465 lines
17 KiB
C++
465 lines
17 KiB
C++
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//===- ObjectHandler.cpp - Implements base ObjectManager attributes -------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements the `OffloadingLLVMTranslationAttrInterface` for the
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// `SelectObject` attribute.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/GPU/IR/GPUDialect.h"
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#include "mlir/Target/LLVMIR/Dialect/GPU/GPUToLLVMIRTranslation.h"
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#include "mlir/Target/LLVMIR/Export.h"
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#include "mlir/Target/LLVMIR/ModuleTranslation.h"
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#include "llvm/IR/Constants.h"
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#include "llvm/IR/IRBuilder.h"
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#include "llvm/IR/LLVMContext.h"
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#include "llvm/IR/Module.h"
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#include "llvm/Support/FormatVariadic.h"
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using namespace mlir;
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namespace {
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// Implementation of the `OffloadingLLVMTranslationAttrInterface` model.
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class SelectObjectAttrImpl
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: public gpu::OffloadingLLVMTranslationAttrInterface::FallbackModel<
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SelectObjectAttrImpl> {
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public:
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// Translates a `gpu.binary`, embedding the binary into a host LLVM module as
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// global binary string.
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LogicalResult embedBinary(Attribute attribute, Operation *operation,
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llvm::IRBuilderBase &builder,
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LLVM::ModuleTranslation &moduleTranslation) const;
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// Translates a `gpu.launch_func` to a sequence of LLVM instructions resulting
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// in a kernel launch call.
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LogicalResult launchKernel(Attribute attribute,
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Operation *launchFuncOperation,
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Operation *binaryOperation,
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llvm::IRBuilderBase &builder,
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LLVM::ModuleTranslation &moduleTranslation) const;
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// Returns the selected object for embedding.
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gpu::ObjectAttr getSelectedObject(gpu::BinaryOp op) const;
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};
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// Returns an identifier for the global string holding the binary.
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std::string getBinaryIdentifier(StringRef binaryName) {
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return binaryName.str() + "_bin_cst";
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}
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} // namespace
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void mlir::gpu::registerOffloadingLLVMTranslationInterfaceExternalModels(
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DialectRegistry ®istry) {
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registry.addExtension(+[](MLIRContext *ctx, gpu::GPUDialect *dialect) {
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SelectObjectAttr::attachInterface<SelectObjectAttrImpl>(*ctx);
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});
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}
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gpu::ObjectAttr
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SelectObjectAttrImpl::getSelectedObject(gpu::BinaryOp op) const {
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ArrayRef<Attribute> objects = op.getObjectsAttr().getValue();
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// Obtain the index of the object to select.
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int64_t index = -1;
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if (Attribute target =
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cast<gpu::SelectObjectAttr>(op.getOffloadingHandlerAttr())
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.getTarget()) {
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// If the target attribute is a number it is the index. Otherwise compare
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// the attribute to every target inside the object array to find the index.
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if (auto indexAttr = mlir::dyn_cast<IntegerAttr>(target)) {
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index = indexAttr.getInt();
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} else {
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for (auto [i, attr] : llvm::enumerate(objects)) {
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auto obj = mlir::dyn_cast<gpu::ObjectAttr>(attr);
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if (obj.getTarget() == target) {
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index = i;
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}
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}
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}
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} else {
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// If the target attribute is null then it's selecting the first object in
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// the object array.
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index = 0;
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}
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if (index < 0 || index >= static_cast<int64_t>(objects.size())) {
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op->emitError("the requested target object couldn't be found");
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return nullptr;
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}
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return mlir::dyn_cast<gpu::ObjectAttr>(objects[index]);
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}
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LogicalResult SelectObjectAttrImpl::embedBinary(
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Attribute attribute, Operation *operation, llvm::IRBuilderBase &builder,
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LLVM::ModuleTranslation &moduleTranslation) const {
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assert(operation && "The binary operation must be non null.");
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if (!operation)
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return failure();
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auto op = mlir::dyn_cast<gpu::BinaryOp>(operation);
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if (!op) {
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operation->emitError("operation must be a GPU binary");
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return failure();
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}
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gpu::ObjectAttr object = getSelectedObject(op);
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if (!object)
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return failure();
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llvm::Module *module = moduleTranslation.getLLVMModule();
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// Embed the object as a global string.
