b5d847b1b9
"Standard-to-LLVM" conversion is one of the oldest passes in existence. It has become quite large due to the size of the Standard dialect itself, which is being split into multiple smaller dialects. Furthermore, several conversion features are useful for any dialect that is being converted to the LLVM dialect, which, without this refactoring, creates a dependency from those conversions to the "standard-to-llvm" one. Put several of the reusable utilities from this conversion to a separate library, namely: - type converter from builtin to LLVM dialect types; - utility for building and accessing values of LLVM structure type; - utility for building and accessing values that represent memref in the LLVM dialect; - lowering options applicable everywhere. Additionally, remove the type wrapping/unwrapping notion from the type converter that is no longer relevant since LLVM types has been reimplemented as first-class MLIR types. Reviewed By: pifon2a Differential Revision: https://reviews.llvm.org/D105534
258 lines
12 KiB
C++
258 lines
12 KiB
C++
//===- LowerGpuOpsToNVVMOps.cpp - MLIR GPU to NVVM lowering passes --------===//
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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 a pass to generate NVVMIR operations for higher-level
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// GPU operations.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Conversion/GPUToNVVM/GPUToNVVMPass.h"
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#include "mlir/Conversion/LLVMCommon/LoweringOptions.h"
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#include "mlir/Conversion/LLVMCommon/TypeConverter.h"
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#include "mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h"
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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/LLVMIR/NVVMDialect.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "llvm/Support/FormatVariadic.h"
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#include "../GPUCommon/GPUOpsLowering.h"
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#include "../GPUCommon/IndexIntrinsicsOpLowering.h"
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#include "../GPUCommon/OpToFuncCallLowering.h"
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#include "../PassDetail.h"
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using namespace mlir;
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namespace {
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struct GPUShuffleOpLowering : public ConvertOpToLLVMPattern<gpu::ShuffleOp> {
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using ConvertOpToLLVMPattern<gpu::ShuffleOp>::ConvertOpToLLVMPattern;
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/// Lowers a shuffle to the corresponding NVVM op.
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///
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/// Convert the `width` argument into an activeMask (a bitmask which specifies
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/// which threads participate in the shuffle) and a maskAndClamp (specifying
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/// the highest lane which participates in the shuffle).
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///
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/// %one = llvm.constant(1 : i32) : i32
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/// %shl = llvm.shl %one, %width : i32
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/// %active_mask = llvm.sub %shl, %one : i32
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/// %mask_and_clamp = llvm.sub %width, %one : i32
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/// %shfl = nvvm.shfl.sync.bfly %active_mask, %value, %offset,
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/// %mask_and_clamp : !llvm<"{ float, i1 }">
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/// %shfl_value = llvm.extractvalue %shfl[0 : index] :
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/// !llvm<"{ float, i1 }">
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/// %shfl_pred = llvm.extractvalue %shfl[1 : index] :
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/// !llvm<"{ float, i1 }">
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LogicalResult
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matchAndRewrite(gpu::ShuffleOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op->getLoc();
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gpu::ShuffleOpAdaptor adaptor(operands);
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auto valueTy = adaptor.value().getType();
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auto int32Type = IntegerType::get(rewriter.getContext(), 32);
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auto predTy = IntegerType::get(rewriter.getContext(), 1);
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auto resultTy = LLVM::LLVMStructType::getLiteral(rewriter.getContext(),
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{valueTy, predTy});
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Value one = rewriter.create<LLVM::ConstantOp>(
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loc, int32Type, rewriter.getI32IntegerAttr(1));
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// Bit mask of active lanes: `(1 << activeWidth) - 1`.
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Value activeMask = rewriter.create<LLVM::SubOp>(
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loc, int32Type,
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rewriter.create<LLVM::ShlOp>(loc, int32Type, one, adaptor.width()),
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one);
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// Clamp lane: `activeWidth - 1`
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Value maskAndClamp =
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rewriter.create<LLVM::SubOp>(loc, int32Type, adaptor.width(), one);
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auto returnValueAndIsValidAttr = rewriter.getUnitAttr();
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Value shfl = rewriter.create<NVVM::ShflBflyOp>(
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loc, resultTy, activeMask, adaptor.value(), adaptor.offset(),
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maskAndClamp, returnValueAndIsValidAttr);
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Value shflValue = rewriter.create<LLVM::ExtractValueOp>(
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loc, valueTy, shfl, rewriter.getIndexArrayAttr(0));
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Value isActiveSrcLane = rewriter.create<LLVM::ExtractValueOp>(
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loc, predTy, shfl, rewriter.getIndexArrayAttr(1));
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rewriter.replaceOp(op, {shflValue, isActiveSrcLane});
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return success();
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}
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};
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/// Import the GPU Ops to NVVM Patterns.
