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[flang][cuda] Correctly allocate memory for descriptor load (#120164)
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CodeGen will allocate memory for a new descriptor on descriptor loads.
CUDA Fortran local descriptor are allocated in managed memory by the
runtime. The newly allocated storage for cuda descriptor must also be
allocated through the runtime.
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clementval authored Dec 17, 2024
1 parent fba3e06 commit 5e1f87e
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Showing 2 changed files with 133 additions and 2 deletions.
106 changes: 104 additions & 2 deletions flang/lib/Optimizer/CodeGen/CodeGen.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
#include "flang/Optimizer/Support/InternalNames.h"
#include "flang/Optimizer/Support/TypeCode.h"
#include "flang/Optimizer/Support/Utils.h"
#include "flang/Runtime/CUDA/descriptor.h"
#include "flang/Runtime/allocator-registry-consts.h"
#include "flang/Runtime/descriptor-consts.h"
#include "flang/Semantics/runtime-type-info.h"
Expand Down Expand Up @@ -2970,6 +2971,93 @@ struct GlobalOpConversion : public fir::FIROpConversion<fir::GlobalOp> {
}
};

static mlir::Value genSourceFile(mlir::Location loc, mlir::ModuleOp mod,
mlir::ConversionPatternRewriter &rewriter) {
auto ptrTy = mlir::LLVM::LLVMPointerType::get(rewriter.getContext());
if (auto flc = mlir::dyn_cast<mlir::FileLineColLoc>(loc)) {
auto fn = flc.getFilename().str() + '\0';
std::string globalName = fir::factory::uniqueCGIdent("cl", fn);

if (auto g = mod.lookupSymbol<fir::GlobalOp>(globalName)) {
return rewriter.create<mlir::LLVM::AddressOfOp>(loc, ptrTy, g.getName());
} else if (auto g = mod.lookupSymbol<mlir::LLVM::GlobalOp>(globalName)) {
return rewriter.create<mlir::LLVM::AddressOfOp>(loc, ptrTy, g.getName());
}

auto crtInsPt = rewriter.saveInsertionPoint();
rewriter.setInsertionPoint(mod.getBody(), mod.getBody()->end());
auto arrayTy = mlir::LLVM::LLVMArrayType::get(
mlir::IntegerType::get(rewriter.getContext(), 8), fn.size());
mlir::LLVM::GlobalOp globalOp = rewriter.create<mlir::LLVM::GlobalOp>(
loc, arrayTy, /*constant=*/true, mlir::LLVM::Linkage::Linkonce,
globalName, mlir::Attribute());

mlir::Region &region = globalOp.getInitializerRegion();
mlir::Block *block = rewriter.createBlock(&region);
rewriter.setInsertionPoint(block, block->begin());
mlir::Value constValue = rewriter.create<mlir::LLVM::ConstantOp>(
loc, arrayTy, rewriter.getStringAttr(fn));
rewriter.create<mlir::LLVM::ReturnOp>(loc, constValue);
rewriter.restoreInsertionPoint(crtInsPt);
return rewriter.create<mlir::LLVM::AddressOfOp>(loc, ptrTy,
globalOp.getName());
}
return rewriter.create<mlir::LLVM::ZeroOp>(loc, ptrTy);
}

static mlir::Value genSourceLine(mlir::Location loc,
mlir::ConversionPatternRewriter &rewriter) {
if (auto flc = mlir::dyn_cast<mlir::FileLineColLoc>(loc))
return rewriter.create<mlir::LLVM::ConstantOp>(loc, rewriter.getI32Type(),
flc.getLine());
return rewriter.create<mlir::LLVM::ConstantOp>(loc, rewriter.getI32Type(), 0);
}

static mlir::Value
genCUFAllocDescriptor(mlir::Location loc,
mlir::ConversionPatternRewriter &rewriter,
mlir::ModuleOp mod, fir::BaseBoxType boxTy,
const fir::LLVMTypeConverter &typeConverter) {
std::optional<mlir::DataLayout> dl =
fir::support::getOrSetDataLayout(mod, /*allowDefaultLayout=*/true);
if (!dl)
mlir::emitError(mod.getLoc(),
"module operation must carry a data layout attribute "
"to generate llvm IR from FIR");

mlir::Value sourceFile = genSourceFile(loc, mod, rewriter);
mlir::Value sourceLine = genSourceLine(loc, rewriter);

mlir::MLIRContext *ctx = mod.getContext();

mlir::LLVM::LLVMPointerType llvmPointerType =
mlir::LLVM::LLVMPointerType::get(ctx);
mlir::Type llvmInt32Type = mlir::IntegerType::get(ctx, 32);
mlir::Type llvmIntPtrType =
mlir::IntegerType::get(ctx, typeConverter.getPointerBitwidth(0));
auto fctTy = mlir::LLVM::LLVMFunctionType::get(
llvmPointerType, {llvmIntPtrType, llvmPointerType, llvmInt32Type});

auto llvmFunc = mod.lookupSymbol<mlir::LLVM::LLVMFuncOp>(
RTNAME_STRING(CUFAllocDesciptor));
auto funcFunc =
mod.lookupSymbol<mlir::func::FuncOp>(RTNAME_STRING(CUFAllocDesciptor));
if (!llvmFunc && !funcFunc)
mlir::OpBuilder::atBlockEnd(mod.getBody())
.create<mlir::LLVM::LLVMFuncOp>(loc, RTNAME_STRING(CUFAllocDesciptor),
fctTy);

mlir::Type structTy = typeConverter.convertBoxTypeAsStruct(boxTy);
std::size_t boxSize = dl->getTypeSizeInBits(structTy) / 8;
mlir::Value sizeInBytes =
genConstantIndex(loc, llvmIntPtrType, rewriter, boxSize);
llvm::SmallVector args = {sizeInBytes, sourceFile, sourceLine};
return rewriter
.create<mlir::LLVM::CallOp>(loc, fctTy, RTNAME_STRING(CUFAllocDesciptor),
args)
.getResult();
}

