[mlir][linalg] Allow controlling folding unit dim reshapes

[antiagainst]

Summary

This commit exposes an option to the pattern
FoldWithProducerReshapeOpByExpansion to allow
folding unit dim reshapes. This gives callers
more fine-grained controls.

Depends On D99016

[mravishankar]

THanks!

[antiagainst]

Rebase

[nicolasvasilache]

same discussion as in another CL, subview/subtensor/subtensor_insert has nicer inplace semantics than reshape

mlir/include/mlir/Dialect/Linalg/Passes.h

 
 /// Create a pass to convert Linalg operations to equivalent operations that
 /// work on primitive types, if possible.
 std::unique_ptr<Pass> createLinalgDetensorizePass();
 
 /// Patterns to fold an expanding (collapsing) tensor_reshape operation with its
 /// producer (consumer) generic operation by expanding the dimensionality of the
 /// loop in the generic op.
-void populateFoldReshapeOpsByExpansionPatterns(RewritePatternSet &patterns);
+void populateFoldReshapeOpsByExpansionPatterns(
+    RewritePatternSet &patterns, bool allowFoldingUnitDimReshapes = false);
 
 /// Patterns to fold a collapsing (expanding) tensor_reshape operation with its
 /// producer (consumer) generic/indexed_generic operation by linearizing the
 /// indexing map used to access the source (target) of the reshape operation in
 /// the generic/indexed_generic operation.
 void populateFoldReshapeOpsByLinearizationPatterns(RewritePatternSet &patterns);
 
 /// Patterns to fold a collapsing (expanding) tensor_reshape operation with its
 /// producer (consumer) generic/indexed_generic operation by linearizing the
 /// indexing map used to access the source (target) of the reshape operation in
 /// the generic/indexed_generic operation. The patterns are applied only when
 /// the tensor reshape involved is collapsing (introducing) unit-extent
 /// dimensions.
 void populateFoldUnitDimsReshapeOpsByLinearizationPatterns(
     RewritePatternSet &patterns);
 
 /// Patterns for fusing linalg operation on tensors.
-void populateLinalgTensorOpsFusionPatterns(RewritePatternSet &patterns);
+void populateLinalgTensorOpsFusionPatterns(
+    RewritePatternSet &patterns, bool allowFoldingUnitDimReshapes = false);
 
 /// Patterns to fold unit-extent dimensions in operands/results of linalg ops on
 /// tensors.
 void populateLinalgFoldUnitExtentDimsPatterns(RewritePatternSet &patterns);
 
 //===----------------------------------------------------------------------===//
 // Registration
 //===----------------------------------------------------------------------===//
 
 /// Generate the code for registering passes.
 #define GEN_PASS_REGISTRATION
 #include "mlir/Dialect/Linalg/Passes.h.inc"
 
 } // namespace mlir
 
 #endif // MLIR_DIALECT_LINALG_PASSES_H_

mlir/include/mlir/Dialect/Linalg/Passes.td

Option<"foldOneTripLoopsOnly", "fold-one-trip-loops-only", "bool",
            "(for testing purposes only)">
   ];
   let dependentDialects = ["linalg::LinalgDialect"];
 }
 
 def LinalgFusionOfTensorOps : Pass<"linalg-fusion-for-tensor-ops"> {
   let summary = "Fuse operations on RankedTensorType in linalg dialect";
   let constructor = "mlir::createLinalgFusionOfTensorOpsPass()";
+  let options = [
+    Option<"allowFoldingUnitDimReshapes", "allow-folding-unit-dim-reshapes",
+           "bool", /*default=*/"false",
+           "Allow fusing linalg.tensor_reshape ops that performs unit "
+           "dimension collapsing">
+  ];
   let dependentDialects = ["linalg::LinalgDialect", "AffineDialect"];
 }
 
 def LinalgFoldReshapeOpsByLinearization :
   Pass<"linalg-fold-reshape-ops-by-linearization"> {
   let summary = "Fold TensorReshapeOps with generic/indexed generic ops by "
                 "linearization";
   let constructor = "mlir::createFoldReshapeOpsByLinearizationPass()";

mlir/lib/Dialect/Linalg/Transforms/FusionOnTensors.cpp

LogicalResult matchAndRewrite(LinalgOpTy op,
     return failure();
   }
 };
 
