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[mlir][tensor] Fold tensor.cast into tensor.collapse_shape op

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This commit folds a `tensor.cast` op into a `tensor.collapse_shape` op
when following two conditions meet:
1. the `tensor.collapse_shape` op consumes result of the `tensor.cast` op.
2. `tensor.cast` op casts to a more dynamic version of the source tensor.
This is added as a canonicalization pattern in `tensor.collapse_shape` op.

Signed-Off-By: Gaurav Shukla <gaurav@nod-labs.com>

Reviewed By: mravishankar

Differential Revision: https://reviews.llvm.org/D130650
This commit is contained in:
Gaurav Shukla 2022-07-28 13:11:05 +05:30 committed by Prashant Kumar
parent 8a61749f76
commit 7d6ef5caef
2 changed files with 50 additions and 4 deletions
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40
mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
View File

@ -928,6 +928,36 @@ struct FoldReshapeWithFromElements : OpRewritePattern<TensorReshapeOp> {
} }
}; };
// Fold CastOp into CollapseShapeOp when adding static information.
struct FoldCollapseOfCastOp : public OpRewritePattern<CollapseShapeOp> {
using OpRewritePattern<CollapseShapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CollapseShapeOp collapseShapeOp,
PatternRewriter &rewriter) const override {
auto castOp = collapseShapeOp.getSrc().getDefiningOp<tensor::CastOp>();
if (!tensor::canFoldIntoConsumerOp(castOp))
return failure();
RankedTensorType srcType =
castOp.getSource().getType().cast<RankedTensorType>();
RankedTensorType newResultType = computeTensorReshapeCollapsedType(
srcType, collapseShapeOp.getReassociationMaps());
if (newResultType == collapseShapeOp.getResultType()) {
rewriter.updateRootInPlace(collapseShapeOp, [&]() {
collapseShapeOp.getSrcMutable().assign(castOp.getSource());
});
} else {
auto newOp = rewriter.create<CollapseShapeOp>(
collapseShapeOp.getLoc(), newResultType, castOp.getSource(),
collapseShapeOp.getReassociation());
rewriter.replaceOpWithNewOp<tensor::CastOp>(
collapseShapeOp, collapseShapeOp.getResultType(), newOp);
}
return success();
}
};
} // namespace } // namespace
void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results, void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
@ -940,10 +970,12 @@ void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results, void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) { MLIRContext *context) {
results.add<ComposeReassociativeReshapeOps<CollapseShapeOp>, results
ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp>, .add<ComposeReassociativeReshapeOps<CollapseShapeOp>,
FoldReshapeWithConstant<CollapseShapeOp>, ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp>,
FoldReshapeWithFromElements<CollapseShapeOp>>(context); FoldReshapeWithConstant<CollapseShapeOp>,
FoldReshapeWithFromElements<CollapseShapeOp>, FoldCollapseOfCastOp>(
context);
} }
OpFoldResult ExpandShapeOp::fold(ArrayRef<Attribute> operands) { OpFoldResult ExpandShapeOp::fold(ArrayRef<Attribute> operands) {

14
mlir/test/Dialect/Tensor/canonicalize.mlir
View File

@ -673,6 +673,20 @@ func.func @compose_expand_of_expand_of_zero_dim(%arg0 : tensor<f32>)
// ----- // -----
// CHECK-LABEL: func.func @collapse_of_cast(
// CHECK-SAME: %[[IN:.*]]: tensor<8x12x32xf32>) -> tensor<?x32xf32> {
// CHECK-NEXT: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[IN]] {{\[}}[0, 1], [2]] : tensor<8x12x32xf32> into tensor<96x32xf32>
// CHECK-NEXT %[[CAST:.*]] = tensor.cast %[[COLLAPSE]] : tensor<96x32xf32> to tensor<?x32xf32>
// CHECK-NEXT return %[[CAST]] : tensor<?x32xf32>
func.func @collapse_of_cast(%t: tensor<8x12x32xf32>) -> tensor<?x32xf32> {
%0 = tensor.cast %t : tensor<8x12x32xf32> to tensor<?x?x?xf32>
%1 = tensor.collapse_shape %0 [[0, 1], [2]] : tensor<?x?x?xf32> into tensor<?x?xf32>
%2 = tensor.cast %1 : tensor<?x?xf32> to tensor<?x32xf32>
return %2 : tensor<?x32xf32>
}
// -----
func.func @fold_collapse_of_expand(%arg0 : tensor<12x4xf32>) -> tensor<12x4xf32> { func.func @fold_collapse_of_expand(%arg0 : tensor<12x4xf32>) -> tensor<12x4xf32> {
%0 = tensor.expand_shape %arg0 [[0, 1], [2]] %0 = tensor.expand_shape %arg0 [[0, 1], [2]]
: tensor<12x4xf32> into tensor<3x4x4xf32> : tensor<12x4xf32> into tensor<3x4x4xf32>