows that an array with 150000 elements of
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// complex type takes roughly 30 seconds to compile and uses 4GB of RAM,
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// on a modern machine.
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// It would be nice to add a driver switch to control the array size
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// after which flang should not continue to compile.
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global = builder.createGlobalConstant(
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loc, arrayTy, globalName,
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[&](fir::FirOpBuilder &builder) {
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@ -431,14 +431,9 @@ static fir::GlobalOp defineGlobal(Fortran::lower::AbstractConverter &converter,
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// If this is an array, check to see if we can use a dense attribute
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// with a tensor mlir type. This optimization currently only supports
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// rank-1 Fortran arrays of integer, real, or logical. The tensor
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// type does not support nested structures which are needed for
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// complex numbers.
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// To get multidimensional arrays to work, we will have to use column major
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// array ordering with the tensor type (so it matches column major ordering
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// with the Fortran fir.array). By default, tensor types assume row major
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// ordering. How to create this tensor type is to be determined.
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if (symTy.isa<fir::SequenceType>() && sym.Rank() == 1 &&
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// Fortran arrays of integer, real, or logical. The tensor type does
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// not support nested structures which are needed for complex numbers.
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if (symTy.isa<fir::SequenceType>() &&
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!Fortran::semantics::IsAllocatableOrPointer(sym)) {
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mlir::Type eleTy = symTy.cast<fir::SequenceType>().getEleTy();
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if (eleTy.isa<mlir::IntegerType, mlir::FloatType, fir::LogicalType>()) {
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@ -102,33 +102,25 @@ subroutine range()
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integer, dimension(10) :: a0
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real, dimension(2,3) :: a1
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integer, dimension(3,4) :: a2
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integer, dimension(2,3,4) :: a3
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a0 = (/1, 2, 3, 3, 3, 3, 3, 3, 3, 3/)
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a1 = reshape((/3.5, 3.5, 3.5, 3.5, 3.5, 3.5/), shape(a1))
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a2 = reshape((/1, 3, 3, 5, 3, 3, 3, 3, 9, 9, 9, 8/), shape(a2))
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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))
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end subroutine range
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! a0 array constructor
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! CHECK: fir.global internal @_QQro.10xi4.{{.*}}(dense<[1, 2, 3, 3, 3, 3, 3, 3, 3, 3]> : tensor<10xi32>) constant : !fir.array<10xi32>
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! a1 array constructor
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! CHECK: fir.global internal @_QQro.2x3xr4.{{.*}} constant : !fir.array<2x3xf32> {
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! CHECK-DAG: %cst = arith.constant {{.*}} : f32
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! CHECK: %{{.*}} = fir.insert_on_range %{{[0-9]+}}, %cst from (0, 0) to (1, 2) :
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! CHECK: fir.global internal @_QQro.2x3xr4.{{.*}}(dense<3.500000e+00> : tensor<3x2xf32>) constant : !fir.array<2x3xf32>
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! a2 array constructor
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! CHECK: fir.global internal @_QQro.3x4xi4.{{.*}} constant : !fir.array<3x4xi32> {
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! CHECK-DAG: %[[c1_i32:.*]] = arith.constant 1 : i32
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! CHECK-DAG: %[[c3_i32:.*]] = arith.constant 3 : i32
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! CHECK-DAG: %[[c5_i32:.*]] = arith.constant 5 : i32
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! CHECK-DAG: %[[c8_i32:.*]] = arith.constant 8 : i32
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! CHECK-DAG: %[[c9_i32:.*]] = arith.constant 9 : i32
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! CHECK: %[[r1:.*]] = fir.insert_value %{{.*}}, %{{.*}}, [0 : index, 0 : index] :
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! CHECK: %[[r2:.*]] = fir.insert_on_range %[[r1]], %[[c3_i32]] from (1, 0) to (2, 0) :
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! CHECK: %[[r3:.*]] = fir.insert_value %[[r2]], %{{.*}}, [0 : index, 1 : index] :
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! CHECK: %[[r4:.*]] = fir.insert_on_range %[[r3]], %[[c3_i32]] from (1, 1) to (1, 2) :
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! CHECK: %[[r5:.*]] = fir.insert_on_range %[[r4]], %[[c9_i32]] from (2, 2) to (1, 3) :
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! CHECK: %[[r6:.*]] = fir.insert_value %[[r5]], %{{.*}}, [2 : index, 3 : index] :
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! 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>
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! a3 array constructor
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! 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>
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! CHECK-LABEL rangeGlobal
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subroutine rangeGlobal()
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@ -137,6 +129,15 @@ subroutine rangeGlobal()
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end subroutine rangeGlobal
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! CHECK-LABEL hugeGlobal
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subroutine hugeGlobal()
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integer, parameter :: D = 500
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integer, dimension(D, D) :: a
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! CHECK: fir.global internal @_QQro.500x500xi4.{{.*}}(dense<{{.*}}> : tensor<500x500xi32>) constant : !fir.array<500x500xi32>
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a = reshape((/(i, i = 1, D * D)/), shape(a))
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end subroutine hugeGlobal
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block data
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real(selected_real_kind(6)) :: x(5,5)
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common /block/ x
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25
flang/test/Lower/dense-array-any-rank.f90
Normal file
25
flang/test/Lower/dense-array-any-rank.f90
Normal file
@ -0,0 +1,25 @@
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! RUN: bbc -emit-fir -o - %s | FileCheck --check-prefixes="CHECK-FIR" %s
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! RUN: %flang_fc1 -emit-llvm -o - %s | FileCheck --check-prefixes="CHECK-LLVMIR" %s
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! CHECK-LABEL: test
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subroutine test()
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integer, dimension(10) :: a1
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integer, dimension(3,4) :: a2
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integer, dimension(2,3,4) :: a3
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a1 = (/1, 2, 3, 4, 5, 6, 7, 8, 9, 10/)
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a2 = reshape((/11, 12, 13, 21, 22, 23, 31, 32, 33, 41, 42, 43/), shape(a2))
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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))
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end subroutine
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! a1 array constructor
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! CHECK-FIR: fir.global internal @_QQro.10xi4.{{.*}}(dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]> : tensor<10xi32>) constant : !fir.array<10xi32>
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! 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]
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! a2 array constructor
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! 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>
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! 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]]}}
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! a3 array constructor
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! 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>
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! 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]]]}}
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