Fix VNNI affine maps

include/TPP/IR/MatcherUtils.h

@@ -61,10 +61,11 @@ bool isTwoDFillOpWithZeros(linalg::LinalgOp linalgOp,
                            SmallVectorImpl<Value> *capturedOperands = nullptr);
 
 // Return a pair where the first member is true if and only if the operation
-// represents a brgemm in VNNI layout. The second member tells if the brgemm has
-// the batch dimension; it has meaning only if the first field is valid.
+// represents a matmul (GEMM or BRGEMM) in VNNI layout. The second member tells
+// if the brgemm has the batch dimension; it has meaning only if the first field
+// is valid.
 std::pair<bool, bool>
-isBrgemmVnniOp(linalg::GenericOp linalgOp,
+isMatmulVnniOp(linalg::GenericOp linalgOp,
                SmallVectorImpl<Value> *capturedOperands = nullptr);
 
 } // namespace utils

include/TPP/Transforms/Utils/VNNIUtils.h

@@ -22,7 +22,7 @@ class AffineMap;
 class VectorType;
 
 namespace linalg {
-class GenericOp;
+class LinalgOp;
 } // namespace linalg
 
 namespace vnni {
@@ -46,11 +46,10 @@ bool isInVnniLayout(VnniOperandRank expectedRank, VectorType vector);
 
 bool isInVnniLayout(int64_t expectedRank, VectorType vector);
 
-// Return the first AffineDimExpr in the map `affineMap`
-// with a VNNI layout pattern (AffineDimExpr floordiv VNNI).
-FailureOr<AffineDimExpr> isInVnniLayout(linalg::GenericOp linalgOp,
-                                        AffineMap affineMap,
-                                        int64_t blockingFactor);
+// Return true if the operation is in VNNI layout.
+// Optionally, the check can be constrained to a specific VNNI blocking factor.
+bool isInVnniLayout(linalg::LinalgOp linalgOp,
+                    std::optional<int64_t> blockingFactor = std::nullopt);
 
 } // namespace utils
 } // namespace vnni

lib/TPP/Conversion/ConvertLinalgToXsmm/ConvertLinalgToXsmm.cpp

@@ -453,6 +453,21 @@ static void replaceOpWithGemmLikeOp(RewriterBase &rewriter,
   }
   auto flags = rewriter.getArrayAttr(gemmFlags);
   SmallVector<Value> invokeOperands;
+  SmallVector<Value> inputs = {linalgOp->getOperands()};
+
+  // Collapse VNNI factor dimension for matrix A:
+  // A <32x8x2> -> A <32x16>
+  if (brgemmInfo.isVnni) {
+    auto rankA = cast<ShapedType>(inputs[0].getType()).getRank();
+    assert(rankA >= 3 && "Invalid A mat rank for VNNI");
+    SmallVector<ReassociationIndices> reassoc;
+    for (int64_t index = 0; index < rankA - 2; index++)
+      reassoc.push_back({index});
+    reassoc.push_back(ReassociationIndices{rankA - 2, rankA - 1});
+
+    inputs[0] =
+        rewriter.create<memref::CollapseShapeOp>(loc, inputs[0], reassoc);
+  }
 
