linalg: add pattern to rewrite matvec/vecmat with transposed matrices

PiperOrigin-RevId: 769621770

Author: asraa

lib/Transforms/LinalgCanonicalizations/LinalgCanonicalizations.cpp

@@ -17,6 +17,7 @@
 #include "mlir/include/mlir/IR/OpDefinition.h"       // from @llvm-project
 #include "mlir/include/mlir/IR/PatternMatch.h"       // from @llvm-project
 #include "mlir/include/mlir/IR/Value.h"              // from @llvm-project
+#include "mlir/include/mlir/IR/ValueRange.h"         // from @llvm-project
 #include "mlir/include/mlir/Support/LLVM.h"          // from @llvm-project
 #include "mlir/include/mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
 
@@ -272,6 +273,50 @@ struct BroadcastToExpandShape
   }
 };
 
+struct RewriteTransposedVecmat
+    : public OpRewritePattern<mlir::linalg::VecmatOp> {
+ public:
+  RewriteTransposedVecmat(MLIRContext *context)
+      : OpRewritePattern<mlir::linalg::VecmatOp>(context) {}
+
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(mlir::linalg::VecmatOp vecmatOp,
+                                PatternRewriter &rewriter) const override {
+    auto transposeOp =
+        vecmatOp.getInputs()[1].getDefiningOp<linalg::TransposeOp>();
+    if (!transposeOp) return failure();
+
+    rewriter.replaceOpWithNewOp<linalg::MatvecOp>(
+        vecmatOp, vecmatOp.getResultTypes()[0],
+        ValueRange{transposeOp.getInput(), vecmatOp.getInputs()[0]},
+        vecmatOp.getDpsInits()[0]);
+    return success();
+  }
+};
+
+struct RewriteTransposedMatvec
+    : public OpRewritePattern<mlir::linalg::MatvecOp> {
+ public:
+  RewriteTransposedMatvec(MLIRContext *context)
+      : OpRewritePattern<mlir::linalg::MatvecOp>(context) {}
+
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(mlir::linalg::MatvecOp matvecOp,
+                                PatternRewriter &rewriter) const override {
+    auto transposeOp =
+        matvecOp.getInputs()[0].getDefiningOp<linalg::TransposeOp>();
+    if (!transposeOp) return failure();
+
+    rewriter.replaceOpWithNewOp<linalg::VecmatOp>(
+        matvecOp, matvecOp.getResultTypes()[0],
+        ValueRange{matvecOp.getInputs()[1], transposeOp.getInput()},
+        matvecOp.getDpsInits()[0]);
+    return success();
+  }
+};
+
 struct LinalgCanonicalizations
     : public impl::LinalgCanonicalizationsBase<LinalgCanonicalizations> {
   void runOnOperation() override {
@@ -281,7 +326,8 @@ struct LinalgCanonicalizations
     RewritePatternSet patterns(context);
     patterns.add<FoldConstantLinalgTranspose, FoldConstantFill,
                  FoldConstantBroadcast, LinalgMapToElementwise,
-                 BroadcastToExpandShape>(context);
+                 BroadcastToExpandShape, RewriteTransposedVecmat,
+                 RewriteTransposedMatvec>(context);
 
     // Run pattern matching and conversion
     // TODO (#1221): Investigate whether folding (default: on) can be skipped

tests/Transforms/linalg_canonicalizations/transposed_matmuls.mlir

@@ -0,0 +1,35 @@
+// RUN: heir-opt --linalg-canonicalizations --split-input-file %s | FileCheck %s
+
+module {
+  // CHECK: func @main
+  // CHECK-SAME: %[[arg0:.*]]: tensor<512x784xf32>,
+  // CHECK-SAME: %[[arg1:.*]]: tensor<784xf32>)
+  // CHECK: %[[cst:.*]] = arith.constant dense<0.{{0*}}e+00> : tensor<512xf32>
+  // CHECK: %[[v0:.*]] = linalg.matvec ins(%[[arg0]], %[[arg1]] : tensor<512x784xf32>, tensor<784xf32>) outs(%[[cst]] : tensor<512xf32>)
+  // CHECK: return %[[v0]] : tensor<512xf32>
+  func.func @main(%arg0: tensor<512x784xf32>, %arg2: tensor<784xf32>) -> tensor<512xf32> {
+    %cst_0 = arith.constant dense<0.000000e+00> : tensor<512xf32>
+    %0 = tensor.empty() : tensor<784x512xf32>
+    %transposed = linalg.transpose ins(%arg0 : tensor<512x784xf32>) outs(%0 : tensor<784x512xf32>) permutation = [1, 0]
+    %1 = linalg.vecmat ins(%arg2, %transposed : tensor<784xf32>, tensor<784x512xf32>) outs(%cst_0 : tensor<512xf32>) -> tensor<512xf32>
+    return %1 : tensor<512xf32>
+  }
+}
+
+// -----
+
+module {
+  // CHECK: func @main
+  // CHECK-SAME: %[[arg0:.*]]: tensor<784x512xf32>,
+  // CHECK-SAME: %[[arg1:.*]]: tensor<784xf32>)
+  // CHECK: %[[cst:.*]] = arith.constant dense<0.{{0*}}e+00> : tensor<512xf32>
+  // CHECK: %[[v0:.*]] = linalg.vecmat ins(%[[arg1]], %[[arg0]] : tensor<784xf32>, tensor<784x512xf32>) outs(%[[cst]] : tensor<512xf32>)
+  // CHECK: return %[[v0]] : tensor<512xf32>
+  func.func @main(%arg0: tensor<784x512xf32>, %arg2: tensor<784xf32>) -> tensor<512xf32> {
+    %cst_0 = arith.constant dense<0.000000e+00> : tensor<512xf32>
+    %0 = tensor.empty() : tensor<512x784xf32>
+    %transposed = linalg.transpose ins(%arg0 : tensor<784x512xf32>) outs(%0 : tensor<512x784xf32>) permutation = [1, 0]
+    %1 = linalg.matvec ins(%transposed, %arg2 : tensor<512x784xf32>, tensor<784xf32>) outs(%cst_0 : tensor<512xf32>) -> tensor<512xf32>
+    return %1 : tensor<512xf32>
+  }
+}