322 lines
13 KiB
C++
322 lines
13 KiB
C++
//===- SubgroupReduceLowering.cpp - subgroup_reduce lowering patterns -----===//
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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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// Implements gradual lowering of `gpu.subgroup_reduce` ops.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/GPU/IR/GPUDialect.h"
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#include "mlir/Dialect/GPU/Transforms/Passes.h"
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#include "mlir/Dialect/GPU/Transforms/Utils.h"
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#include "mlir/Dialect/Vector/IR/VectorOps.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Location.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/Support/LogicalResult.h"
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#include "llvm/Support/FormatVariadic.h"
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#include "llvm/Support/MathExtras.h"
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#include <cassert>
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#include <cstdint>
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using namespace mlir;
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namespace {
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/// Example, assumes `maxShuffleBitwidth` equal to 32:
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/// ```
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/// %a = gpu.subgroup_reduce add %x : (vector<3xf16>) -> vector<3xf16>
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/// ==>
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/// %v0 = arith.constant dense<0.0> : vector<3xf16>
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/// %e0 = vector.extract_strided_slice %x
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/// {offsets = [0], sizes = [2], strides = [1}: vector<3xf32> to vector<2xf32>
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/// %r0 = gpu.subgroup_reduce add %e0 : (vector<2xf16>) -> vector<2xf16>
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/// %v1 = vector.insert_strided_slice %r0, %v0
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/// {offsets = [0], strides = [1}: vector<2xf32> into vector<3xf32>
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/// %e1 = vector.extract %x[2] : f16 from vector<2xf16>
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/// %r1 = gpu.subgroup_reduce add %e1 : (f16) -> f16
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/// %a = vector.insert %r1, %v1[2] : f16 into vector<3xf16>
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/// ```
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struct BreakDownSubgroupReduce final : OpRewritePattern<gpu::SubgroupReduceOp> {
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BreakDownSubgroupReduce(MLIRContext *ctx, unsigned maxShuffleBitwidth,
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PatternBenefit benefit)
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: OpRewritePattern(ctx, benefit), maxShuffleBitwidth(maxShuffleBitwidth) {
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}
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LogicalResult matchAndRewrite(gpu::SubgroupReduceOp op,
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PatternRewriter &rewriter) const override {
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auto vecTy = dyn_cast<VectorType>(op.getType());
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if (!vecTy || vecTy.getNumElements() < 2)
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return rewriter.notifyMatchFailure(op, "not a multi-element reduction");
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assert(vecTy.getRank() == 1 && "Unexpected vector type");
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assert(!vecTy.isScalable() && "Unexpected vector type");
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Type elemTy = vecTy.getElementType();
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unsigned elemBitwidth = elemTy.getIntOrFloatBitWidth();
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if (elemBitwidth >= maxShuffleBitwidth)
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return rewriter.notifyMatchFailure(
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op, llvm::formatv("element type too large ({0}), cannot break down "
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"into vectors of bitwidth {1} or less",
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elemBitwidth, maxShuffleBitwidth));
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unsigned elementsPerShuffle = maxShuffleBitwidth / elemBitwidth;
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assert(elementsPerShuffle >= 1);
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unsigned numNewReductions =
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llvm::divideCeil(vecTy.getNumElements(), elementsPerShuffle);
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assert(numNewReductions >= 1);
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if (numNewReductions == 1)
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return rewriter.notifyMatchFailure(op, "nothing to break down");
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Location loc = op.getLoc();
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Value res =
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rewriter.create<arith::ConstantOp>(loc, rewriter.getZeroAttr(vecTy));
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for (unsigned i = 0; i != numNewReductions; ++i) {
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int64_t startIdx = i * elementsPerShuffle;
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int64_t endIdx =
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std::min(startIdx + elementsPerShuffle, vecTy.getNumElements());
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int64_t numElems = endIdx - startIdx;
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Value extracted;
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if (numElems == 1) {
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extracted =
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rewriter.create<vector::ExtractOp>(loc, op.getValue(), startIdx);
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} else {
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extracted = rewriter.create<vector::ExtractStridedSliceOp>(
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loc, op.getValue(), /*offsets=*/startIdx, /*sizes=*/numElems,
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/*strides=*/1);
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}
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Value reduce = rewriter.create<gpu::SubgroupReduceOp>(
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loc, extracted, op.getOp(), op.getUniform());
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if (numElems == 1) {
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res = rewriter.create<vector::InsertOp>(loc, reduce, res, startIdx);
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continue;
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}
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res = rewriter.create<vector::InsertStridedSliceOp>(
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loc, reduce, res, /*offsets=*/startIdx, /*strides=*/1);
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}
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rewriter.replaceOp(op, res);
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return success();
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}
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private:
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unsigned maxShuffleBitwidth = 0;
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};
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/// Example:
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/// ```
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/// %a = gpu.subgroup_reduce add %x : (vector<1xf32>) -> vector<1xf32>
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/// ==>
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/// %e0 = vector.extract %x[0] : f32 from vector<1xf32>
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/// %r0 = gpu.subgroup_reduce add %e0 : (f32) -> f32
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/// %a = vector.broadcast %r0 : f32 to vector<1xf32>
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/// ```
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struct ScalarizeSingleElementReduce final
