[BACKEND] Improve and simplify ReduceOp's lowering

We implement a LinearLayout-based `ReduceOp` lowering. This has a
number of benefits:

- The logic is noticeably simpler as we barely have to implement anything. ConvertLayout and some LL helpers do all the heavy lifting
- We get shmem swizzling for free
- We sometimes save a shmem round-trip (before we did it unconditionally)
- It is now clear that we have a `tmpLl` variable we can carefully choose (we'll do so in a future PR)
- It opens the door to returning an arbitrary layout (fusing a `convert_layout` into this op)
- It is now really simple to generalise this op to perform cross-cluster reductions, provided that `convert_layout` supports them.
- We fix some latent issues the previous implementation had when run on arbitrary linear layouts. We add a funky regression test that used to fail and now passes.
- All this while being LOC-neutral!

In future PRs we will improve the choice fo `tmpLl` to avoid in many
cases the last `convert_layout`, and we will pack the inputs in shmem to
be able to vectorize the load/stores for full reductions with multiple inputs.

This PR was the result of quite a long (but rather successful) vibe-coding session together with `gpt-5.2-codex`. I found particularly useful being able to emit a ConvertLayout within this lowering rather than having to call the lowering of the function manually. This simplifies the code quite a bit and I would have struggled to convince MLIR to do so myself.

Author: lezcano

include/triton/Analysis/Utility.h

@@ -42,30 +42,34 @@ class ReduceOpHelper {
     }
   }
 
-  ArrayRef<int64_t> getSrcShape() { return srcShape; }
-
-  Attribute getSrcLayout() { return srcEncoding; }
-
-  triton::ReduceOp getOperation() { return op; }
-
-  unsigned getThreadOffsetOnReductionAxis();
+  RankedTensorType getSrcTy() { return srcTy; }
 
   bool isWarpSynchronous();
 
   unsigned getInterWarpSizeWithUniqueData();
 
   unsigned getIntraWarpSizeWithUniqueData();
 
-  // The shape of the shared memory space needed for the reduction.
-  SmallVector<unsigned> getScratchRepShape();
+  bool isReduceWithinCTA();
+
+  bool isAssociative();
 
-  SmallVector<unsigned> getOrderWithAxisAtBeginning();
+  static triton::ColumnAction
+  makeAxisContiguous(const triton::LinearLayout &layout, int axis);
 
-  unsigned getScratchSizeInBytes();
+  static triton::LinearLayout
+  zeroBasesAlongDimAndReorder(const triton::LinearLayout &layout, unsigned axis,
+                              mlir::StringAttr dim);
 
-  bool isReduceWithinCTA();
+  static triton::LinearLayout getInterLayout(const triton::LinearLayout &layout,
+                                             unsigned axis);
 
-  bool isAssociative();
+  static triton::LinearLayout reducedRegLaneLayout(RankedTensorType srcTy,
+                                                   unsigned axis);
+
+  SmallVector<unsigned>
+  getScratchBytesForCvt(const triton::LinearLayout &srcLayout,
+                        const triton::LinearLayout &dstLayout);
 
 private:
   triton::ReduceOp op;

include/triton/Conversion/TritonGPUToLLVM/TargetInfoBase.h

@@ -2,6 +2,7 @@
 #define TRITON_CONVERSION_TRITONGPU_TO_LLVM_TARGETINFOBASE_H
 
 #include "triton/Conversion/MLIRTypes.h"
+#include "llvm/ADT/ArrayRef.h"
 
 namespace mlir::triton {
 enum class ProgramIDDim : uint32_t;
@@ -63,8 +64,7 @@ class TargetInfoBase {
 
   virtual bool warpReduce(RewriterBase &rewriter, Location loc,
                           SmallVector<Value> &acc, triton::ReduceOp op,
-                          unsigned numLaneToReduce,
-                          unsigned interleave) const = 0;
+                          unsigned activeLanes) const = 0;
 
   virtual std::string getMulhiFuncName(Type resultElementTy) const = 0;
   // Emits LLVM code with |rewriter| to print a message following the given

lib/Analysis/Allocation.cpp

@@ -2,6 +2,7 @@
 
 #include <algorithm>
 #include <limits>
+#include <numeric>
 
 #include "mlir/Analysis/Liveness.h"
 #include "mlir/Support/LLVM.h"
@@ -14,6 +15,7 @@
 #include "triton/Tools/LayoutUtils.h"
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/Support/Debug.h"
+#include "llvm/Support/MathExtras.h"
 #include "llvm/Support/raw_ostream.h"
 
