forked from OSchip/llvm-project
[mlir][tensor] Fold `tensor.cast` into `tensor.collapse_shape` op
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
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@ -928,6 +928,36 @@ struct FoldReshapeWithFromElements : OpRewritePattern<TensorReshapeOp> {
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}
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};
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// Fold CastOp into CollapseShapeOp when adding static information.
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struct FoldCollapseOfCastOp : public OpRewritePattern<CollapseShapeOp> {
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using OpRewritePattern<CollapseShapeOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(CollapseShapeOp collapseShapeOp,
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PatternRewriter &rewriter) const override {
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auto castOp = collapseShapeOp.getSrc().getDefiningOp<tensor::CastOp>();
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if (!tensor::canFoldIntoConsumerOp(castOp))
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return failure();
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RankedTensorType srcType =
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castOp.getSource().getType().cast<RankedTensorType>();
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RankedTensorType newResultType = computeTensorReshapeCollapsedType(
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srcType, collapseShapeOp.getReassociationMaps());
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if (newResultType == collapseShapeOp.getResultType()) {
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rewriter.updateRootInPlace(collapseShapeOp, [&]() {
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collapseShapeOp.getSrcMutable().assign(castOp.getSource());
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});
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} else {
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auto newOp = rewriter.create<CollapseShapeOp>(
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collapseShapeOp.getLoc(), newResultType, castOp.getSource(),
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collapseShapeOp.getReassociation());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(
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collapseShapeOp, collapseShapeOp.getResultType(), newOp);
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}
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return success();
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}
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};
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} // namespace
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void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
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@ -940,10 +970,12 @@ void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
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void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
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MLIRContext *context) {
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results.add<ComposeReassociativeReshapeOps<CollapseShapeOp>,
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ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp>,
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FoldReshapeWithConstant<CollapseShapeOp>,
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FoldReshapeWithFromElements<CollapseShapeOp>>(context);
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results
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.add<ComposeReassociativeReshapeOps<CollapseShapeOp>,
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ComposeCollapseOfExpandOp<CollapseShapeOp, ExpandShapeOp>,
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FoldReshapeWithConstant<CollapseShapeOp>,
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FoldReshapeWithFromElements<CollapseShapeOp>, FoldCollapseOfCastOp>(
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context);
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}
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OpFoldResult ExpandShapeOp::fold(ArrayRef<Attribute> operands) {
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@ -673,6 +673,20 @@ func.func @compose_expand_of_expand_of_zero_dim(%arg0 : tensor<f32>)
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// -----
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// CHECK-LABEL: func.func @collapse_of_cast(
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// CHECK-SAME: %[[IN:.*]]: tensor<8x12x32xf32>) -> tensor<?x32xf32> {
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// CHECK-NEXT: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[IN]] {{\[}}[0, 1], [2]] : tensor<8x12x32xf32> into tensor<96x32xf32>
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// CHECK-NEXT %[[CAST:.*]] = tensor.cast %[[COLLAPSE]] : tensor<96x32xf32> to tensor<?x32xf32>
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// CHECK-NEXT return %[[CAST]] : tensor<?x32xf32>
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func.func @collapse_of_cast(%t: tensor<8x12x32xf32>) -> tensor<?x32xf32> {
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%0 = tensor.cast %t : tensor<8x12x32xf32> to tensor<?x?x?xf32>
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%1 = tensor.collapse_shape %0 [[0, 1], [2]] : tensor<?x?x?xf32> into tensor<?x?xf32>
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%2 = tensor.cast %1 : tensor<?x?xf32> to tensor<?x32xf32>
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return %2 : tensor<?x32xf32>
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}
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// -----
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func.func @fold_collapse_of_expand(%arg0 : tensor<12x4xf32>) -> tensor<12x4xf32> {
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%0 = tensor.expand_shape %arg0 [[0, 1], [2]]
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: tensor<12x4xf32> into tensor<3x4x4xf32>
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