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#include "GPUToNVVM.cpp.inc"
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/// A pass that replaces all occurrences of GPU device operations with their
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/// corresponding NVVM equivalent.
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///
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/// This pass only handles device code and is not meant to be run on GPU host
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/// code.
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struct LowerGpuOpsToNVVMOpsPass
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: public ConvertGpuOpsToNVVMOpsBase<LowerGpuOpsToNVVMOpsPass> {
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LowerGpuOpsToNVVMOpsPass() = default;
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LowerGpuOpsToNVVMOpsPass(unsigned indexBitwidth) {
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this->indexBitwidth = indexBitwidth;
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}
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void runOnOperation() override {
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gpu::GPUModuleOp m = getOperation();
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/// Customize the bitwidth used for the device side index computations.
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LowerToLLVMOptions options(
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m.getContext(),
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DataLayout(cast<DataLayoutOpInterface>(m.getOperation())));
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options.emitCWrappers = true;
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if (indexBitwidth != kDeriveIndexBitwidthFromDataLayout)
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options.overrideIndexBitwidth(indexBitwidth);
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/// MemRef conversion for GPU to NVVM lowering. The GPU dialect uses memory
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/// space 5 for private memory attributions, but NVVM represents private
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/// memory allocations as local `alloca`s in the default address space. This
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/// converter drops the private memory space to support the use case above.
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LLVMTypeConverter converter(m.getContext(), options);
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converter.addConversion([&](MemRefType type) -> Optional<Type> {
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if (type.getMemorySpaceAsInt() !=
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gpu::GPUDialect::getPrivateAddressSpace())
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return llvm::None;
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return converter.convertType(MemRefType::Builder(type).setMemorySpace(0));
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});
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// Lowering for MMAMatrixType.
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converter.addConversion([&](gpu::MMAMatrixType type) -> Type {
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// The number of items in structToReturn are dependent on the the dataType
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// and the MMA operand that this operation is associated with.
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llvm::DenseMap<StringRef, int64_t> numElemsPerThreadF16,
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numElemsPerThreadF32;
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numElemsPerThreadF16["AOp"] = 8;
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numElemsPerThreadF16["BOp"] = 8;
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numElemsPerThreadF16["COp"] = 4;
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numElemsPerThreadF32["AOp"] = 8;
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numElemsPerThreadF32["BOp"] = 8;
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numElemsPerThreadF32["COp"] = 8;
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Type structToReturn;
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if (type.getElementType().isF16()) {
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// Number of f16's in 32-bit.
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unsigned vecSize = 2;
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Type vec = VectorType::get(vecSize, FloatType::getF16(&getContext()));
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unsigned size = numElemsPerThreadF16[type.getOperand()];
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SmallVector<Type> elements(size, vec);
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structToReturn =
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LLVM::LLVMStructType::getLiteral(&getContext(), elements);
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} else if (type.getElementType().isF32()) {
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unsigned size = numElemsPerThreadF32[type.getOperand()];
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SmallVector<Type> elements(size, FloatType::getF32(&getContext()));
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structToReturn =
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LLVM::LLVMStructType::getLiteral(&getContext(), elements);
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}
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return structToReturn;
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});
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RewritePatternSet patterns(m.getContext());
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RewritePatternSet llvmPatterns(m.getContext());
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// Apply in-dialect lowering first. In-dialect lowering will replace ops
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// which need to be lowered further, which is not supported by a single
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// conversion pass.
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populateGpuRewritePatterns(patterns);
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(void)applyPatternsAndFoldGreedily(m, std::move(patterns));
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populateStdToLLVMConversionPatterns(converter, llvmPatterns);
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populateGpuToNVVMConversionPatterns(converter, llvmPatterns);
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populateGpuWMMAToNVVMConversionPatterns(converter, llvmPatterns);
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LLVMConversionTarget target(getContext());
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configureGpuToNVVMConversionLegality(target);
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if (failed(applyPartialConversion(m, target, std::move(llvmPatterns))))
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signalPassFailure();
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}
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};
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} // anonymous namespace
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void mlir::configureGpuToNVVMConversionLegality(ConversionTarget &target) {
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target.addIllegalOp<FuncOp>();
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target.addLegalDialect<::mlir::LLVM::LLVMDialect>();
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target.addLegalDialect<::mlir::NVVM::NVVMDialect>();
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target.addIllegalDialect<gpu::GPUDialect>();
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target.addIllegalOp<LLVM::CosOp, LLVM::ExpOp, LLVM::FAbsOp, LLVM::FCeilOp,
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LLVM::FFloorOp, LLVM::LogOp, LLVM::Log10Op, LLVM::Log2Op,
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LLVM::PowOp, LLVM::SinOp, LLVM::SqrtOp>();
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// TODO: Remove once we support replacing non-root ops.
