[LinalgToXeGPU] Lower linalg.matmul_transpose_b into xegpu.dpas

lib/gc/Transforms/GPU/LinalgToXeGPU.cpp

@@ -94,6 +94,7 @@ static bool isDPASCompatible(linalg::LinalgOp linalgOp, int kTile,
                              ArrayRef<int64_t> dpasTile) {
   if (!(isa<linalg::MatmulOp>(linalgOp) ||
         isa<linalg::BatchReduceMatmulOp>(linalgOp) ||
+        isa<linalg::MatmulTransposeBOp>(linalgOp) ||
         isa<linalg::GenericOp>(linalgOp))) {
     return false;
   }
@@ -633,12 +634,11 @@ static SmallVector<Value> updateTilesOffsets(PatternRewriter &rewriter,
 //
 // The descriptor sub-tiles are ordered in row-major fashion with respect to the
 // whole load tile.
-static SmallVector<Value> createDescriptorTiles(PatternRewriter &rewriter,
-                                                Location loc, Value src,
-                                                ArrayRef<int64_t> loadShape,
-                                                ArrayRef<int64_t> loadOffsets,
-                                                ArrayRef<int64_t> descTile,
-                                                int arrayLength = 1) {
+static SmallVector<Value>
+createDescriptorTiles(PatternRewriter &rewriter, Location loc, Value src,
+                      ArrayRef<int64_t> loadShape,
+                      ArrayRef<int64_t> loadOffsets, ArrayRef<int64_t> descTile,
+                      int arrayLength = 1, bool transpose = false) {
   assert(arrayLength == 1 && "Array descriptors are not supported");
 
   auto type = cast<ShapedType>(src.getType());
@@ -669,6 +669,9 @@ static SmallVector<Value> createDescriptorTiles(PatternRewriter &rewriter,
     Value newRowOffs = rewriter.create<arith::ConstantIndexOp>(loc, i);
     for (int j = 0; j < loadShape[1]; j += descTile[1] * arrayLength) {
       Value newColOffs = rewriter.create<arith::ConstantIndexOp>(loc, j);
+      if (transpose) {
+        std::swap(newRowOffs, newColOffs);
+      }
       auto tile = rewriter
                       .create<xegpu::UpdateNdOffsetOp>(
                           loc, descType, rootTile,
@@ -693,7 +696,8 @@ static SmallVector<Value> createDescriptorTiles(PatternRewriter &rewriter,
 static SmallVector<Value> createCoarseDscTiles(PatternRewriter &rewriter,
                                                Location loc, Value src,
                                                ArrayRef<int64_t> sgTile,
-                                               bool isVnni) {
+                                               bool isVnni,
+                                               bool transpose = false) {
   assert(sgTile.size() <= 2 &&
          "Require at most 2D tile size for eltwise lowering");
 
@@ -727,7 +731,8 @@ static SmallVector<Value> createCoarseDscTiles(PatternRewriter &rewriter,
   // NOLINTEND
 
   return createDescriptorTiles(rewriter, loc, src, sgTile2D, {0, 0},
-                               {sgLoadRows, sgLoadCols}, arrayLength);
+                               {sgLoadRows, sgLoadCols}, arrayLength,
+                               transpose);
 }
 
 // Return vector type with specified VNNI shape.
@@ -745,7 +750,8 @@ static SmallVector<Value>
 loadNdDescTiles(PatternRewriter &rewriter, Location loc, ValueRange loadTiles,
                 xegpu::CachePolicyAttr hint,
                 std::optional<VnniConfig> vnniConf = std::nullopt,
-                DenseI64ArrayAttr transpose = nullptr) {
+                DenseI64ArrayAttr transpose = nullptr,
+                IntegerAttr transpose_bit = nullptr) {
   // Assume all tiles have the same shape.
   auto tileType = cast<xegpu::TensorDescType>(loadTiles[0].getType());
   assert(llvm::all_of(loadTiles,
@@ -760,7 +766,6 @@ loadNdDescTiles(PatternRewriter &rewriter, Location loc, ValueRange loadTiles,
                                 *vnniConf);
     packedAttr = mlir::UnitAttr::get(rewriter.getContext());
   }
-  IntegerAttr transpose_bit = nullptr;
   SmallVector<Value> loadVec;
   for (auto tile : loadTiles) {
 