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llvm::Constant *binary = llvm::ConstantDataArray::getString(
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builder.getContext(), object.getObject().getValue(), false);
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llvm::GlobalVariable *serializedObj =
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new llvm::GlobalVariable(*module, binary->getType(), true,
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llvm::GlobalValue::LinkageTypes::InternalLinkage,
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binary, getBinaryIdentifier(op.getName()));
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serializedObj->setLinkage(llvm::GlobalValue::LinkageTypes::InternalLinkage);
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serializedObj->setAlignment(llvm::MaybeAlign(8));
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serializedObj->setUnnamedAddr(llvm::GlobalValue::UnnamedAddr::None);
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return success();
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}
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namespace llvm {
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namespace {
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class LaunchKernel {
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public:
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LaunchKernel(Module &module, IRBuilderBase &builder,
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mlir::LLVM::ModuleTranslation &moduleTranslation);
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// Get the kernel launch callee.
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FunctionCallee getKernelLaunchFn();
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// Get the kernel launch callee.
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FunctionCallee getClusterKernelLaunchFn();
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// Get the module function callee.
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FunctionCallee getModuleFunctionFn();
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// Get the module load callee.
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FunctionCallee getModuleLoadFn();
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// Get the module load JIT callee.
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FunctionCallee getModuleLoadJITFn();
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// Get the module unload callee.
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FunctionCallee getModuleUnloadFn();
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// Get the stream create callee.
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FunctionCallee getStreamCreateFn();
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// Get the stream destroy callee.
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FunctionCallee getStreamDestroyFn();
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// Get the stream sync callee.
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FunctionCallee getStreamSyncFn();
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// Ger or create the function name global string.
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Value *getOrCreateFunctionName(StringRef moduleName, StringRef kernelName);
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// Create the void* kernel array for passing the arguments.
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Value *createKernelArgArray(mlir::gpu::LaunchFuncOp op);
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// Create the full kernel launch.
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mlir::LogicalResult createKernelLaunch(mlir::gpu::LaunchFuncOp op,
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mlir::gpu::ObjectAttr object);
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private:
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Module &module;
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IRBuilderBase &builder;
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mlir::LLVM::ModuleTranslation &moduleTranslation;
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Type *i32Ty{};
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Type *i64Ty{};
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Type *voidTy{};
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Type *intPtrTy{};
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PointerType *ptrTy{};
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};
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} // namespace
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} // namespace llvm
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LogicalResult SelectObjectAttrImpl::launchKernel(
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Attribute attribute, Operation *launchFuncOperation,
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Operation *binaryOperation, llvm::IRBuilderBase &builder,
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LLVM::ModuleTranslation &moduleTranslation) const {
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assert(launchFuncOperation && "The launch func operation must be non null.");
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if (!launchFuncOperation)
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return failure();
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auto launchFuncOp = mlir::dyn_cast<gpu::LaunchFuncOp>(launchFuncOperation);
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if (!launchFuncOp) {
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launchFuncOperation->emitError("operation must be a GPU launch func Op.");
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return failure();
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}
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auto binOp = mlir::dyn_cast<gpu::BinaryOp>(binaryOperation);
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if (!binOp) {
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binaryOperation->emitError("operation must be a GPU binary.");
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return failure();
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}
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gpu::ObjectAttr object = getSelectedObject(binOp);
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if (!object)
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return failure();