 #define DEBUG_TYPE "allocation-shared-memory"
@@ -42,6 +44,36 @@ unsigned getNumScratchElemsSwizzledCvt(RankedTensorType srcTy,
   return smem.getTotalOutDimSize() / reps;
 }
 
+namespace {
+constexpr int64_t kReduceScratchAlign = 16;
+
+Type getReduceMemElemTy(Type elemTy, MLIRContext *ctx) {
+  if (elemTy.isIntOrFloat() && elemTy.getIntOrFloatBitWidth() < 8)
+    return IntegerType::get(ctx, 8);
+  return elemTy;
+}
+
+int64_t getReduceScratchSizeBytes(triton::ReduceOp op,
+                                  ArrayRef<unsigned> bytesPerOperand) {
+  std::vector<unsigned> indices(op.getNumOperands());
+  std::iota(indices.begin(), indices.end(), 0);
+  auto *ctx = op.getContext();
+  std::sort(indices.begin(), indices.end(), [&](unsigned i, unsigned j) {
+    auto lhsTy = getReduceMemElemTy(op.getElementTypes()[i], ctx);
+    auto rhsTy = getReduceMemElemTy(op.getElementTypes()[j], ctx);
+    return getIntOrFloatOrPtrBitWidth(lhsTy) >
+           getIntOrFloatOrPtrBitWidth(rhsTy);
+  });
+  // Aling to 16 bytes to allow for vectorisation
+  int64_t offset = 0;
+  for (unsigned idx : indices) {
+    offset += llvm::alignTo(bytesPerOperand[idx], kReduceScratchAlign);
+  }
+  return offset;
+}
+
+} // namespace
+
 // Both `atomic_cas` and `atomic_rmw` may need scratch memory to store values
 // because Triton's block-based programming model ensures that
 // all threads sharing the same partition of the tensor see the same values,
@@ -69,7 +101,14 @@ static SmallVector<unsigned> getRepShapeForAtomic(Value result) {
 unsigned defaultAllocationAnalysisScratchSizeFn(Operation *op) {
   if (auto reduceOp = dyn_cast<ReduceOp>(op)) {
     ReduceOpHelper helper(reduceOp);
-    return helper.getScratchSizeInBytes();
+    if (helper.isWarpSynchronous())
+      return 0;
+
+    auto regLl = ReduceOpHelper::reducedRegLaneLayout(helper.getSrcTy(),
+                                                      reduceOp.getAxis());
+    auto tmpLl = ReduceOpHelper::getInterLayout(regLl, reduceOp.getAxis());
+    auto bytesRegToTmp = helper.getScratchBytesForCvt(regLl, tmpLl);
+    return getReduceScratchSizeBytes(reduceOp, bytesRegToTmp);
   }
   if (auto scanOp = dyn_cast<ScanOp>(op)) {
     ScanLoweringHelper helper(scanOp);

lib/Analysis/Utility.cpp

@@ -23,33 +23,6 @@ namespace mlir {
 using namespace triton;
 using namespace triton::gpu;
 
