From: Murali Vijayaraghavan <muralivi@google.com>
Date: Wed, 5 Oct 2022 23:46:02 +0000 (+0000)
Subject: [mlir] vector.multi_reduction canonicalizes to vector.shape_cast (or
X-Git-Tag: upstream/17.0.6~31451
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=9c3d3eeb51b7a3f6428bab7bd46452ce18029060;p=platform%2Fupstream%2Fllvm.git

[mlir] vector.multi_reduction canonicalizes to vector.shape_cast (or
vector.extract, if the result is a scalar) only if all reduction
dimensions are of size 1.

Differential Revision: https://reviews.llvm.org/D135333
---

diff --git a/mlir/include/mlir/Dialect/Vector/IR/VectorOps.td b/mlir/include/mlir/Dialect/Vector/IR/VectorOps.td
index 94d0ea9..575dfbb9 100644
--- a/mlir/include/mlir/Dialect/Vector/IR/VectorOps.td
+++ b/mlir/include/mlir/Dialect/Vector/IR/VectorOps.td
@@ -396,6 +396,7 @@ def Vector_MultiDimReductionOp :
   let assemblyFormat =
     "$kind `,` $source `,` $acc attr-dict $reduction_dims `:` type($source) `to` type($dest)";
   let hasFolder = 1;
+  let hasCanonicalizer = 1;
   let hasVerifier = 1;
 }
 
diff --git a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
index 4929ca1..5e0a177 100644
--- a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
+++ b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
@@ -309,6 +309,50 @@ LogicalResult MultiDimReductionOp::verify() {
   return success();
 }
 
+namespace {
+// Only unit dimensions that are being reduced are folded. If the dimension is
+// unit, but not reduced, it is not folded, thereby keeping the output type the
+// same. If not all dimensions which are reduced are of unit dimension, this
+// transformation does nothing. This is just a generalization of
+// ElideSingleElementReduction for ReduceOp.
+struct ElideUnitDimsInMultiDimReduction
+    : public OpRewritePattern<MultiDimReductionOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(MultiDimReductionOp reductionOp,
+                                PatternRewriter &rewriter) const override {
+    ArrayRef<int64_t> shape = reductionOp.getSourceVectorType().getShape();
+    for (auto dim : enumerate(shape)) {
+      if (reductionOp.isReducedDim(dim.index()) && dim.value() != 1)
+        return failure();
+    }
+    Location loc = reductionOp.getLoc();
+    Value acc = reductionOp.getAcc();
+    Value cast;
+    if (reductionOp.getDestType().isa<VectorType>()) {
+      cast = rewriter.create<vector::ShapeCastOp>(
+          loc, reductionOp.getDestType(), reductionOp.getSource());
+    } else {
+      // This means we are reducing all the dimensions, and all reduction
+      // dimensions are of size 1. So a simple extraction would do.
+      cast = rewriter.create<vector::ExtractOp>(
+          loc, reductionOp.getDestType(), reductionOp.getSource(),
+          rewriter.getI64ArrayAttr(SmallVector<int64_t>(shape.size(), 0)));
+    }
+
+    Value result = vector::makeArithReduction(rewriter, loc,
+                                              reductionOp.getKind(), acc, cast);
+    rewriter.replaceOp(reductionOp, result);
+    return success();
+  }
+};
+} // namespace
+
+void MultiDimReductionOp::getCanonicalizationPatterns(
+    RewritePatternSet &results, MLIRContext *context) {
+  results.add<ElideUnitDimsInMultiDimReduction>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // ReductionOp
 //===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Vector/canonicalize.mlir b/mlir/test/Dialect/Vector/canonicalize.mlir
index 15c78c1..c3d9ae2 100644
--- a/mlir/test/Dialect/Vector/canonicalize.mlir
+++ b/mlir/test/Dialect/Vector/canonicalize.mlir
@@ -1348,6 +1348,44 @@ func.func @vector_multi_reduction_single_parallel(%arg0: vector<2xf32>, %acc: ve
 
 // -----
 
+// CHECK-LABEL: func @vector_multi_reduction_unit_dimensions(
+//  CHECK-SAME: %[[SOURCE:.+]]: vector<5x1x4x1x20xf32>, %[[ACC:.+]]: vector<5x4x20xf32>
+func.func @vector_multi_reduction_unit_dimensions(%source: vector<5x1x4x1x20xf32>, %acc: vector<5x4x20xf32>) -> vector<5x4x20xf32> {
+//       CHECK:   %[[CAST:.+]] = vector.shape_cast  %[[SOURCE]] : vector<5x1x4x1x20xf32> to vector<5x4x20xf32>
+//       CHECK:   %[[RESULT:.+]] = arith.mulf  %[[ACC]], %[[CAST]] : vector<5x4x20xf32>
+    %0 = vector.multi_reduction <mul>, %source, %acc [1, 3] : vector<5x1x4x1x20xf32> to vector<5x4x20xf32>
+
+//       CHECK:     return %[[RESULT]] : vector<5x4x20xf32>
+    return %0 : vector<5x4x20xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @vector_multi_reduction_unit_dimensions_fail(
+//  CHECK-SAME: %[[SRC:.+]]: vector<5x1x4x1x20xf32>, %[[ACCUM:.+]]: vector<5x1x20xf32>
+func.func @vector_multi_reduction_unit_dimensions_fail(%source: vector<5x1x4x1x20xf32>, %acc: vector<5x1x20xf32>) -> vector<5x1x20xf32> {
+//       CHECK:   %[[RES:.+]] = vector.multi_reduction  <mul>, %[[SRC]], %[[ACCUM]] [1, 2] : vector<5x1x4x1x20xf32> to vector<5x1x20xf32>
+    %0 = vector.multi_reduction <mul>, %source, %acc [1, 2] : vector<5x1x4x1x20xf32> to vector<5x1x20xf32>
+
+//       CHECK:     return %[[RES]] : vector<5x1x20xf32>
+    return %0 : vector<5x1x20xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @vector_multi_reduction_unit_dimensions_single_elem(
+//  CHECK-SAME: %[[SOURCE:.+]]: vector<1x1x1xf32>, %[[ACC:.+]]: f32
+func.func @vector_multi_reduction_unit_dimensions_single_elem(%source: vector<1x1x1xf32>, %acc: f32) -> f32 {
+//       CHECK:   %[[CAST:.+]] = vector.extract  %[[SOURCE]][0, 0, 0] : vector<1x1x1xf32>
+//       CHECK:   %[[RESULT:.+]] = arith.mulf  %[[ACC]], %[[CAST]] : f32
+    %0 = vector.multi_reduction <mul>, %source, %acc [0,1,2] : vector<1x1x1xf32> to f32
+
+//       CHECK:     return %[[RESULT]] : f32
+    return %0 : f32
+}
+
+// -----
+
 // CHECK-LABEL: func @insert_strided_slice_full_range
 //  CHECK-SAME: %[[SOURCE:.+]]: vector<16x16xf16>, %{{.+}}: vector<16x16xf16>
 func.func @insert_strided_slice_full_range(%source: vector<16x16xf16>, %dest: vector<16x16xf16>) -> vector<16x16xf16> {