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: OpRewritePattern<gpu::SubgroupReduceOp> {
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(gpu::SubgroupReduceOp op,
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PatternRewriter &rewriter) const override {
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auto vecTy = dyn_cast<VectorType>(op.getType());
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if (!vecTy || vecTy.getNumElements() != 1)
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return rewriter.notifyMatchFailure(op, "not a single-element reduction");
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assert(vecTy.getRank() == 1 && "Unexpected vector type");
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assert(!vecTy.isScalable() && "Unexpected vector type");
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Location loc = op.getLoc();
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Value extracted = rewriter.create<vector::ExtractOp>(loc, op.getValue(), 0);
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Value reduce = rewriter.create<gpu::SubgroupReduceOp>(
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loc, extracted, op.getOp(), op.getUniform());
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rewriter.replaceOpWithNewOp<vector::BroadcastOp>(op, vecTy, reduce);
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return success();
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}
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};
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/// Emits a subgroup reduction using a sequence of shuffles. Uses the `packFn`
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/// and `unpackFn` to convert to the native shuffle type and to the reduction
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/// type, respectively. For example, with `input` of type `f16`, `packFn` could
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/// build ops to cast the value to `i32` to perform shuffles, while `unpackFn`
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/// would cast it back to `f16` to perform arithmetic reduction on. Assumes that
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/// the subgroup is `subgroupSize` lanes wide and reduces across all of them.
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static Value createSubgroupShuffleReduction(
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OpBuilder &builder, Location loc, Value input, gpu::AllReduceOperation mode,
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unsigned subgroupSize, function_ref<Value(Value)> packFn,
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function_ref<Value(Value)> unpackFn) {
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assert(llvm::isPowerOf2_32(subgroupSize));
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// Lane value always stays in the original type. We use it to perform arith
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// reductions.
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Value laneVal = input;
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// Parallel reduction using butterfly shuffles.
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for (unsigned i = 1; i < subgroupSize; i <<= 1) {
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Value shuffled = builder
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.create<gpu::ShuffleOp>(loc, packFn(laneVal), i,
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/*width=*/subgroupSize,
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/*mode=*/gpu::ShuffleMode::XOR)
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.getShuffleResult();
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laneVal = vector::makeArithReduction(builder, loc,
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gpu::convertReductionKind(mode),
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laneVal, unpackFn(shuffled));
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assert(laneVal.getType() == input.getType());
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}
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return laneVal;
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}
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/// Lowers scalar gpu subgroup reductions to a series of shuffles.
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struct ScalarSubgroupReduceToShuffles final
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: OpRewritePattern<gpu::SubgroupReduceOp> {
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ScalarSubgroupReduceToShuffles(MLIRContext *ctx, unsigned subgroupSize,
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unsigned shuffleBitwidth,
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PatternBenefit benefit)
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: OpRewritePattern(ctx, benefit), subgroupSize(subgroupSize),
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shuffleBitwidth(shuffleBitwidth) {}
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LogicalResult matchAndRewrite(gpu::SubgroupReduceOp op,
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PatternRewriter &rewriter) const override {
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Type valueTy = op.getType();
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unsigned elemBitwidth =
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getElementTypeOrSelf(valueTy).getIntOrFloatBitWidth();
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if (!valueTy.isIntOrFloat() || elemBitwidth > shuffleBitwidth)
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return rewriter.notifyMatchFailure(
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op, "value type is not a compatible scalar");
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Location loc = op.getLoc();
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// Since this is already a native shuffle scalar, no packing is necessary.
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if (elemBitwidth == shuffleBitwidth) {
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auto identityFn = [](Value v) { return v; };
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rewriter.replaceOp(op, createSubgroupShuffleReduction(
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rewriter, loc, op.getValue(), op.getOp(),
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subgroupSize, identityFn, identityFn));
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return success();
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}
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auto shuffleIntType = rewriter.getIntegerType(shuffleBitwidth);
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auto equivIntType = rewriter.getIntegerType(elemBitwidth);
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auto packFn = [loc, &rewriter, equivIntType,
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shuffleIntType](Value unpackedVal) -> Value {
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auto asInt =
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rewriter.create<arith::BitcastOp>(loc, equivIntType, unpackedVal);
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return rewriter.create<arith::ExtUIOp>(loc, shuffleIntType, asInt);
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};
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auto unpackFn = [loc, &rewriter, equivIntType,
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valueTy](Value packedVal) -> Value {
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auto asInt =
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rewriter.create<arith::TruncIOp>(loc, equivIntType, packedVal);
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return rewriter.create<arith::BitcastOp>(loc, valueTy, asInt);
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};
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rewriter.replaceOp(op, createSubgroupShuffleReduction(
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rewriter, loc, op.getValue(), op.getOp(),
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subgroupSize, packFn, unpackFn));
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return success();
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}
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private:
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unsigned subgroupSize = 0;
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unsigned shuffleBitwidth = 0;
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};
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/// Lowers vector gpu subgroup reductions to a series of shuffles.