/// `fir.load` --> `llvm.load`
struct LoadOpConversion : public fir::FIROpConversion<fir::LoadOp> {
using FIROpConversion::FIROpConversion;
Expand All @@ -2986,9 +3074,23 @@ struct LoadOpConversion : public fir::FIROpConversion<fir::LoadOp> {
// loading a fir.ref<fir.box> is implemented as taking a snapshot of the
// descriptor value into a new descriptor temp.
auto inputBoxStorage = adaptor.getOperands()[0];
mlir::Value newBoxStorage;
mlir::Location loc = load.getLoc();
auto newBoxStorage =
genAllocaAndAddrCastWithType(loc, llvmLoadTy, defaultAlign, rewriter);
if (auto callOp = mlir::dyn_cast_or_null<mlir::LLVM::CallOp>(
inputBoxStorage.getDefiningOp())) {
if (callOp.getCallee() &&
(*callOp.getCallee())
.starts_with(RTNAME_STRING(CUFAllocDesciptor))) {
// CUDA Fortran local descriptor are allocated in managed memory. So
// new storage must be allocated the same way.
auto mod = load->getParentOfType<mlir::ModuleOp>();
newBoxStorage =
genCUFAllocDescriptor(loc, rewriter, mod, boxTy, lowerTy());
}
}
if (!newBoxStorage)
newBoxStorage = genAllocaAndAddrCastWithType(loc, llvmLoadTy,
defaultAlign, rewriter);

TypePair boxTypePair{boxTy, llvmLoadTy};
mlir::Value boxSize =
Expand Down
29 changes: 29 additions & 0 deletions flang/test/Fir/CUDA/cuda-code-gen.mlir
Original file line number Diff line number Diff line change
@@ -0,0 +1,29 @@
// RUN: fir-opt --split-input-file --fir-to-llvm-ir="target=x86_64-unknown-linux-gnu" %s | FileCheck %s

module attributes {dlti.dl_spec = #dlti.dl_spec<#dlti.dl_entry<f80, dense<128> : vector<2xi64>>, #dlti.dl_entry<i128, dense<128> : vector<2xi64>>, #dlti.dl_entry<i64, dense<64> : vector<2xi64>>, #dlti.dl_entry<!llvm.ptr<272>, dense<64> : vector<4xi64>>, #dlti.dl_entry<!llvm.ptr<271>, dense<32> : vector<4xi64>>, #dlti.dl_entry<!llvm.ptr<270>, dense<32> : vector<4xi64>>, #dlti.dl_entry<f128, dense<128> : vector<2xi64>>, #dlti.dl_entry<f64, dense<64> : vector<2xi64>>, #dlti.dl_entry<f16, dense<16> : vector<2xi64>>, #dlti.dl_entry<i32, dense<32> : vector<2xi64>>, #dlti.dl_entry<i16, dense<16> : vector<2xi64>>, #dlti.dl_entry<i8, dense<8> : vector<2xi64>>, #dlti.dl_entry<i1, dense<8> : vector<2xi64>>, #dlti.dl_entry<!llvm.ptr, dense<64> : vector<4xi64>>, #dlti.dl_entry<"dlti.endianness", "little">, #dlti.dl_entry<"dlti.stack_alignment", 128 : i64>>} {

func.func @_QQmain() attributes {fir.bindc_name = "cufkernel_global"} {
%c0 = arith.constant 0 : index
%0 = fir.address_of(@_QQclX3C737464696E3E00) : !fir.ref<!fir.char<1,8>>
%c4_i32 = arith.constant 4 : i32
%c48 = arith.constant 48 : index
%1 = fir.convert %c48 : (index) -> i64
%2 = fir.convert %0 : (!fir.ref<!fir.char<1,8>>) -> !fir.ref<i8>
%3 = fir.call @_FortranACUFAllocDesciptor(%1, %2, %c4_i32) : (i64, !fir.ref<i8>, i32) -> !fir.ref<!fir.box<none>>
%4 = fir.convert %3 : (!fir.ref<!fir.box<none>>) -> !fir.ref<!fir.box<!fir.heap<!fir.array<?xi32>>>>
%5 = fir.zero_bits !fir.heap<!fir.array<?xi32>>
%6 = fircg.ext_embox %5(%c0) {allocator_idx = 2 : i32} : (!fir.heap<!fir.array<?xi32>>, index) -> !fir.box<!fir.heap<!fir.array<?xi32>>>
fir.store %6 to %4 : !fir.ref<!fir.box<!fir.heap<!fir.array<?xi32>>>>
%8 = fir.load %3 : !fir.ref<!fir.box<none>>
return
}

// CHECK-LABEL: llvm.func @_QQmain()
// CHECK-COUNT-2: llvm.call @_FortranACUFAllocDesciptor

fir.global linkonce @_QQclX3C737464696E3E00 constant : !fir.char<1,8> {
%0 = fir.string_lit "<stdin>\00"(8) : !fir.char<1,8>
fir.has_value %0 : !fir.char<1,8>
}
func.func private @_FortranACUFAllocDesciptor(i64, !fir.ref<i8>, i32) -> !fir.ref<!fir.box<none>> attributes {fir.runtime}
}

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