283b5e733d
Summary: In the original design, gpu.launch required explicit capture of uses and passing them as operands to the gpu.launch operation. This was motivated by infrastructure restrictions rather than design. This change lifts the requirement and removes the concept of kernel arguments from gpu.launch. Instead, the kernel outlining transformation now does the explicit capturing. This is a breaking change for users of gpu.launch. Differential Revision: https://reviews.llvm.org/D73769
247 lines
10 KiB
C++
247 lines
10 KiB
C++
//===- KernelOutlining.cpp - Implementation of GPU kernel outlining -------===//
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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 GPU dialect kernel outlining pass.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/GPU/GPUDialect.h"
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#include "mlir/Dialect/GPU/Passes.h"
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#include "mlir/Dialect/StandardOps/Ops.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/SymbolTable.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/RegionUtils.h"
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using namespace mlir;
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template <typename OpTy>
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static void createForAllDimensions(OpBuilder &builder, Location loc,
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SmallVectorImpl<Value> &values) {
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for (StringRef dim : {"x", "y", "z"}) {
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Value v = builder.create<OpTy>(loc, builder.getIndexType(),
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builder.getStringAttr(dim));
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values.push_back(v);
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}
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}
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// Add operations generating block/thread ids and grid/block dimensions at the
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// beginning of the `body` region and replace uses of the respective function
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// arguments.
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static void injectGpuIndexOperations(Location loc, Region &body) {
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OpBuilder builder(loc->getContext());
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Block &firstBlock = body.front();
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builder.setInsertionPointToStart(&firstBlock);
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SmallVector<Value, 12> indexOps;
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createForAllDimensions<gpu::BlockIdOp>(builder, loc, indexOps);
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createForAllDimensions<gpu::ThreadIdOp>(builder, loc, indexOps);
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createForAllDimensions<gpu::GridDimOp>(builder, loc, indexOps);
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createForAllDimensions<gpu::BlockDimOp>(builder, loc, indexOps);
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// Replace the leading 12 function args with the respective thread/block index
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// operations. Iterate backwards since args are erased and indices change.
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for (int i = 11; i >= 0; --i) {
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firstBlock.getArgument(i).replaceAllUsesWith(indexOps[i]);
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firstBlock.eraseArgument(i);
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}
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}
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static bool isInliningBeneficiary(Operation *op) {
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return isa<ConstantOp>(op) || isa<DimOp>(op);
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}
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// Move arguments of the given kernel function into the function if this reduces
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// the number of kernel arguments.
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static gpu::LaunchFuncOp inlineBeneficiaryOps(gpu::GPUFuncOp kernelFunc,
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gpu::LaunchFuncOp launch) {
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OpBuilder kernelBuilder(kernelFunc.getBody());
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auto &firstBlock = kernelFunc.getBody().front();
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SmallVector<Value, 8> newLaunchArgs;
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BlockAndValueMapping map;
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for (int i = 0, e = launch.getNumKernelOperands(); i < e; ++i) {
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map.map(launch.getKernelOperand(i), kernelFunc.getArgument(i));
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}
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for (int i = launch.getNumKernelOperands() - 1; i >= 0; --i) {
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auto operandOp = launch.getKernelOperand(i).getDefiningOp();
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if (!operandOp || !isInliningBeneficiary(operandOp)) {
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newLaunchArgs.push_back(launch.getKernelOperand(i));
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continue;
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}
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// Only inline operations that do not create new arguments.
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if (!llvm::all_of(operandOp->getOperands(),
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[map](Value value) { return map.contains(value); })) {
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continue;
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}
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auto clone = kernelBuilder.clone(*operandOp, map);
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firstBlock.getArgument(i).replaceAllUsesWith(clone->getResult(0));
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firstBlock.eraseArgument(i);
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}
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if (newLaunchArgs.size() == launch.getNumKernelOperands())
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return launch;
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std::reverse(newLaunchArgs.begin(), newLaunchArgs.end());
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OpBuilder LaunchBuilder(launch);
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SmallVector<Type, 8> newArgumentTypes;
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newArgumentTypes.reserve(firstBlock.getNumArguments());
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for (auto value : firstBlock.getArguments()) {
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newArgumentTypes.push_back(value.getType());
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}
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kernelFunc.setType(LaunchBuilder.getFunctionType(newArgumentTypes, {}));
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auto newLaunch = LaunchBuilder.create<gpu::LaunchFuncOp>(
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launch.getLoc(), kernelFunc, launch.getGridSizeOperandValues(),
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launch.getBlockSizeOperandValues(), newLaunchArgs);
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launch.erase();
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return newLaunch;
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}
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// Outline the `gpu.launch` operation body into a kernel function. Replace
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// `gpu.terminator` operations by `gpu.return` in the generated function.
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static gpu::GPUFuncOp outlineKernelFunc(gpu::LaunchOp launchOp,
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llvm::SetVector<Value> &operands) {
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Location loc = launchOp.getLoc();
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// Create a builder with no insertion point, insertion will happen separately
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// due to symbol table manipulation.
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OpBuilder builder(launchOp.getContext());
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// Identify uses from values defined outside of the scope of the launch
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// operation.