@@ -860,13 +865,82 @@ extractVecSubTiles(PatternRewriter &rewriter, Location loc,
   return subTiles;
 }
 
+// Checks whether the given `matmulOperand` is produced by a
+// `linalg::TransposeOp` and ensures that the transpose result is only used by
+// valid operations, such as `linalg::MatmulOp`, `linalg::BatchReduceMatmulOp`,
+// or `linalg::GenericOp`.
+//
+// If a valid transpose operation is found, the function records it for later
+// removal and returns the operand of the transpose operation as the new matrix
+// multiplication operand.
+static FailureOr<Value> findAndReplaceTranspose(const Value &matmulOperand,
+                                                PatternRewriter &rewriter) {
+  auto defOp = matmulOperand.getDefiningOp();
+  if (!defOp) {
+    return failure();
+  }
+  linalg::TransposeOp transposeOp = nullptr;
+
+  for (auto x : defOp->getUsers()) {
+    if (isa<linalg::TransposeOp>(x)) {
+      if (transposeOp) {
+        return rewriter.notifyMatchFailure(
+            transposeOp, "Only one transpose operation is allowed");
+      }
+
+      transposeOp = dyn_cast<linalg::TransposeOp>(x);
+
+      auto transposeRes = transposeOp.getDpsInits()[0];
+      // verify that there are no other users of the transpose result
+      // rather than our matmul
+      for (auto trUser : transposeRes.getUsers()) {
+        if (isa<linalg::MatmulOp>(trUser) ||
+            isa<linalg::BatchReduceMatmulOp>(trUser) ||
+            isa<linalg::GenericOp>(trUser)) {
+          auto matmulOp = dyn_cast<linalg::LinalgOp>(trUser);
+          auto actualMatmulOperand = matmulOp.getDpsInputs()[1];
+          if (actualMatmulOperand != matmulOperand) {
+            return rewriter.notifyMatchFailure(
+                trUser,
+                "Transpose result is used by more than one matmul operation");
+          }
+        } else if (isa<memref::DeallocOp>(trUser) ||
+                   isa<memref::AllocaOp>(trUser)) {
+          // allow allocs and deallocs as users
+          continue;
+        } else if (isa<linalg::TransposeOp>(trUser)) {
+          // check if it's the same transpose as we're processing
+          if (!mlir::OperationEquivalence::isEquivalentTo(trUser, transposeOp,
+                                                          /*flags=*/nullptr)) {
+            return rewriter.notifyMatchFailure(
+                trUser, "Only one transpose operation is allowed");
+          }
+          continue;
+        } else {
+          return rewriter.notifyMatchFailure(
+              trUser,
+              "Transpose result is not allowed to be used by this operation");
+        }
+      }
+    }
+  }
+  if (transposeOp) {
+    auto ret = transposeOp.getDpsInputs()[0];
+    rewriter.eraseOp(transposeOp);
+    return ret;
+  }
+  return rewriter.notifyMatchFailure(
+      defOp, "No transpose operation producing the operand was found");
+}
+
 // Create XeGPU DPAS kernel out of GEMM-like operation.
 static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
                                       ArrayRef<int64_t> dpasTile, int kTile,
                                       int prefetchStages,
                                       PatternRewriter &rewriter) {
   assert((isa<linalg::MatmulOp>(linalgOp) ||
           isa<linalg::BatchReduceMatmulOp>(linalgOp) ||
+          isa<linalg::MatmulTransposeBOp>(linalgOp) ||
           isa<linalg::GenericOp>(linalgOp)) &&
          "Requires a GEMM-like op for DPAS lowering");
 
@@ -877,6 +951,17 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
   auto matB = linalgOp.getDpsInputs()[1];
   auto matC = linalgOp.getDpsInits()[0];
 
+  bool transposeB = false;
+  if (isa<linalg::MatmulTransposeBOp>(linalgOp)) {
+    transposeB = true;
+  } else {
+    auto newMatB = findAndReplaceTranspose(matB, rewriter);
+    if (!failed(newMatB)) {
+      matB = *newMatB;
+      transposeB = true;
+    }
+  }
+
   auto typeA = cast<ShapedType>(matA.getType());
   auto typeC = cast<ShapedType>(matC.getType());
 
