[Mlir-commits] [mlir] [mlir][linalg] Add pass to transpose matmul op (PR #89075)

[Benjamin Maxwell]

================
@@ -0,0 +1,161 @@
+//===- TransposeMatmul.cpp - Convert Linalg matmul to transposed variants -===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+// This is intended to be a simple high-level (target-agnostic) matmul
+// transposition transformation.
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/Passes.h"
+#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+
+namespace mlir {
+#define GEN_PASS_DEF_LINALGTRANSPOSEMATMULPASS
+#include "mlir/Dialect/Linalg/Passes.h.inc"
+} // namespace mlir
+
+#define DEBUG_TYPE "linalg-transpose-matmul"
+
+using namespace mlir;
+using namespace mlir::linalg;
+
+namespace {
+/// Pattern to replace
+///
+///   linalg.matmul(a, b)
+///
+/// with
+///
+///   linalg.matmul_transpose_a(linalg.transpose(a), b)
+///
+/// By default A is transposed. If `transposeA` is set to false then B is
+/// transposed.
+struct TransposeMatmul final : public OpRewritePattern<linalg::MatmulOp> {
+  TransposeMatmul(MLIRContext *ctx, bool transposeA, PatternBenefit benefit = 1)
+      : OpRewritePattern(ctx, benefit), transposeA(transposeA) {}
+
+  LogicalResult matchAndRewrite(linalg::MatmulOp matmulOp,
+                                PatternRewriter &rewriter) const override {
+    if (!bufferization::hasTensorSemantics(matmulOp))
+      return rewriter.notifyMatchFailure(
+          matmulOp, "only matmul ops with tensors are supported");
+
+    Location loc = matmulOp.getLoc();
+    Value input = matmulOp.getInputs()[transposeA ? 0 : 1];
+    auto type = cast<ShapedType>(input.getType());
+
+    SmallVector<Value> dynamicDims;
+    if (type.isDynamicDim(1))
+      dynamicDims.push_back(rewriter.create<tensor::DimOp>(loc, input, 1));
+    if (type.isDynamicDim(0))
+      dynamicDims.push_back(rewriter.create<tensor::DimOp>(loc, input, 0));
+
+    auto shape = type.getShape();
+    SmallVector<int64_t> transposedShape{shape[1], shape[0]};
+    Value empty = rewriter.create<tensor::EmptyOp>(
+        loc, transposedShape, type.getElementType(), dynamicDims);
----------------
MacDue wrote:

Nit: This does not need to be a vector:
```suggestion
    std::array transposedShape{shape[1], shape[0]};
    Value empty = rewriter.create<tensor::EmptyOp>(
        loc, transposedShape, type.getElementType(), dynamicDims);
```
or 
```suggestion
    Value empty = rewriter.create<tensor::EmptyOp>(
        loc, ArrayRef<int64_t>{shape[1], shape[0]}, type.getElementType(), dynamicDims);
```
Should work

https://github.com/llvm/llvm-project/pull/89075