From: thomasraoux <thomasraoux@google.com>
Date: Fri, 17 Sep 2021 17:09:57 +0000 (-0700)
Subject: [mlir][linalg] Extend drop unit dim pattern to all cases of reduction
X-Git-Tag: upstream/15.0.7~31218
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=36aac53b36983c5ae8b7dcb0519c34e8c41dc4e5;p=platform%2Fupstream%2Fllvm.git

[mlir][linalg] Extend drop unit dim pattern to all cases of reduction

Even with all parallel loops reading the output value is still allowed so we
don't have to handle reduction loops differently.

Differential Revision: https://reviews.llvm.org/D109851
---

diff --git a/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp b/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
index e23a58e..8315de4 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/DropUnitDims.cpp
@@ -187,40 +187,13 @@ struct FoldUnitDimLoops : public OpRewritePattern<GenericOp> {
       return failure();
     SmallVector<int64_t> dims = genericOp.getStaticShape();
 
-    // Find all the reduction iterators. Those need some special consideration
-    // (see below).
-    auto getLoopDimsOfType =
-        [&](StringRef iteratorTypeName) -> SmallVector<unsigned, 4> {
-      SmallVector<AffineExpr> dimExprs;
-      getDimsOfType(genericOp, iteratorTypeName, dimExprs);
-      return llvm::to_vector<4>(llvm::map_range(dimExprs, [](AffineExpr expr) {
-        return expr.cast<AffineDimExpr>().getPosition();
-      }));
-    };
-    auto reductionDims = getLoopDimsOfType(getReductionIteratorTypeName());
-
     DenseSet<unsigned> unitDims;
     SmallVector<unsigned, 4> unitDimsReductionLoops;
     ArrayAttr iteratorTypes = genericOp.iterator_types();
     for (auto expr : enumerate(invertedMap.getResults())) {
       if (AffineDimExpr dimExpr = expr.value().dyn_cast<AffineDimExpr>())
-        if (dims[dimExpr.getPosition()] == 1) {
-          if (isParallelIterator(iteratorTypes[expr.index()]))
-            unitDims.insert(expr.index());
-          else if (isReductionIterator(iteratorTypes[expr.index()]))
-            unitDimsReductionLoops.push_back(expr.index());
-        }
-    }
-
-    // Reduction loops can be dropped if there is at least one other reduction
-    // loop that is not dropped. This accounts for the initial value read in the
-    // reduction loop.
-    if (!unitDimsReductionLoops.empty() && reductionDims.size() > 1) {
-      if (unitDimsReductionLoops.size() == reductionDims.size())
-        unitDims.insert(reductionDims.begin(), std::prev(reductionDims.end()));
-      else
-        unitDims.insert(unitDimsReductionLoops.begin(),
-                        unitDimsReductionLoops.end());
+        if (dims[dimExpr.getPosition()] == 1)
+          unitDims.insert(expr.index());
     }
 
     if (unitDims.empty())
diff --git a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
index 0271638..60ad723 100644
--- a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
+++ b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
@@ -361,7 +361,7 @@ func @unit_dim_for_reduction(%arg0: tensor<1x?x1x?xf32>) -> tensor<1x?xf32> {
 
 // -----
 
-func @unit_dim_for_reduction_keep_one(%arg0: tensor<1x?x1x1xf32>) -> tensor<1x1xf32> {
+func @unit_dim_for_both_reduction(%arg0: tensor<1x?x1x1xf32>) -> tensor<1x1xf32> {
   %cst = constant 1.000000e+00 : f32
   %c3 = constant 3 : index
   %1 = linalg.init_tensor [1, 1] : tensor<1x1xf32>
@@ -378,17 +378,16 @@ func @unit_dim_for_reduction_keep_one(%arg0: tensor<1x?x1x1xf32>) -> tensor<1x1x
   } -> tensor<1x1xf32>
   return %3 : tensor<1x1xf32>
 }
-//  CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
-//  CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1) -> (d0)>
-//      CHECK: func @unit_dim_for_reduction_keep_one
+//  CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0) -> (d0)>
+//      CHECK: func @unit_dim_for_both_reduction
 // CHECK-SAME:   %[[ARG0:.+]]: tensor<1x?x1x1xf32>
-//  CHECK-DAG:   %[[RESHAPE:.+]] = linalg.tensor_collapse_shape %[[ARG0]] {{\[}}[0, 1, 2], [3]]
+//  CHECK-DAG:   %[[RESHAPE:.+]] = linalg.tensor_collapse_shape %[[ARG0]] {{\[}}[0, 1, 2, 3]
 //      CHECK:   %[[INIT:.+]] = linalg.init_tensor [1] : tensor<1xf32>
 //      CHECK:   %[[FILL:.+]] = linalg.fill(%{{.+}}, %[[INIT]])
 //      CHECK:   %[[RESULT:.+]] = linalg.generic
-// CHECK-SAME:     indexing_maps = [#[[MAP2]], #[[MAP3]]]
-// CHECK-SAME:     iterator_types = ["parallel", "reduction"]
-// CHECK-SAME:     ins(%[[RESHAPE]] : tensor<?x1xf32>)
+// CHECK-SAME:     indexing_maps = [#[[MAP2]], #[[MAP2]]]
+// CHECK-SAME:     iterator_types = ["parallel"]
+// CHECK-SAME:     ins(%[[RESHAPE]] : tensor<?xf32>)
 // CHECK-SAME:     outs(%[[FILL]] : tensor<1xf32>)
 //      CHECK:   %[[RESULT_RESHAPE:.+]] = linalg.tensor_expand_shape %[[RESULT]] {{\[}}[0, 1]]
 //      CHECK:   return %[[RESULT_RESHAPE]]