4bfae66d70
This change refactors and cleans up the implementation of the operation walk methods. After this refactoring is that the explicit template parameter for the operation type is no longer needed for the explicit op walks. For example:
op->walk<AffineForOp>([](AffineForOp op) { ... });
is now accomplished via:
op->walk([](AffineForOp op) { ... });
PiperOrigin-RevId: 266209552
119 lines
4.6 KiB
C++
119 lines
4.6 KiB
C++
//===- KernelOutlining.cpp - Implementation of GPU kernel outling ---------===//
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//
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// Copyright 2019 The MLIR Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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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/Pass/Pass.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 gird/block dimensions at the
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// beginning of `kernelFunc` and replace uses of the respective function args.
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static void injectGpuIndexOperations(Location loc, FuncOp kernelFunc) {
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OpBuilder OpBuilder(kernelFunc.getBody());
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SmallVector<Value *, 12> indexOps;
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createForAllDimensions<gpu::BlockId>(OpBuilder, loc, indexOps);
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createForAllDimensions<gpu::ThreadId>(OpBuilder, loc, indexOps);
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createForAllDimensions<gpu::GridDim>(OpBuilder, loc, indexOps);
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createForAllDimensions<gpu::BlockDim>(OpBuilder, 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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auto &firstBlock = kernelFunc.front();
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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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// Outline the `gpu.launch` operation body into a kernel function. Replace
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// `gpu.return` operations by `std.return` in the generated functions.
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static FuncOp outlineKernelFunc(gpu::LaunchOp launchOp) {
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Location loc = launchOp.getLoc();
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SmallVector<Type, 4> kernelOperandTypes(launchOp.getKernelOperandTypes());
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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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FuncOp outlinedFunc = FuncOp::create(loc, kernelFuncName, type);
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outlinedFunc.getBody().takeBody(launchOp.getBody());
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Builder builder(launchOp.getContext());
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outlinedFunc.setAttr(gpu::GPUDialect::getKernelFuncAttrName(),
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builder.getUnitAttr());
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injectGpuIndexOperations(loc, outlinedFunc);
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outlinedFunc.walk([](mlir::gpu::Return op) {
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OpBuilder replacer(op);
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replacer.create<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`.
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static void convertToLaunchFuncOp(gpu::LaunchOp &launchOp, FuncOp kernelFunc) {
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OpBuilder builder(launchOp);
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SmallVector<Value *, 4> kernelOperandValues(
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launchOp.getKernelOperandValues());
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builder.create<gpu::LaunchFuncOp>(
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launchOp.getLoc(), kernelFunc, launchOp.getGridSizeOperandValues(),
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launchOp.getBlockSizeOperandValues(), kernelOperandValues);
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launchOp.erase();
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}
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namespace {
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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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ModuleManager moduleManager(getModule());
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for (auto func : getModule().getOps<FuncOp>()) {
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func.walk([&](mlir::gpu::LaunchOp op) {
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FuncOp outlinedFunc = outlineKernelFunc(op);
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moduleManager.insert(outlinedFunc);
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convertToLaunchFuncOp(op, outlinedFunc);
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});
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}
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}
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};
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} // namespace
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std::unique_ptr<ModulePassBase> 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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