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struct VectorSubgroupReduceToShuffles final
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: OpRewritePattern<gpu::SubgroupReduceOp> {
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VectorSubgroupReduceToShuffles(MLIRContext *ctx, unsigned subgroupSize,
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unsigned shuffleBitwidth,
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PatternBenefit benefit)
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: OpRewritePattern(ctx, benefit), subgroupSize(subgroupSize),
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shuffleBitwidth(shuffleBitwidth) {}
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LogicalResult matchAndRewrite(gpu::SubgroupReduceOp op,
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PatternRewriter &rewriter) const override {
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auto vecTy = dyn_cast<VectorType>(op.getType());
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if (!vecTy)
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return rewriter.notifyMatchFailure(op, "value type is not a vector");
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unsigned vecBitwidth =
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vecTy.getNumElements() * vecTy.getElementTypeBitWidth();
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if (vecBitwidth > shuffleBitwidth)
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return rewriter.notifyMatchFailure(
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op,
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llvm::formatv("vector type bitwidth too large ({0}), cannot lower "
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"to shuffles of size {1}",
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vecBitwidth, shuffleBitwidth));
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unsigned elementsPerShuffle =
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shuffleBitwidth / vecTy.getElementTypeBitWidth();
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if (elementsPerShuffle * vecTy.getElementTypeBitWidth() != shuffleBitwidth)
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return rewriter.notifyMatchFailure(
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op, "shuffle bitwidth is not a multiple of the element bitwidth");
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Location loc = op.getLoc();
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// If the reduced type is smaller than the native shuffle size, extend it,
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// perform the shuffles, and extract at the end.
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auto extendedVecTy = VectorType::get(
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static_cast<int64_t>(elementsPerShuffle), vecTy.getElementType());
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Value extendedInput = op.getValue();
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if (vecBitwidth < shuffleBitwidth) {
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auto zero = rewriter.create<arith::ConstantOp>(
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loc, rewriter.getZeroAttr(extendedVecTy));
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extendedInput = rewriter.create<vector::InsertStridedSliceOp>(
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loc, extendedInput, zero, /*offsets=*/0, /*strides=*/1);
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}
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auto shuffleIntType = rewriter.getIntegerType(shuffleBitwidth);
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auto shuffleVecType = VectorType::get(1, shuffleIntType);
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auto packFn = [loc, &rewriter, shuffleVecType](Value unpackedVal) -> Value {
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auto asIntVec =
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rewriter.create<vector::BitCastOp>(loc, shuffleVecType, unpackedVal);
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return rewriter.create<vector::ExtractOp>(loc, asIntVec, 0);
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};
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auto unpackFn = [loc, &rewriter, shuffleVecType,
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extendedVecTy](Value packedVal) -> Value {
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auto asIntVec =
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rewriter.create<vector::BroadcastOp>(loc, shuffleVecType, packedVal);
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return rewriter.create<vector::BitCastOp>(loc, extendedVecTy, asIntVec);
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};
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Value res =
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createSubgroupShuffleReduction(rewriter, loc, extendedInput, op.getOp(),
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subgroupSize, packFn, unpackFn);
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if (vecBitwidth < shuffleBitwidth) {
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res = rewriter.create<vector::ExtractStridedSliceOp>(
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loc, res, /*offsets=*/0, /*sizes=*/vecTy.getNumElements(),
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/*strides=*/1);
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}
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rewriter.replaceOp(op, res);
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return success();
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}
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private:
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unsigned subgroupSize = 0;
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unsigned shuffleBitwidth = 0;
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};
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} // namespace
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void mlir::populateGpuBreakDownSubgrupReducePatterns(
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RewritePatternSet &patterns, unsigned maxShuffleBitwidth,
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PatternBenefit benefit) {
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patterns.add<BreakDownSubgroupReduce>(patterns.getContext(),
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maxShuffleBitwidth, benefit);
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patterns.add<ScalarizeSingleElementReduce>(patterns.getContext(), benefit);
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}
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void mlir::populateGpuLowerSubgroupReduceToShufflePattenrs(
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RewritePatternSet &patterns, unsigned subgroupSize,
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unsigned shuffleBitwidth, PatternBenefit benefit) {
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patterns.add<ScalarSubgroupReduceToShuffles, VectorSubgroupReduceToShuffles>(
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patterns.getContext(), subgroupSize, shuffleBitwidth, benefit);
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}
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