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target.addLegalOp<gpu::YieldOp, gpu::GPUModuleOp, gpu::ModuleEndOp>();
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}
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void mlir::populateGpuToNVVMConversionPatterns(LLVMTypeConverter &converter,
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RewritePatternSet &patterns) {
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populateWithGenerated(patterns);
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patterns
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.add<GPUIndexIntrinsicOpLowering<gpu::ThreadIdOp, NVVM::ThreadIdXOp,
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NVVM::ThreadIdYOp, NVVM::ThreadIdZOp>,
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GPUIndexIntrinsicOpLowering<gpu::BlockDimOp, NVVM::BlockDimXOp,
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NVVM::BlockDimYOp, NVVM::BlockDimZOp>,
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GPUIndexIntrinsicOpLowering<gpu::BlockIdOp, NVVM::BlockIdXOp,
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NVVM::BlockIdYOp, NVVM::BlockIdZOp>,
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GPUIndexIntrinsicOpLowering<gpu::GridDimOp, NVVM::GridDimXOp,
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NVVM::GridDimYOp, NVVM::GridDimZOp>,
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GPUShuffleOpLowering, GPUReturnOpLowering>(converter);
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// Explicitly drop memory space when lowering private memory
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// attributions since NVVM models it as `alloca`s in the default
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// memory space and does not support `alloca`s with addrspace(5).
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patterns.add<GPUFuncOpLowering>(
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converter, /*allocaAddrSpace=*/0,
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Identifier::get(NVVM::NVVMDialect::getKernelFuncAttrName(),
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&converter.getContext()));
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patterns.add<OpToFuncCallLowering<AbsFOp>>(converter, "__nv_fabsf",
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"__nv_fabs");
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patterns.add<OpToFuncCallLowering<math::AtanOp>>(converter, "__nv_atanf",
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"__nv_atan");
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patterns.add<OpToFuncCallLowering<math::Atan2Op>>(converter, "__nv_atan2f",
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"__nv_atan2");
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patterns.add<OpToFuncCallLowering<CeilFOp>>(converter, "__nv_ceilf",
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"__nv_ceil");
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patterns.add<OpToFuncCallLowering<math::CosOp>>(converter, "__nv_cosf",
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"__nv_cos");
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patterns.add<OpToFuncCallLowering<math::ExpOp>>(converter, "__nv_expf",
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"__nv_exp");
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patterns.add<OpToFuncCallLowering<math::ExpM1Op>>(converter, "__nv_expm1f",
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"__nv_expm1");
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patterns.add<OpToFuncCallLowering<FloorFOp>>(converter, "__nv_floorf",
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"__nv_floor");
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patterns.add<OpToFuncCallLowering<math::LogOp>>(converter, "__nv_logf",
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"__nv_log");
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patterns.add<OpToFuncCallLowering<math::Log1pOp>>(converter, "__nv_log1pf",
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"__nv_log1p");
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patterns.add<OpToFuncCallLowering<math::Log10Op>>(converter, "__nv_log10f",
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"__nv_log10");
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patterns.add<OpToFuncCallLowering<math::Log2Op>>(converter, "__nv_log2f",
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"__nv_log2");
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patterns.add<OpToFuncCallLowering<math::PowFOp>>(converter, "__nv_powf",
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"__nv_pow");
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patterns.add<OpToFuncCallLowering<math::RsqrtOp>>(converter, "__nv_rsqrtf",
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"__nv_rsqrt");
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patterns.add<OpToFuncCallLowering<math::SinOp>>(converter, "__nv_sinf",
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"__nv_sin");
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patterns.add<OpToFuncCallLowering<math::SqrtOp>>(converter, "__nv_sqrtf",
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"__nv_sqrt");
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patterns.add<OpToFuncCallLowering<math::TanhOp>>(converter, "__nv_tanhf",
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"__nv_tanh");
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}
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std::unique_ptr<OperationPass<gpu::GPUModuleOp>>
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mlir::createLowerGpuOpsToNVVMOpsPass(unsigned indexBitwidth) {
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return std::make_unique<LowerGpuOpsToNVVMOpsPass>(indexBitwidth);
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}
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