@@ -961,7 +1046,8 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
 
   // Create B sub-tiles.
   SmallVector<Value> tilesB =
-      createCoarseDscTiles(rewriter, loc, matB, {kTile, dimN}, /*isVnni=*/true);
+      createCoarseDscTiles(rewriter, loc, matB, {kTile, dimN},
+                           /*isVnni=*/true, transposeB);
 
   // Create input prefetch tiles.
   int64_t numThreads = 1;
@@ -997,7 +1083,9 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
         {dimM, dimN}, kTile);
     auto prefetchDescB = createGemmCoopPrefetchTile(
         rewriter, linalgOp, /*inputPos=*/1, numThreads, {blockRows, blockCols},
-        {dimM, dimN}, kTile);
+        (transposeB) ? std::vector<int32_t>{dimM, dimN}
+                     : std::vector<int32_t>{dimN, dimM},
+        kTile);
 
     if (succeeded(prefetchDescA) && succeeded(prefetchDescB)) {
       prefetchA = prefetchDescA->getResult();
@@ -1012,7 +1100,9 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
         prefetchA = updateTilesOffsets(rewriter, loc, ValueRange{prefetchA},
                                        {0, kTile})[0];
         prefetchB = updateTilesOffsets(rewriter, loc, ValueRange{prefetchB},
-                                       {kTile, 0})[0];
+                                       (transposeB)
+                                           ? std::vector<int64_t>{0, kTile}
+                                           : std::vector<int64_t>{kTile, 0})[0];
       }
     } else {
       // Disable coop prefetching on failure.
@@ -1083,15 +1173,26 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
       loadNdDescTiles(rewriter, loc, tilesA, readCacheHint);
   auto tileTypeA = cast<xegpu::TensorDescType>(tilesA[0].getType());
 
+  DenseI64ArrayAttr transpose = nullptr;
+  IntegerAttr transpose_bit = nullptr;
+
+  if (transposeB) {
+    transpose_bit = rewriter.getIntegerAttr(rewriter.getIntegerType(32), 32);
+    transpose = DenseI64ArrayAttr::get(rewriter.getContext(), {1, 0});
+  }
+
   // Load B sub-tiles.
   SmallVector<Value> loadVecB =
-      loadNdDescTiles(rewriter, loc, tilesB, readCacheHint, vnniConfB);
+      loadNdDescTiles(rewriter, loc, tilesB, readCacheHint, vnniConfB,
+                      transpose, transpose_bit);
   auto tileTypeB = cast<xegpu::TensorDescType>(tilesB[0].getType());
 
   // Update offsets of the input tiles.
   // Shift along the reduction dimension.
   tilesA = updateTilesOffsets(rewriter, loc, tilesA, {0, kTile});
-  tilesB = updateTilesOffsets(rewriter, loc, tilesB, {kTile, 0});
+  tilesB = updateTilesOffsets(rewriter, loc, tilesB,
+                              transposeB ? std::vector<int64_t>{0, kTile}
+                                         : std::vector<int64_t>{kTile, 0});
 
   // Prefetch the next set of input tiles.
   if (isCoopPrefetch) {
@@ -1101,7 +1202,9 @@ static LogicalResult createDPASKernel(linalg::LinalgOp linalgOp,
     prefetchA =
         updateTilesOffsets(rewriter, loc, ValueRange{prefetchA}, {0, kTile})[0];
     prefetchB =
-        updateTilesOffsets(rewriter, loc, ValueRange{prefetchB}, {kTile, 0})[0];
+        updateTilesOffsets(rewriter, loc, ValueRange{prefetchB},
+                           transposeB ? std::vector<int64_t>{0, kTile}
+                                      : std::vector<int64_t>{kTile, 0})[0];
   } else {
     // Apply naive prefetching for each subgroup separately.
     prefetchTiles(rewriter, loc, tilesA, readCacheHint);
@@ -1288,7 +1391,7 @@ struct ConvertGemmLikeToXeGPU : public OpRewritePattern<LinalgOpTy> {
   // Constrain conversion to the supported GEMM-like ops.
   static_assert(
       llvm::is_one_of<LinalgOpTy, linalg::MatmulOp, linalg::BatchReduceMatmulOp,
-                      linalg::GenericOp>::value);
+                      linalg::GenericOp, linalg::MatmulTransposeBOp>::value);
 