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getUsedValuesDefinedAbove(launchOp.body(), operands);
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SmallVector<Type, 4> kernelOperandTypes;
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kernelOperandTypes.reserve(operands.size());
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for (Value operand : operands) {
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kernelOperandTypes.push_back(operand.getType());
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}
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FunctionType type =
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FunctionType::get(kernelOperandTypes, {}, launchOp.getContext());
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std::string kernelFuncName =
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Twine(launchOp.getParentOfType<FuncOp>().getName(), "_kernel").str();
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auto outlinedFunc = builder.create<gpu::GPUFuncOp>(loc, kernelFuncName, type);
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outlinedFunc.setAttr(gpu::GPUDialect::getKernelFuncAttrName(),
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builder.getUnitAttr());
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outlinedFunc.body().takeBody(launchOp.body());
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injectGpuIndexOperations(loc, outlinedFunc.body());
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Block &entryBlock = outlinedFunc.body().front();
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for (Value operand : operands) {
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BlockArgument newArg = entryBlock.addArgument(operand.getType());
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replaceAllUsesInRegionWith(operand, newArg, outlinedFunc.body());
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}
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outlinedFunc.walk([](gpu::TerminatorOp op) {
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OpBuilder replacer(op);
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replacer.create<gpu::ReturnOp>(op.getLoc());
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op.erase();
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});
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return outlinedFunc;
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}
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// Replace `gpu.launch` operations with an `gpu.launch_func` operation launching
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// `kernelFunc`. The kernel func contains the body of the `gpu.launch` with
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// constant region arguments inlined.
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static void convertToLaunchFuncOp(gpu::LaunchOp &launchOp,
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gpu::GPUFuncOp kernelFunc,
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ValueRange operands) {
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OpBuilder builder(launchOp);
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auto launchFuncOp = builder.create<gpu::LaunchFuncOp>(
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launchOp.getLoc(), kernelFunc, launchOp.getGridSizeOperandValues(),
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launchOp.getBlockSizeOperandValues(), operands);
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inlineBeneficiaryOps(kernelFunc, launchFuncOp);
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launchOp.erase();
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}
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namespace {
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/// Pass that moves the kernel of each LaunchOp into its separate nested module.
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///
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/// This pass moves the kernel code of each LaunchOp into a function created
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/// inside a nested module. It also creates an external function of the same
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/// name in the parent module.
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///
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/// The gpu.modules are intended to be compiled to a cubin blob independently in
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/// a separate pass. The external functions can then be annotated with the
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/// symbol of the cubin accessor function.
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class GpuKernelOutliningPass : public ModulePass<GpuKernelOutliningPass> {
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public:
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void runOnModule() override {
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SymbolTable symbolTable(getModule());
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bool modified = false;
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for (auto func : getModule().getOps<FuncOp>()) {
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// Insert just after the function.
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Block::iterator insertPt(func.getOperation()->getNextNode());
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func.walk([&](gpu::LaunchOp op) {
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llvm::SetVector<Value> operands;
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gpu::GPUFuncOp outlinedFunc = outlineKernelFunc(op, operands);
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// Create nested module and insert outlinedFunc. The module will
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// originally get the same name as the function, but may be renamed on
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// insertion into the parent module.
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auto kernelModule = createKernelModule(outlinedFunc, symbolTable);
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symbolTable.insert(kernelModule, insertPt);
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// Potentially changes signature, pulling in constants.
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convertToLaunchFuncOp(op, outlinedFunc, operands.getArrayRef());
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modified = true;
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});
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}
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// If any new module was inserted in this module, annotate this module as
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// a container module.
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if (modified)
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getModule().setAttr(gpu::GPUDialect::getContainerModuleAttrName(),
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UnitAttr::get(&getContext()));
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}
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private:
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// Returns a gpu.module containing kernelFunc and all callees (recursive).
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gpu::GPUModuleOp createKernelModule(gpu::GPUFuncOp kernelFunc,
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const SymbolTable &parentSymbolTable) {
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// TODO: This code cannot use an OpBuilder because it must be inserted into
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// a SymbolTable by the caller. SymbolTable needs to be refactored to
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// prevent manual building of Ops with symbols in code using SymbolTables
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// and then this needs to use the OpBuilder.
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auto context = getModule().getContext();
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Builder builder(context);
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OperationState state(kernelFunc.getLoc(),
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gpu::GPUModuleOp::getOperationName());
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gpu::GPUModuleOp::build(&builder, state, kernelFunc.getName());
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auto kernelModule = cast<gpu::GPUModuleOp>(Operation::create(state));
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SymbolTable symbolTable(kernelModule);
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symbolTable.insert(kernelFunc);
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SmallVector<Operation *, 8> symbolDefWorklist = {kernelFunc};
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while (!symbolDefWorklist.empty()) {
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if (Optional<SymbolTable::UseRange> symbolUses =
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SymbolTable::getSymbolUses(symbolDefWorklist.pop_back_val())) {
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for (SymbolTable::SymbolUse symbolUse : *symbolUses) {
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StringRef symbolName =
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symbolUse.getSymbolRef().cast<FlatSymbolRefAttr>().getValue();
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if (symbolTable.lookup(symbolName))
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continue;
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Operation *symbolDefClone =
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parentSymbolTable.lookup(symbolName)->clone();
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symbolDefWorklist.push_back(symbolDefClone);
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symbolTable.insert(symbolDefClone);
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}
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}
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}
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return kernelModule;
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}
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};
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} // namespace
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std::unique_ptr<OpPassBase<ModuleOp>> mlir::createGpuKernelOutliningPass() {
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return std::make_unique<GpuKernelOutliningPass>();
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}
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static PassRegistration<GpuKernelOutliningPass>
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pass("gpu-kernel-outlining",
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"Outline gpu.launch bodies to kernel functions.");
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