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return llvm::LaunchKernel(*moduleTranslation.getLLVMModule(), builder,
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moduleTranslation)
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.createKernelLaunch(launchFuncOp, object);
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}
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llvm::LaunchKernel::LaunchKernel(
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Module &module, IRBuilderBase &builder,
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mlir::LLVM::ModuleTranslation &moduleTranslation)
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: module(module), builder(builder), moduleTranslation(moduleTranslation) {
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i32Ty = builder.getInt32Ty();
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i64Ty = builder.getInt64Ty();
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ptrTy = builder.getPtrTy(0);
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voidTy = builder.getVoidTy();
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intPtrTy = builder.getIntPtrTy(module.getDataLayout());
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}
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llvm::FunctionCallee llvm::LaunchKernel::getKernelLaunchFn() {
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return module.getOrInsertFunction(
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"mgpuLaunchKernel",
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FunctionType::get(voidTy,
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ArrayRef<Type *>({ptrTy, intPtrTy, intPtrTy, intPtrTy,
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intPtrTy, intPtrTy, intPtrTy, i32Ty,
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ptrTy, ptrTy, ptrTy, i64Ty}),
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false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getClusterKernelLaunchFn() {
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return module.getOrInsertFunction(
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"mgpuLaunchClusterKernel",
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FunctionType::get(
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voidTy,
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ArrayRef<Type *>({ptrTy, intPtrTy, intPtrTy, intPtrTy, intPtrTy,
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intPtrTy, intPtrTy, intPtrTy, intPtrTy, intPtrTy,
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i32Ty, ptrTy, ptrTy, ptrTy}),
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false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getModuleFunctionFn() {
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return module.getOrInsertFunction(
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"mgpuModuleGetFunction",
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FunctionType::get(ptrTy, ArrayRef<Type *>({ptrTy, ptrTy}), false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getModuleLoadFn() {
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return module.getOrInsertFunction(
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"mgpuModuleLoad",
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FunctionType::get(ptrTy, ArrayRef<Type *>({ptrTy, i64Ty}), false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getModuleLoadJITFn() {
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return module.getOrInsertFunction(
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"mgpuModuleLoadJIT",
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FunctionType::get(ptrTy, ArrayRef<Type *>({ptrTy, i32Ty}), false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getModuleUnloadFn() {
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return module.getOrInsertFunction(
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"mgpuModuleUnload",
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FunctionType::get(voidTy, ArrayRef<Type *>({ptrTy}), false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getStreamCreateFn() {
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return module.getOrInsertFunction("mgpuStreamCreate",
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FunctionType::get(ptrTy, false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getStreamDestroyFn() {
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return module.getOrInsertFunction(
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"mgpuStreamDestroy",
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FunctionType::get(voidTy, ArrayRef<Type *>({ptrTy}), false));
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}
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llvm::FunctionCallee llvm::LaunchKernel::getStreamSyncFn() {
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return module.getOrInsertFunction(
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"mgpuStreamSynchronize",
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FunctionType::get(voidTy, ArrayRef<Type *>({ptrTy}), false));
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}
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// Generates an LLVM IR dialect global that contains the name of the given
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// kernel function as a C string, and returns a pointer to its beginning.
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llvm::Value *llvm::LaunchKernel::getOrCreateFunctionName(StringRef moduleName,
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StringRef kernelName) {
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std::string globalName =
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std::string(formatv("{0}_{1}_kernel_name", moduleName, kernelName));
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if (GlobalVariable *gv = module.getGlobalVariable(globalName))
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return gv;
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return builder.CreateGlobalString(kernelName, globalName);
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}
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// Creates a struct containing all kernel parameters on the stack and returns
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// an array of type-erased pointers to the fields of the struct. The array can
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// then be passed to the CUDA / ROCm (HIP) kernel launch calls.