   ConvertGemmLikeToXeGPU(MLIRContext *ctx, LinalgToXeGPUOptions options)
       : OpRewritePattern<LinalgOpTy>(ctx), options(options) {}
@@ -1495,8 +1598,9 @@ struct ConvertMemoryFillToXeGPU : public OpRewritePattern<LinalgOpTy> {
 void populateLinalgGemmToXeGPUPatterns(RewritePatternSet &patterns,
                                        LinalgToXeGPUOptions options) {
   patterns.add<ConvertGemmLikeToXeGPU<linalg::MatmulOp>,
-               ConvertGemmLikeToXeGPU<linalg::GenericOp>>(patterns.getContext(),
-                                                          options);
+               ConvertGemmLikeToXeGPU<linalg::GenericOp>,
+               ConvertGemmLikeToXeGPU<linalg::MatmulTransposeBOp>>(
+      patterns.getContext(), options);
 }
 
 void populateLinalgEltwiseToXeGPUPatterns(RewritePatternSet &patterns,

test/mlir/test/gc/Transforms/GPU/linalg-to-xegpu-dpas-transpose-sep-alloc.mlir

@@ -0,0 +1,86 @@
+// RUN: gc-opt %s -linalg-to-xegpu="dpas-tile=8,16,16 k-tile=16" -canonicalize -split-input-file | FileCheck %s
+
+module {
+  func.func @matmul_transpose_b_sep(%arg0: memref<1024x1024xf16>, %arg1: memref<1024x1024xf16>, %arg2: memref<1024x1024xf16>) {
+    %c0 = arith.constant 0 : index
+    %c32 = arith.constant 32 : index
+    %c1024 = arith.constant 1024 : index
+    %alloc = memref.alloc() {alignment = 64 : i64} : memref<1024x1024xf16>
+    scf.parallel (%arg3, %arg4) = (%c0, %c0) to (%c1024, %c1024) step (%c32, %c32) {
+      %subview_0 = memref.subview %arg2[%arg3, %arg4] [32, 32] [1, 1] : memref<1024x1024xf16> to memref<32x32xf16, strided<[1024, 1], offset: ?>>
+      %subview_1 = memref.subview %arg0[%arg3, 0] [32, 1024] [1, 1] : memref<1024x1024xf16> to memref<32x1024xf16, strided<[1024, 1], offset: ?>>
+      %subview_2 = memref.subview %arg1[%arg4, 0] [32, 1024] [1, 1] : memref<1024x1024xf16> to memref<32x1024xf16, strided<[1024, 1], offset: ?>>
+      %subview_3 = memref.subview %alloc[0, %arg4] [1024, 32] [1, 1] : memref<1024x1024xf16> to memref<1024x32xf16, strided<[1024, 1], offset: ?>>
+      linalg.transpose ins(%subview_2 : memref<32x1024xf16, strided<[1024, 1], offset: ?>>) outs(%subview_3 : memref<1024x32xf16, strided<[1024, 1], offset: ?>>) permutation = [1, 0]
+      linalg.matmul ins(%subview_1, %subview_3 : memref<32x1024xf16, strided<[1024, 1], offset: ?>>, memref<1024x32xf16, strided<[1024, 1], offset: ?>>) outs(%subview_0 : memref<32x32xf16, strided<[1024, 1], offset: ?>>)
+      scf.reduce
+    }
+    memref.dealloc %alloc : memref<1024x1024xf16>
+    return
+  }
+}
+
+// CHECK-LABEL: func.func @matmul_transpose_b_sep
+// CHECK-SAME:  %[[Ap:.+]]: memref<1024x1024xf16>, %[[Bp:.+]]: memref<1024x1024xf16>, %[[Cp:.+]]: memref<1024x1024xf16>
+
+// CHECK-NOT: memref.alloc()
+
+// CHECK: scf.parallel (%[[iter1:.+]], %[[iter2:.+]]) = (%c0, %c0) to (%c1024, %c1024) step (%c32, %c32) {
