llvm-capstone/mlir/lib/Dialect/GPU/Transforms/KernelOutlining.cpp
2019-09-19 14:34:30 -07:00

157 lines
6.2 KiB
C++

//===- KernelOutlining.cpp - Implementation of GPU kernel outling ---------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This file implements the GPU dialect kernel outlining pass.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/GPU/GPUDialect.h"
#include "mlir/Dialect/GPU/Passes.h"
#include "mlir/Dialect/StandardOps/Ops.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/Pass/Pass.h"
using namespace mlir;
template <typename OpTy>
static void createForAllDimensions(OpBuilder &builder, Location loc,
SmallVectorImpl<Value *> &values) {
for (StringRef dim : {"x", "y", "z"}) {
Value *v = builder.create<OpTy>(loc, builder.getIndexType(),
builder.getStringAttr(dim));
values.push_back(v);
}
}
// Add operations generating block/thread ids and gird/block dimensions at the
// beginning of `kernelFunc` and replace uses of the respective function args.
static void injectGpuIndexOperations(Location loc, FuncOp kernelFunc) {
OpBuilder OpBuilder(kernelFunc.getBody());
SmallVector<Value *, 12> indexOps;
createForAllDimensions<gpu::BlockId>(OpBuilder, loc, indexOps);
createForAllDimensions<gpu::ThreadId>(OpBuilder, loc, indexOps);
createForAllDimensions<gpu::GridDim>(OpBuilder, loc, indexOps);
createForAllDimensions<gpu::BlockDim>(OpBuilder, loc, indexOps);
// Replace the leading 12 function args with the respective thread/block index
// operations. Iterate backwards since args are erased and indices change.
for (int i = 11; i >= 0; --i) {
auto &firstBlock = kernelFunc.front();
firstBlock.getArgument(i)->replaceAllUsesWith(indexOps[i]);
firstBlock.eraseArgument(i);
}
}
// Move all constant arguments of the given kernel function into the function,
// thereby reducing the number of kernel arguments.
static gpu::LaunchFuncOp inlineConstants(FuncOp kernelFunc,
gpu::LaunchFuncOp launch) {
OpBuilder kernelBuilder(kernelFunc.getBody());
auto &firstBlock = kernelFunc.getBody().front();
llvm::SmallVector<Value *, 8> newLaunchArgs;
for (int i = launch.getNumKernelOperands() - 1; i >= 0; --i) {
auto operandOp = launch.getKernelOperand(i)->getDefiningOp();
auto constant = dyn_cast_or_null<ConstantOp>(operandOp);
if (!constant) {
newLaunchArgs.push_back(launch.getKernelOperand(i));
continue;
}
auto newConstant = kernelBuilder.clone(*operandOp);
firstBlock.getArgument(i)->replaceAllUsesWith(newConstant->getResult(0));
firstBlock.eraseArgument(i);
}
if (newLaunchArgs.size() == launch.getNumKernelOperands())
return launch;
std::reverse(newLaunchArgs.begin(), newLaunchArgs.end());
OpBuilder LaunchBuilder(launch);
SmallVector<Type, 8> newArgumentTypes;
newArgumentTypes.reserve(firstBlock.getNumArguments());
for (auto value : firstBlock.getArguments()) {
newArgumentTypes.push_back(value->getType());
}
kernelFunc.setType(LaunchBuilder.getFunctionType(newArgumentTypes, {}));
auto newLaunch = LaunchBuilder.create<gpu::LaunchFuncOp>(
launch.getLoc(), kernelFunc, launch.getGridSizeOperandValues(),
launch.getBlockSizeOperandValues(), newLaunchArgs);
launch.erase();
return newLaunch;
}
// Outline the `gpu.launch` operation body into a kernel function. Replace
// `gpu.return` operations by `std.return` in the generated functions.
static FuncOp outlineKernelFunc(gpu::LaunchOp launchOp) {
Location loc = launchOp.getLoc();
SmallVector<Type, 4> kernelOperandTypes(launchOp.getKernelOperandTypes());
FunctionType type =
FunctionType::get(kernelOperandTypes, {}, launchOp.getContext());
std::string kernelFuncName =
Twine(launchOp.getParentOfType<FuncOp>().getName(), "_kernel").str();
FuncOp outlinedFunc = FuncOp::create(loc, kernelFuncName, type);
outlinedFunc.getBody().takeBody(launchOp.getBody());
Builder builder(launchOp.getContext());
outlinedFunc.setAttr(gpu::GPUDialect::getKernelFuncAttrName(),
builder.getUnitAttr());
injectGpuIndexOperations(loc, outlinedFunc);
outlinedFunc.walk([](mlir::gpu::Return op) {
OpBuilder replacer(op);
replacer.create<ReturnOp>(op.getLoc());
op.erase();
});
return outlinedFunc;
}
// Replace `gpu.launch` operations with an `gpu.launch_func` operation launching
// `kernelFunc`. The kernel func contains the body of the `gpu.launch` with
// constant region arguments inlined.
static void convertToLaunchFuncOp(gpu::LaunchOp &launchOp, FuncOp kernelFunc) {
OpBuilder builder(launchOp);
SmallVector<Value *, 4> kernelOperandValues(
launchOp.getKernelOperandValues());
auto launchFuncOp = builder.create<gpu::LaunchFuncOp>(
launchOp.getLoc(), kernelFunc, launchOp.getGridSizeOperandValues(),
launchOp.getBlockSizeOperandValues(), kernelOperandValues);
inlineConstants(kernelFunc, launchFuncOp);
launchOp.erase();
}
namespace {
class GpuKernelOutliningPass : public ModulePass<GpuKernelOutliningPass> {
public:
void runOnModule() override {
ModuleManager moduleManager(getModule());
for (auto func : getModule().getOps<FuncOp>()) {
func.walk([&](mlir::gpu::LaunchOp op) {
FuncOp outlinedFunc = outlineKernelFunc(op);
moduleManager.insert(outlinedFunc);
convertToLaunchFuncOp(op, outlinedFunc);
});
}
}
};
} // namespace
std::unique_ptr<OpPassBase<ModuleOp>> mlir::createGpuKernelOutliningPass() {
return std::make_unique<GpuKernelOutliningPass>();
}
static PassRegistration<GpuKernelOutliningPass>
pass("gpu-kernel-outlining",
"Outline gpu.launch bodies to kernel functions.");