   if (batch != 0) {
     DenseI64ArrayAttr dims = DenseI64ArrayAttr::get(
@@ -463,8 +478,7 @@ static void replaceOpWithGemmLikeOp(RewriterBase &rewriter,
     Value batchDim = rewriter.create<arith::ConstantOp>(
         loc, integer64, rewriter.getIntegerAttr(integer64, batch));
     invokeOperands.push_back(dispatched);
-    invokeOperands.append(linalgOp->getOperands().begin(),
-                          linalgOp->getOperands().end());
+    invokeOperands.append(inputs);
     invokeOperands.push_back(batchDim);
     rewriter.replaceOpWithNewOp<xsmm::BrgemmOp>(linalgOp, dtype,
                                                 invokeOperands);
@@ -474,8 +488,7 @@ static void replaceOpWithGemmLikeOp(RewriterBase &rewriter,
     Value dispatched = rewriter.create<xsmm::GemmDispatchOp>(
         loc, integer64, dims, flags, dtype);
     invokeOperands.push_back(dispatched);
-    invokeOperands.append(linalgOp->getOperands().begin(),
-                          linalgOp->getOperands().end());
+    invokeOperands.append(inputs);
     rewriter.replaceOpWithNewOp<xsmm::GemmOp>(linalgOp, dtype, invokeOperands);
   }
 }
@@ -502,7 +515,7 @@ checkStructure(linalg::LinalgOp linalgOp) {
     return failure();
   }
   if (contractionDims->m.size() != 1 || contractionDims->n.size() != 1 ||
-      (contractionDims->k.size() != 2 && contractionDims->k.size() != 1) ||
+      contractionDims->k.size() > 3 || contractionDims->k.size() < 1 ||
       contractionDims->batch.size() != 0) {
     LLVM_DEBUG(llvm::dbgs() << "[checkStructure] Wrong dimensions\n");
     return failure();
@@ -575,14 +588,16 @@ static FailureOr<BrgemmInfo> checkAccess(linalg::LinalgOp linalgOp, unsigned m,
   auto loops = linalgOp.computeStaticLoopSizes();
   int64_t batchVal = (batchPos) ? loops[batchPos.value()] : 0;
 
+  bool isVnni = vnni::utils::isInVnniLayout(linalgOp);
+
   BrgemmInfo info{loops[m], loops[n], loops[k], batchVal, *lda,
-                  *ldb,     *ldc,     strideA,  strideB};
+                  *ldb,     *ldc,     strideA,  strideB,  isVnni};
   return info;
 }
 
 // Check if the given generic is mappable to a brgemm xsmm op.
 // - It is a contraction, with:
-// -- 1 m and 1 n and 2 k dimensions.
+// -- 1 m, 1 n, and 2 or 3 (VNNI) k dimensions.
 // -- m appears on the LHS and OUT but not in RHS.
 // -- n appears on the RHS and OUT but not in LHS.
 // -- k and k' appear on the RHS and LHS but not OUT.
@@ -600,8 +615,15 @@ static FailureOr<BrgemmInfo> isMappableToBrgemm(linalg::LinalgOp linalgOp) {
   unsigned m = contractionDims->m[0];
   unsigned n = contractionDims->n[0];
   unsigned k = contractionDims->k.back();
+
+  // Check if there is a batch reduce dimension.
+  // At least one K-dim is the GEMM reduction.
+  // In case of VNNI layout, there is additional reduction dimension
+  // representing VNNI blocking factor.
   std::optional<unsigned> batch;
-  if (contractionDims->k.size() == 2)
+  unsigned numBrgemmReductionDims =
+      vnni::utils::isInVnniLayout(linalgOp) ? 3 : 2;
+  if (contractionDims->k.size() == numBrgemmReductionDims)
     batch = contractionDims->k.front();
 
   LLVM_DEBUG(llvm::dbgs() << "[isMappableToBrgemm] Candidate dims: "
@@ -772,17 +794,23 @@ makeMinorDimensionsInnerMost(RewriterBase &rewriter, linalg::GenericOp linalgOp,
     return linalgOp;
   }
 
-  if (!isInnerMostDim(operandA, *minorKInCodomainOpA)) {
+  bool isVnni = vnni::utils::isInVnniLayout(linalgOp);
+
+  if (!isVnni && !isInnerMostDim(operandA, *minorKInCodomainOpA)) {
     LLVM_DEBUG(llvm::dbgs()
                << "[makeMinorDimensionsInnerMost] emit transpose for A\n");
-    assert(isInnerMostDim(operandA, *minorMInCodomainOpA));
+    if (!isInnerMostDim(operandA, *minorMInCodomainOpA))
+      return failure();
     emitTransposeOnOperand(rewriter, linalgOp, operandA, *minorKInCodomainOpA,
                            *minorMInCodomainOpA);
   }
-  if (!isInnerMostDim(operandB, *minorNInCodomainOpB)) {
+  // Do not inject transposes in case of VNNI format.
+  // Otherwise, it breaks later VNNI layout validation.
+  if (!isVnni && !isInnerMostDim(operandB, *minorNInCodomainOpB)) {
     LLVM_DEBUG(llvm::dbgs()
                << "[makeMinorDimensionsInnerMost] emit transpose for B\n");
-    assert(isInnerMostDim(operandB, *minorKInCodomainOpB));
+    if (!isInnerMostDim(operandB, *minorKInCodomainOpB))
+      return failure();
     emitTransposeOnOperand(rewriter, linalgOp, operandB, *minorKInCodomainOpB,
                            *minorNInCodomainOpB);
   }
@@ -795,7 +823,7 @@ void ConvertLinalgToXsmm::runOnOperation() {
   IRRewriter rewriter(&getContext());
 