-SmallVector<unsigned> ReduceOpHelper::getOrderWithAxisAtBeginning() {
-  auto order = toLinearEncoding(srcTy).getOrder();
-  auto it = std::find(order.begin(), order.end(), axis);
-  // delete the axis from order
-  order.erase(it);
-  // insert axis at the beginning of order
-  order.insert(order.begin(), axis);
-  return order;
-}
-
-// Thread offset is the thread index offset of two adjacent threads on the
-// reduction axis within the warp.
-unsigned ReduceOpHelper::getThreadOffsetOnReductionAxis() {
-  auto *ctx = srcEncoding.getContext();
-  auto linearLayout = toLinearLayout(srcTy);
-  auto kLane = mlir::StringAttr::get(ctx, "lane");
-  const auto &bases = linearLayout.getBases();
-  const auto &lanes = bases.find(kLane)->second;
-  auto offset = 1;
-  for (const auto &lane : lanes) {
-    if (lane[axis] != 0)
-      break;
-    offset *= 2;
-  }
-  return offset;
-}
-
 // Cases where distributed shared memory is not required in ConvertLayout:
 // (1) numCTAs == 1
 // (2) numCTAs > 1 but srcCGALayout == dstCGALayout
@@ -107,29 +80,6 @@ bool ReduceOpHelper::isWarpSynchronous() {
   return getWarpsPerCTA(srcEncoding, srcShape)[axis] == 1;
 }
 
-SmallVector<unsigned> ReduceOpHelper::getScratchRepShape() {
-  SmallVector<unsigned> smemShape;
-  // This case doesn't need inter-warp communication
-  if (isWarpSynchronous())
-    return {0, 0};
-
-  smemShape = convertType<unsigned>(srcShape);
-  smemShape[axis] = getInterWarpSizeWithUniqueData();
-
-  return smemShape;
-}
-
-unsigned ReduceOpHelper::getScratchSizeInBytes() {
-  auto smemShape = getScratchRepShape();
-  auto elems = product<unsigned>(smemShape);
-
-  unsigned bytesPerElem = 0;
-  for (const auto &ty : srcElementTypes) {
-    bytesPerElem += ceil<unsigned>(ty.getIntOrFloatBitWidth(), 8);
-  }
-  return bytesPerElem * elems;
-}
-
 bool ReduceOpHelper::isReduceWithinCTA() {
   // TODO: Support reduce across CTAS
   // Layout optimization passes such as PlanCTAPass and
@@ -157,6 +107,120 @@ bool ReduceOpHelper::isAssociative() {
   return !hasNoAssociativeOp;
 }
 