+// CHECK: %[[C:.+]] = memref.subview %[[Cp]][%[[iter1]], %[[iter2]]] {{.*}}
+// CHECK: %[[A:.+]] = memref.subview %[[Ap]][%[[iter1]], 0] {{.*}}
+// CHECK: %[[B:.+]] = memref.subview %[[Bp]][%[[iter2]], 0] {{.*}}
+
+// CHECK-NOT: linalg.transpose
+
+// Create output initial value load tiles.
+// CHECK-DAG: %[[rootC:.+]] = xegpu.create_nd_tdesc %[[C]]
+// CHECK: %[[tC:.+]] = xegpu.update_nd_offset %[[rootC]], [%c0, %c0]
+// CHECK-COUNT-7: xegpu.update_nd_offset
+
+// Load initial accumulator values.
+// CHECK-DAG: %[[vC:.+]] = xegpu.load_nd %[[tC]]
+// CHECK-COUNT-7: xegpu.load_nd
+
+// Extend the type to match DPAS output precision.
+// CHECK: %[[vC_f32:.+]] = arith.extf %[[vC]]
+// CHECK-COUNT-7: arith.extf
+
+// Create input load tiles.
+// CHECK: %[[rootA:.+]] = xegpu.create_nd_tdesc %[[A]]
+// CHECK: %[[tA:.+]] = xegpu.update_nd_offset %[[rootA]], [%c0, %c0]
+// CHECK: %[[rootB:.+]] = xegpu.create_nd_tdesc %[[B]]
+// CHECK: %[[tB:.+]] = xegpu.update_nd_offset %[[rootB]], [%c0, %c0]
+// CHECK: %[[tB1:.+]] = xegpu.update_nd_offset %[[rootB]], [%c16, %c0]
+
+// Create DPAS computation loop over tiled reduction dimension.
+// CHECK: %[[res:.+]]:11 = scf.for{{.*}}%c0 to %c1024 step %c16
+// CHECK-SAME: iter_args(%[[acc:.+]] = %[[vC_f32]],{{.*}}%[[iterA:.+]] = %[[tA]],{{.*}}%[[iterB:.+]] = %[[tB]],{{.*}}%[[iterB1:.+]] = %[[tB1]]
+// CHECK-SAME: {
+
+// Load input values and update the load tile position.
+// CHECK:   %[[vA:.+]] = xegpu.load_nd %[[iterA]]
+// CHECK:   %[[vB:.+]] = xegpu.load_nd %[[iterB]] {{.*}}transpose = array<i64: 1, 0>{{.*}}transpose_bit_width = 32 : i32{{.*}}
+// CHECK:   %[[vB1:.+]] = xegpu.load_nd %[[iterB1]] {{.*}}transpose = array<i64: 1, 0>, transpose_bit_width = 32 : i32{{.*}}
+
+// CHECK:   %[[new_tA:.+]] = xegpu.update_nd_offset %[[iterA]], [%c0, %c16]
+// CHECK:   %[[new_tB:.+]] = xegpu.update_nd_offset %[[iterB]], [%c0, %c16]
+// CHECK:   %[[new_tB1:.+]] = xegpu.update_nd_offset %[[iterB1]], [%c0, %c16]
+
+// Apply simple prefetching scheme - start loading the next set of input
+// tiles before computation is started.
+// CHECK:   xegpu.prefetch_nd %[[new_tA]]
+// CHECK:   xegpu.prefetch_nd %[[new_tB]]
+// CHECK:   xegpu.prefetch_nd %[[new_tB1]]
+
+// Extract DPAS-sized chunks from larger loaded tile A.
+// Tile B is already in the correct shape.
+// CHECK:   %[[vA_flat:.+]] = vector.shape_cast %[[vA]] : vector<32x16xf16> to vector<512xf16>
+// CHECK:   %[[vA_dpas_flat:.+]] = vector.extract_strided_slice{{.*}}: vector<512xf16> to vector<128xf16>
+// CHECK:   %[[vA_dpas:.+]] = vector.shape_cast %[[vA_dpas_flat]] : vector<128xf16> to vector<8x8x2xf16>
+// CHECK-COUNT-3: vector.extract_strided_slice
+
+// Perform DPAS computation.
+// CHECK:   %[[dpas:.+]] = xegpu.dpas %[[vA_dpas]], %[[vB]], %[[acc]]
+// CHECK-COUNT-7: xegpu.dpas
+
+// CHECK-NOT: memref.dealloc()