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[MLIR][HLO] Eliminate duplicate broadcastable constraints 

PiperOrigin-RevId: 376715240
This commit is contained in:
A. Unique TensorFlower 2021-05-31 13:07:16 -07:00 committed by TensorFlow MLIR Team
parent 511a1db4f3
commit 0f341012c6
2 changed files with 34 additions and 49 deletions
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67
lib/Dialect/mhlo/transforms/move_up_dynamic_broadcasts_for_fusion.cc
View File

@ -306,58 +306,25 @@ struct MergeAssumingOpsPattern : public OpRewritePattern<shape::AssumingOp> {
}
};
// Eliminate casted extent tensors. Instead, produce the concrete extent tensor
// type where possible.
struct CanonicalizeCastedShapeOfOpPattern
: public OpRewritePattern<tensor::CastOp> {
using OpRewritePattern<tensor::CastOp>::OpRewritePattern;
struct EliminateDuplicateCstrBroadcastableOps
: public OpRewritePattern<shape::CstrBroadcastableOp> {
using OpRewritePattern<shape::CstrBroadcastableOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::CastOp op,
LogicalResult matchAndRewrite(shape::CstrBroadcastableOp op,
PatternRewriter &rewriter) const override {
// Only merge tensor cast into `shape_of` ops.
auto shape_of_op = op.source().getDefiningOp<shape::ShapeOfOp>();
if (!shape_of_op) return failure();
// Search for previous occurence of the same constraint.
Operation *it = op->getPrevNode();
while (it != nullptr) {
if (auto candidate = llvm::dyn_cast<shape::CstrBroadcastableOp>(it)) {
if (candidate.shapes() == op.shapes()) {
rewriter.replaceOp(op, candidate.result());
return success();
}
}
it = it->getPrevNode();
}
// Desired type must be an extent tensor type.
auto result_ty = op.getType().dyn_cast<RankedTensorType>();
if (!result_ty || result_ty.getRank() != 1 ||
!result_ty.getElementType().isIndex())
return failure();
rewriter.replaceOpWithNewOp<shape::ShapeOfOp>(op, result_ty,
shape_of_op.arg());
if (shape_of_op->getUses().empty()) rewriter.eraseOp(shape_of_op);
return success();
}
};
// TODO(frgossen): Remove this once it has landed upstream.
struct CanonicalizeBroadcastPattern
: public OpRewritePattern<shape::BroadcastOp> {
using OpRewritePattern<shape::BroadcastOp>::OpRewritePattern;
LogicalResult matchAndRewrite(shape::BroadcastOp op,
PatternRewriter &rewriter) const override {
// Only concretize dynamic extent tensor result types.
auto resultTy = op.getType().dyn_cast<RankedTensorType>();
if (!resultTy || !resultTy.isDynamicDim(0)) return failure();
// Infer resulting shape rank if possible.
int64_t maxRank = 0;
for (Value shape : op.shapes()) {
if (auto extentTensorTy = shape.getType().dyn_cast<RankedTensorType>()) {
// Cannot infer resulting shape rank if any operand is dynamically
// ranked.
if (extentTensorTy.isDynamicDim(0)) return failure();
maxRank = std::max(maxRank, extentTensorTy.getDimSize(0));
}
}
auto newOp = rewriter.create<shape::BroadcastOp>(
op.getLoc(), RankedTensorType::get({maxRank}, rewriter.getIndexType()),
op.shapes());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
return success();
}
};
@ -432,8 +399,7 @@ void PopulateMoveUpDynamicBroadcastsForFusionPatterns(
MLIRContext *context, OwningRewritePatternList *patterns) {
// clang-format off
patterns->insert<
CanonicalizeBroadcastPattern,
CanonicalizeCastedShapeOfOpPattern,
EliminateDuplicateCstrBroadcastableOps,
InlineBroadcastedShapeOperandsPattern<shape::CstrBroadcastableOp>,
MergeAssumingOpsPattern,
MoveIntoAssumingOpPattern<shape::ShapeOfOp>,
@ -443,6 +409,7 @@ void PopulateMoveUpDynamicBroadcastsForFusionPatterns(
MoveUpBroadcastInDimOpPattern,
ShapeReificationPattern>(context);
// clang-format on
shape::BroadcastOp::getCanonicalizationPatterns(*patterns, context);
tensor::CastOp::getCanonicalizationPatterns(*patterns, context);
}

18
tests/move_up_dynamic_broadcasts_for_fusion.mlir
View File

@ -374,3 +374,21 @@ func @sub_sub(%arg0: tensor<?x32xf16>, %arg1 : tensor<?x32xf16>,
}
return %7 : tensor<?x?x32xf16>
}
// -----
// CHECK-LABEL: @redundant_cstr_broadcastable
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?xindex>, %[[ARG1:.*]]: tensor<?xindex>)
func @redundant_cstr_broadcastable(%arg0: tensor<?xindex>,
%arg1 : tensor<?xindex>) {
// CHECK-DAG: %[[WITNESS:.*]] = shape.cstr_broadcastable %[[ARG0]], %[[ARG1]]
// CHECK: shape.assuming %[[WITNESS]]
%0 = shape.cstr_broadcastable %arg0, %arg1 : tensor<?xindex>, tensor<?xindex>
%1 = shape.cstr_broadcastable %arg0, %arg1 : tensor<?xindex>, tensor<?xindex>
%2 = shape.assuming_all %0, %1
shape.assuming %2 -> () {
"some.op"() : () -> ()
shape.assuming_yield
}
return
}