+ColumnAction ReduceOpHelper::makeAxisContiguous(const LinearLayout &layout,
+                                                int axis) {
+  auto *ctx = layout.getOutDimNames().begin()->getContext();
+  auto kReg = StringAttr::get(ctx, "register");
+  const auto &bases = layout.getBases().lookup(kReg);
+  SmallVector<size_t> perm;
+  SmallVector<size_t> back;
+  for (size_t i = 0; i < bases.size(); ++i) {
+    if (bases[i][axis] != 0)
+      perm.push_back(i);
+    else
+      back.push_back(i);
+  }
+  perm.append(back.begin(), back.end());
+  return ColumnAction(perm, kReg, bases.size());
+}
+
+LinearLayout
+ReduceOpHelper::zeroBasesAlongDimAndReorder(const LinearLayout &layout,
+                                            unsigned axis, StringAttr dim) {
+  LinearLayout::BasesT newBases;
+  for (auto [inDim, bases] : layout.getBases()) {
+    std::vector<std::vector<int32_t>> newInBases = bases;
+    if (inDim == dim) {
+      for (auto &basis : newInBases)
+        basis[axis] = 0;
+    }
+    newBases[inDim] = std::move(newInBases);
+  }
+
+  int32_t nextAxisBase = 1;
+  for (auto &[inDim, inDimBases] : newBases) {
+    for (auto &basis : inDimBases) {
+      if (basis[axis] == 0)
+        continue;
+      basis[axis] = nextAxisBase;
+      nextAxisBase *= 2;
+    }
+  }
+
+  return LinearLayout(std::move(newBases), to_vector(layout.getOutDimNames()));
+}
+
+LinearLayout ReduceOpHelper::getInterLayout(const LinearLayout &layout,
+                                            unsigned axis) {
+  auto *ctx = layout.getOutDimNames().begin()->getContext();
+  auto kLane = mlir::StringAttr::get(ctx, "lane");
+  auto kWarp = mlir::StringAttr::get(ctx, "warp");
+  auto regBases = layout.getBases();
+  auto linearAttr = triton::gpu::LinearEncodingAttr::get(ctx, layout);
+  int laneBits = layout.getInDimSizeLog2(kLane);
+  int neededLaneBits = llvm::Log2_32(linearAttr.getWarpsPerCTA()[axis]);
+  // TODO move to verifier
+  assert(neededLaneBits <= laneBits && "NYI: more inter-warps than lanes");
+  // Move the warp axis bases we need to reduce into lane bases, while
+  // keeping non-axis components in their original in-dim.
+  auto &laneBases = regBases[kLane];
+  auto &warpBases = regBases[kWarp];
+  int moved = 0;
+  for (auto &warpBasis : warpBases) {
+    if (warpBasis[axis] == 0)
+      continue;
+    assert(moved < neededLaneBits && "unexpected warp axis bases count");
+    std::swap(laneBases[moved], warpBasis);
+    moved++;
+  }
+
+  return LinearLayout(std::move(regBases), to_vector(layout.getOutDimNames()));
+}
+
+LinearLayout ReduceOpHelper::reducedRegLaneLayout(RankedTensorType srcTy,
+                                                  unsigned axis) {
+  auto *ctx = srcTy.getContext();
+  auto kReg = StringAttr::get(ctx, "register");
+  auto kLane = StringAttr::get(ctx, "lane");
+  auto kWarp = StringAttr::get(ctx, "warp");
+
+  auto reduced = triton::gpu::toLinearLayout(srcTy);
+  reduced = reduced.sublayout({kReg, kLane, kWarp},
+                              to_vector(reduced.getOutDimNames()));
+  reduced = actionRemoveBroadcastedRegs(reduced).apply(reduced);
+  reduced = makeAxisContiguous(reduced, axis).apply(reduced);
+  reduced = zeroBasesAlongDimAndReorder(reduced, axis, kReg);
+  reduced = actionRemoveBroadcastedRegs(reduced).apply(reduced);
+  reduced = zeroBasesAlongDimAndReorder(reduced, axis, kLane);
+  return reduced;
+}
+
+SmallVector<unsigned>
+ReduceOpHelper::getScratchBytesForCvt(const LinearLayout &srcLayout,
+                                      const LinearLayout &dstLayout) {
+  SmallVector<unsigned> bytes(srcElementTypes.size(), 0);
+  auto *ctx = op.getContext();
+  SmallVector<int64_t> shape;
+  shape.reserve(srcLayout.getNumOutDims());
+  for (auto dim : srcLayout.getOutDimNames()) {
+    shape.push_back(srcLayout.getOutDimSize(dim));
+  }
+  auto srcEnc = triton::gpu::LinearEncodingAttr::get(ctx, srcLayout);
+  auto dstEnc = triton::gpu::LinearEncodingAttr::get(ctx, dstLayout);
+  for (unsigned i = 0; i < srcElementTypes.size(); ++i) {
+    auto elemTy = srcElementTypes[i];
+    if (elemTy.isIntOrFloat() && elemTy.getIntOrFloatBitWidth() < 8)
+      elemTy = IntegerType::get(ctx, 8);
+    auto srcTy = RankedTensorType::get(shape, elemTy, srcEnc);
+    auto dstTy = RankedTensorType::get(shape, elemTy, dstEnc);
+    if (!cvtNeedsSharedMemory(srcTy, dstTy))
+      continue;
+    auto elems = getNumScratchElemsSwizzledCvt(srcTy, dstTy);
+    bytes[i] = elems * getBitwidth(srcTy) / 8;
+  }
+  return bytes;
+}
+
 ScanLoweringHelper::ScanLoweringHelper(triton::ScanOp op) : scanOp(op) {
   auto firstTy = cast<RankedTensorType>(op.getOperands()[0].getType());
   srcShape = firstTy.getShape();