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// The generated code is essentially as follows:
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//
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// %struct = alloca(sizeof(struct { Parameters... }))
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// %array = alloca(NumParameters * sizeof(void *))
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// for (i : [0, NumParameters))
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// %fieldPtr = llvm.getelementptr %struct[0, i]
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// llvm.store parameters[i], %fieldPtr
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// %elementPtr = llvm.getelementptr %array[i]
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// llvm.store %fieldPtr, %elementPtr
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// return %array
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llvm::Value *
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llvm::LaunchKernel::createKernelArgArray(mlir::gpu::LaunchFuncOp op) {
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SmallVector<Value *> args =
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moduleTranslation.lookupValues(op.getKernelOperands());
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SmallVector<Type *> structTypes(args.size(), nullptr);
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for (auto [i, arg] : llvm::enumerate(args))
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structTypes[i] = arg->getType();
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Type *structTy = StructType::create(module.getContext(), structTypes);
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Value *argStruct = builder.CreateAlloca(structTy, 0u);
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Value *argArray = builder.CreateAlloca(
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ptrTy, ConstantInt::get(intPtrTy, structTypes.size()));
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for (auto [i, arg] : enumerate(args)) {
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Value *structMember = builder.CreateStructGEP(structTy, argStruct, i);
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builder.CreateStore(arg, structMember);
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Value *arrayMember = builder.CreateConstGEP1_32(ptrTy, argArray, i);
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builder.CreateStore(structMember, arrayMember);
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}
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return argArray;
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}
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// Emits LLVM IR to launch a kernel function:
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// %0 = call %binarygetter
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// %1 = call %moduleLoad(%0)
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// %2 = <see generateKernelNameConstant>
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// %3 = call %moduleGetFunction(%1, %2)
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// %4 = call %streamCreate()
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// %5 = <see generateParamsArray>
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// call %launchKernel(%3, <launchOp operands 0..5>, 0, %4, %5, nullptr)
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// call %streamSynchronize(%4)
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// call %streamDestroy(%4)
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// call %moduleUnload(%1)
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mlir::LogicalResult
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llvm::LaunchKernel::createKernelLaunch(mlir::gpu::LaunchFuncOp op,
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mlir::gpu::ObjectAttr object) {
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auto llvmValue = [&](mlir::Value value) -> Value * {
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Value *v = moduleTranslation.lookupValue(value);
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assert(v && "Value has not been translated.");
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return v;
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};
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// Get grid dimensions.
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mlir::gpu::KernelDim3 grid = op.getGridSizeOperandValues();
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Value *gx = llvmValue(grid.x), *gy = llvmValue(grid.y),
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*gz = llvmValue(grid.z);
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// Get block dimensions.
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mlir::gpu::KernelDim3 block = op.getBlockSizeOperandValues();
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Value *bx = llvmValue(block.x), *by = llvmValue(block.y),
|
||
|
|
*bz = llvmValue(block.z);
|
||
|
|
|
||
|
|
// Get dynamic shared memory size.
|
||
|
|
Value *dynamicMemorySize = nullptr;
|
||
|
|
if (mlir::Value dynSz = op.getDynamicSharedMemorySize())
|
||
|
|
dynamicMemorySize = llvmValue(dynSz);
|
||
|
|
else
|
||
|
|
dynamicMemorySize = ConstantInt::get(i32Ty, 0);
|
||
|
|
|
||
|
|
// Create the argument array.
|
||
|
|
Value *argArray = createKernelArgArray(op);
|
||
|
|
|
||
|
|
// Default JIT optimization level.
|
||
|
|
llvm::Constant *optV = llvm::ConstantInt::get(i32Ty, 0);
|
||
|
|
// Check if there's an optimization level embedded in the object.
|
||
|
|
DictionaryAttr objectProps = object.getProperties();
|
||
|
|
mlir::Attribute optAttr;
|
||
|
|
if (objectProps && (optAttr = objectProps.get("O"))) {
|
||
|
|
auto optLevel = dyn_cast<IntegerAttr>(optAttr);
|
||
|
|
if (!optLevel)
|
||
|
|
return op.emitError("the optimization level must be an integer");
|
||
|
|
optV = llvm::ConstantInt::get(i32Ty, optLevel.getValue());
|
||
|
|
}
|
||
|
|
|
||
|
|
// Load the kernel module.