   // Enable conversion for linalg.generic to XSMM Brgemm if possible.
-  auto res = getOperation()->walk([&](linalg::GenericOp genericOp) {
+  getOperation()->walk([&](linalg::GenericOp genericOp) {
     auto contractionDims = checkStructure(genericOp);
     // If the generic does not match the structure of a Brgemm op, skip it.
     if (failed(contractionDims))
@@ -804,22 +832,18 @@ void ConvertLinalgToXsmm::runOnOperation() {
     unsigned n = contractionDims->n[0];
     unsigned k = contractionDims->k.back();
     std::optional<unsigned> batch;
-    if (contractionDims->k.size() == 2)
+    if (contractionDims->k.size() == 3)
       contractionDims->k.front();
 
     if (failed(checkAccess(genericOp, m, n, k, batch))) {
       // The generic is a Brgemm but the strides of the selected dims (m, n, k)
       // are not unit strides. Inject transposes to bring them innermost.
       if (failed(makeMinorDimensionsInnerMost(rewriter, genericOp, m, n, k))) {
-        return WalkResult::interrupt();
+        return WalkResult::skip();
       }
     }
     return WalkResult::advance();
   });
-  if (res.wasInterrupted()) {
-    LLVM_DEBUG(llvm::dbgs() << "pass failed!\n");
-    return signalPassFailure();
-  }
   SmallVector<StringRef> skipPatterns(skipOperations.begin(),
                                       skipOperations.end());
   tpp::populateLinalgToXsmmPatterns(patterns, skipPatterns);
@@ -1069,11 +1093,11 @@ struct ConvertGenericToVnniMatmulLikeOp
       return rewriter.notifyMatchFailure(genericOp, "expects buffer semantics");
     }
 
-    auto [isBrgemmOp, hasBatch] = structured_match::utils::isBrgemmVnniOp(
+    auto [isMatmulVnni, hasBatch] = structured_match::utils::isMatmulVnniOp(
         genericOp, /*operands=*/nullptr);
-    if (!isBrgemmOp) {
+    if (!isMatmulVnni) {
       return rewriter.notifyMatchFailure(
-          genericOp, "expects an operation mappable to brgemm");
+          genericOp, "expects an operation mappable to VNNI contraction");
     }
 
     Value bufferA = genericOp.getDpsInputs()[0];
@@ -1085,7 +1109,15 @@ struct ConvertGenericToVnniMatmulLikeOp
     int64_t kPos = 1;
     if (hasBatch)
       kPos++;
-    int64_t k = cast<ShapedType>(bufferA.getType()).getShape()[kPos];
+    // Take the whole reduction dim size. Account for the VNNI factor (ensured
+    // by the earlier check) that splits the K dim in the shape.
+    std::optional<int64_t> vnniFactor =
+        vnni::utils::getVnniBlockingFactor(bufferB.getType());
+    if (!vnniFactor)
+      return rewriter.notifyMatchFailure(genericOp,
+                                         "failed to determine VNNI factor");
+    int64_t k =
+        cast<ShapedType>(bufferA.getType()).getShape()[kPos] * *vnniFactor;
     int64_t batch = 0;
     if (hasBatch)
       batch = cast<ShapedType>(bufferA.getType()).getShape()[0];
@@ -1107,8 +1139,7 @@ struct ConvertGenericToVnniMatmulLikeOp
     if (hasBatch)
       leadingDimPosOnAandB++;
     int64_t lda = (*stridesOnLhs)[leadingDimPosOnAandB];
-    int64_t ldb = (*stridesOnRhs)[leadingDimPosOnAandB] /
-                  *vnni::utils::getVnniBlockingFactor(bufferB.getType());
+    int64_t ldb = (*stridesOnRhs)[leadingDimPosOnAandB] / *vnniFactor;
     int64_t ldc = (*stridesOnOutput)[0];
 