 /// Pattern to fuse a tensor_reshape op with its consumer
 /// generic/indexed_generic op, when the reshape op is collapsing
 /// dimensions. The dimensionality of the loop in the consumer is expanded.
 template <typename GenericOpTy>
-struct FoldWithProducerReshapeOpByExpansion
+class FoldWithProducerReshapeOpByExpansion
     : public OpRewritePattern<GenericOpTy> {
-  using OpRewritePattern<GenericOpTy>::OpRewritePattern;
+public:
+  FoldWithProducerReshapeOpByExpansion(MLIRContext *context,
+                                       bool foldUnitDimReshapes,
+                                       PatternBenefit benefit = 1)
+      : OpRewritePattern<GenericOpTy>(context, benefit),
+        allowFoldingUnitDimReshapes(foldUnitDimReshapes) {}
 
   LogicalResult matchAndRewrite(GenericOpTy genericOp,
                                 PatternRewriter &rewriter) const override {
     LinalgOp linalgOp = cast<LinalgOp>(genericOp.getOperation());
     for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
       TensorReshapeOp reshapeOp =
           operand.value().getDefiningOp<TensorReshapeOp>();
       if (!reshapeOp)
         continue;
 
       // Fold only if
       // - The tensor reshape op is folding.
       // - All constraints of fusing with reshape by expansion are met.
       if (reshapeOp.getSrcType().getRank() <
               reshapeOp.getResultType().getRank() ||
           !isFusableWithReshapeByDimExpansion(linalgOp, operand.index()) ||
+          (!allowFoldingUnitDimReshapes &&
            isUnitDimExpansionOnly(reshapeOp.getSrcType().getShape(),
-                                 reshapeOp.getReassociationMaps()))
+                                  reshapeOp.getReassociationMaps())))
         continue;
 
       Optional<SmallVector<Value, 1>> replacementValues =
           fuseWithReshapeByExpansion(linalgOp, reshapeOp, operand.index(),
                                      rewriter);
       if (!replacementValues)
         return failure();
       rewriter.replaceOp(genericOp, replacementValues.getValue());
       return success();
     }
     return failure();
   }
+
+private:
+  bool allowFoldingUnitDimReshapes;
 };
 
 /// Pattern to fold tensor_reshape op with its producer. The corresponding index
 /// map in the consumer needs to be modified to linearize the folded dimension.
 template <bool foldUnitDimReshapesOnly>
 struct FoldConsumerReshapeOpByLinearization
     : public OpRewritePattern<TensorReshapeOp> {
   using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;

 };
 
 /// Pass that fuses generic ops on tensors. Used only for testing.
 struct FusionOfTensorOpsPass
     : public LinalgFusionOfTensorOpsBase<FusionOfTensorOpsPass> {
   void runOnOperation() override {
     Operation *op = getOperation();
     RewritePatternSet patterns(op->getContext());
-    populateLinalgTensorOpsFusionPatterns(patterns);
+    populateLinalgTensorOpsFusionPatterns(patterns,
+                                          allowFoldingUnitDimReshapes);
     (void)applyPatternsAndFoldGreedily(op->getRegions(), std::move(patterns));
   }
 };
 
 /// Pass to test folding of reshape op with generic/indexed_generic ops by
 /// linearization.
 struct FoldReshapeOpsByLinearizationPass
     : public LinalgFoldReshapeOpsByLinearizationBase<
void mlir::populateFoldUnitDimsReshapeOpsByLinearizationPatterns(
     RewritePatternSet &patterns) {
   patterns.add<FoldProducerReshapeOpByLinearization<GenericOp, true>,
                FoldProducerReshapeOpByLinearization<IndexedGenericOp, true>,
                FoldConsumerReshapeOpByLinearization<true>>(
       patterns.getContext());
 }
 
 void mlir::populateFoldReshapeOpsByExpansionPatterns(
-    RewritePatternSet &patterns) {
-  patterns.add<FoldReshapeWithGenericOpByExpansion,
-               FoldWithProducerReshapeOpByExpansion<GenericOp>,
+    RewritePatternSet &patterns, bool allowFoldingUnitDimReshapes) {
+  patterns.add<FoldReshapeWithGenericOpByExpansion>(patterns.getContext());
+  patterns.add<FoldWithProducerReshapeOpByExpansion<GenericOp>,
                FoldWithProducerReshapeOpByExpansion<IndexedGenericOp>>(
-      patterns.getContext());
+      patterns.getContext(), allowFoldingUnitDimReshapes);
 }
 