|
||
|
|
StringRef moduleName = op.getKernelModuleName().getValue();
|
||
|
|
std::string binaryIdentifier = getBinaryIdentifier(moduleName);
|
||
|
|
Value *binary = module.getGlobalVariable(binaryIdentifier, true);
|
||
|
|
if (!binary)
|
||
|
|
return op.emitError() << "Couldn't find the binary: " << binaryIdentifier;
|
||
|
|
|
||
|
|
auto binaryVar = dyn_cast<llvm::GlobalVariable>(binary);
|
||
|
|
if (!binaryVar)
|
||
|
|
return op.emitError() << "Binary is not a global variable: "
|
||
|
|
<< binaryIdentifier;
|
||
|
|
llvm::Constant *binaryInit = binaryVar->getInitializer();
|
||
|
|
auto binaryDataSeq =
|
||
|
|
dyn_cast_if_present<llvm::ConstantDataSequential>(binaryInit);
|
||
|
|
if (!binaryDataSeq)
|
||
|
|
return op.emitError() << "Couldn't find binary data array: "
|
||
|
|
<< binaryIdentifier;
|
||
|
|
llvm::Constant *binarySize =
|
||
|
|
llvm::ConstantInt::get(i64Ty, binaryDataSeq->getNumElements() *
|
||
|
|
binaryDataSeq->getElementByteSize());
|
||
|
|
|
||
|
|
Value *moduleObject =
|
||
|
|
object.getFormat() == gpu::CompilationTarget::Assembly
|
||
|
|
? builder.CreateCall(getModuleLoadJITFn(), {binary, optV})
|
||
|
|
: builder.CreateCall(getModuleLoadFn(), {binary, binarySize});
|
||
|
|
|
||
|
|
// Load the kernel function.
|
||
|
|
Value *moduleFunction = builder.CreateCall(
|
||
|
|
getModuleFunctionFn(),
|
||
|
|
{moduleObject,
|
||
|
|
getOrCreateFunctionName(moduleName, op.getKernelName().getValue())});
|
||
|
|
|
||
|
|
// Get the stream to use for execution. If there's no async object then create
|
||
|
|
// a stream to make a synchronous kernel launch.
|
||
|
|
Value *stream = nullptr;
|
||
|
|
bool handleStream = false;
|
||
|
|
if (mlir::Value asyncObject = op.getAsyncObject()) {
|
||
|
|
stream = llvmValue(asyncObject);
|
||
|
|
} else {
|
||
|
|
handleStream = true;
|
||
|
|
stream = builder.CreateCall(getStreamCreateFn(), {});
|
||
|
|
}
|
||
|
|
|
||
|
|
llvm::Constant *paramsCount =
|
||
|
|
llvm::ConstantInt::get(i64Ty, op.getNumKernelOperands());
|
||
|
|
|
||
|
|
// Create the launch call.
|
||
|
|
Value *nullPtr = ConstantPointerNull::get(ptrTy);
|
||
|
|
|
||
|
|
// Launch kernel with clusters if cluster size is specified.
|
||
|
|
if (op.hasClusterSize()) {
|
||
|
|
mlir::gpu::KernelDim3 cluster = op.getClusterSizeOperandValues();
|
||
|
|
Value *cx = llvmValue(cluster.x), *cy = llvmValue(cluster.y),
|
||
|
|
*cz = llvmValue(cluster.z);
|
||
|
|
builder.CreateCall(
|
||
|
|
getClusterKernelLaunchFn(),
|
||
|
|
ArrayRef<Value *>({moduleFunction, cx, cy, cz, gx, gy, gz, bx, by, bz,
|
||
|
|
dynamicMemorySize, stream, argArray, nullPtr}));
|
||
|
|
} else {
|
||
|
|
builder.CreateCall(getKernelLaunchFn(),
|
||
|
|
ArrayRef<Value *>({moduleFunction, gx, gy, gz, bx, by,
|
||
|
|
bz, dynamicMemorySize, stream,
|
||
|
|
argArray, nullPtr, paramsCount}));
|
||
|
|
}
|
||
|
|
|
||
|
|
// Sync & destroy the stream, for synchronous launches.
|
||
|
|
if (handleStream) {
|
||
|
|
builder.CreateCall(getStreamSyncFn(), {stream});
|
||
|
|
builder.CreateCall(getStreamDestroyFn(), {stream});
|
||
|
|
}
|
||
|
|
|
||
|
|
// Unload the kernel module.
|
||
|
|
builder.CreateCall(getModuleUnloadFn(), {moduleObject});
|
||
|
|
|
||
|
|
return success();
|
||
|
|
}
|