     BrgemmInfo brgemmInfo{m,   n,   k,       batch,   lda,

lib/TPP/IR/MatcherUtils.cpp

@@ -35,9 +35,9 @@ getIteratorPos(linalg::LinalgOp linalgOp, AffineMap indexingMap,
   return res;
 }
 
-// Return true if the linalg.generic can be mapped to a brgemm in VNNI
-// format.
-std::pair<bool, bool> isBrgemmVnniOp(linalg::GenericOp linalgOp,
+// Return true if the linalg.generic can be mapped to a matmul (GEMM or BRGEMM)
+// in VNNI format.
+std::pair<bool, bool> isMatmulVnniOp(linalg::GenericOp linalgOp,
                                      SmallVectorImpl<Value> *operands) {
   bool hasBatch = false;
   auto blockingFactor =
@@ -56,7 +56,7 @@ std::pair<bool, bool> isBrgemmVnniOp(linalg::GenericOp linalgOp,
           .operation(NumOfLoops(_OR(EqualsTo(5), EqualsTo(4))))
           .input(MatchAll(), HasStaticShape())
           .output(MatchAll(), HasStaticShape())
-          .input(MatchOne(0), HasMap(BroadcastableProjectedPermutation(), &mapOperandA))
+          .input(MatchOne(0), HasMap(Any(), &mapOperandA))
           .input(MatchOne(1), HasMap(Any(), &mapOperandB))
           .output(MatchOne(0), HasMap(BroadcastableProjectedPermutation(), &mapOperandC))
           .region(MatchOne(0),
@@ -82,17 +82,18 @@ std::pair<bool, bool> isBrgemmVnniOp(linalg::GenericOp linalgOp,
 
   llvm::SmallVector<int64_t> operandAPosIterRed = getIteratorPos(
       linalgOp, mapOperandA, mlir::utils::IteratorType::reduction);
-  if (operandAPosIterRed.size() != 2 && operandAPosIterRed.size() != 1)
+  unsigned numRedItersA = operandAPosIterRed.size();
+  if (numRedItersA != 3 && numRedItersA != 2)
     return std::make_pair(false, hasBatch);
 
+  // Check if there is an extra outer batch reduce K-dim.
+  // For VNNI format:
+  //  - one inner K-dim is the GEMM reduction
+  //  - one inner K-dim is the VNNI blocking factor
   int64_t batchRedIter = std::numeric_limits<int64_t>::max();
-  int64_t kRedIter = std::numeric_limits<int64_t>::max();
-  if (operandAPosIterRed.size() == 2) {
+  if (numRedItersA == 3) {
     batchRedIter = operandAPosIterRed[0];
-    kRedIter = operandAPosIterRed[1];
     hasBatch = true;
-  } else {
-    kRedIter = operandAPosIterRed[0];
   }
 
   // Operand B: One parallel iterator (j) and three reduction ones (batch,
@@ -112,10 +113,10 @@ std::pair<bool, bool> isBrgemmVnniOp(linalg::GenericOp linalgOp,
     return std::make_pair(false, hasBatch);
   }
 
-  auto vnniDim =
-      vnni::utils::isInVnniLayout(linalgOp, mapOperandB, *blockingFactor);
-  bool isBrgemmOp = succeeded(vnniDim) && vnniDim->getPosition() == kRedIter;
-  return std::make_pair(isBrgemmOp, hasBatch);
+  // At this point, the operation is a valid matmul contraction.
+  // Finally, ensure that it is in VNNI layout.
+  bool isVnniMatmul = vnni::utils::isInVnniLayout(linalgOp, *blockingFactor);
+  return std::make_pair(isVnniMatmul, hasBatch);
 }
 
 // Return true if all the operand have the same type, i.e., no implicit