-void mlir::populateLinalgTensorOpsFusionPatterns(RewritePatternSet &patterns) {
+void mlir::populateLinalgTensorOpsFusionPatterns(
+    RewritePatternSet &patterns, bool allowFoldingUnitDimReshapes) {
   auto *context = patterns.getContext();
   patterns
       .add<FuseTensorOps<GenericOp>, FuseTensorOps<IndexedGenericOp>,
            FoldSplatConstants<GenericOp>, FoldSplatConstants<IndexedGenericOp>>(
           context);
-  populateFoldReshapeOpsByExpansionPatterns(patterns);
+  populateFoldReshapeOpsByExpansionPatterns(patterns,
+                                            allowFoldingUnitDimReshapes);
   GenericOp::getCanonicalizationPatterns(patterns, context);
   IndexedGenericOp::getCanonicalizationPatterns(patterns, context);
   TensorReshapeOp::getCanonicalizationPatterns(patterns, context);
 }
 
 std::unique_ptr<Pass> mlir::createLinalgFusionOfTensorOpsPass() {
   return std::make_unique<FusionOfTensorOpsPass>();
 }
 
 std::unique_ptr<Pass> mlir::createFoldReshapeOpsByLinearizationPass() {
   return std::make_unique<FoldReshapeOpsByLinearizationPass>();
 }

mlir/test/Dialect/Linalg/reshape_fusion.mlir

-// RUN: mlir-opt %s -linalg-fusion-for-tensor-ops -split-input-file -verify-each=0 | FileCheck %s
+// RUN: mlir-opt %s -linalg-fusion-for-tensor-ops="allow-folding-unit-dim-reshapes=false" -split-input-file -verify-each=0 | FileCheck %s
+// RUN: mlir-opt %s -linalg-fusion-for-tensor-ops="allow-folding-unit-dim-reshapes=true" -split-input-file -verify-each=0 | FileCheck %s --check-prefix=FOLDUNITDIM
 
 #map0 = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
 #map1 = affine_map<(d0, d1, d2) -> (d1, d2, d0)>
 func @generic_op_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xf32>,
                                          %arg1 : tensor<?x?x?xf32>) ->
                                          tensor<?x?x?xf32>
 {
   %0 = linalg.tensor_reshape %arg0 [affine_map<(i, j, k, l) -> (i)>,
%c = linalg.indexed_generic {
        ^bb0(%arg0 : index, %arg1 : index, %arg2 : index, %arg3 : i32, %arg4: i32, %s: i32):
          %1 = addi %arg3, %arg4 : i32
          %2 = index_cast %arg0 : index to i32
          %3 = addi %1, %2 : i32
          %4 = index_cast %arg1 : index to i32
          %5 = addi %3, %4 : i32
          %6 = index_cast %arg2 : index to i32
          %7 = addi %5, %6 : i32
-   linalg.yield %7 : i32
+         linalg.yield %7 : i32
        } -> tensor<6x4x210xi32>
   %d = linalg.tensor_reshape %c
          [affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1)>,
           affine_map<(d0, d1, d2, d3, d4, d5) -> (d2)>,
           affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>]
        : tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
   return %d : tensor<2x3x4x5x6x7xi32>
 }
^bb0(%arg2: f32, %arg3: f32, %arg4: f32):  // no predecessors
     linalg.yield %4 : f32
   } -> tensor<?x2x4xf32>
   return %3 : tensor<?x2x4xf32>
 }
 //      CHECK: func @unit_dim_reshape_expansion_full
 //  CHECK-DAG:   linalg.tensor_reshape
 //  CHECK-DAG:   linalg.init_tensor
 //      CHECK:   linalg.generic
+// CHECK-SAME:     ins(%{{.+}}, %{{.+}} : tensor<?x2x4xf32>, tensor<?x2x4xf32>)
+
+//         FOLDUNITDIM: func @unit_dim_reshape_expansion_full
+//         FOLDUNITDIM:   linalg.init_tensor
+// FOLDUNITDIM-COUNT-2:   linalg.tensor_reshape
+//         FOLDUNITDIM:   linalg.generic
+//    FOLDUNITDIM-SAME:     ins(%{{.+}}, %{{.+}} : tensor<1x?x1x2x1x4xf32>, tensor<1x?x1x2x1x4xf32>)
+