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[MLIR][Shape] Concretize broadcast result type if possible 

As a canonicalization, infer the resulting shape rank if possible.

Differential Revision: https://reviews.llvm.org/D102068
This commit is contained in:
Frederik Gossen 2021-05-10 10:22:23 +02:00
parent 541f107871
commit a81e45b8bc
3 changed files with 52 additions and 5 deletions
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3
mlir/include/mlir/Dialect/Shape/IR/Shape.h
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@ -29,7 +29,8 @@ class PatternRewriter;
namespace shape {
/// Alias type for extent tensors.
RankedTensorType getExtentTensorType(MLIRContext *ctx);
RankedTensorType getExtentTensorType(MLIRContext *ctx,
int64_t rank = ShapedType::kDynamicSize);
// Check if a type is an extent tensor, e.g., tensor<?xindex>.
bool isExtentTensorType(Type);

37
mlir/lib/Dialect/Shape/IR/Shape.cpp
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@ -27,8 +27,8 @@ namespace {
#include "ShapeCanonicalization.inc"
}
RankedTensorType shape::getExtentTensorType(MLIRContext *ctx) {
return RankedTensorType::get({ShapedType::kDynamicSize}, IndexType::get(ctx));
RankedTensorType shape::getExtentTensorType(MLIRContext *ctx, int64_t rank) {
return RankedTensorType::get({rank}, IndexType::get(ctx));
}
bool shape::isExtentTensorType(Type type) {
@ -660,11 +660,42 @@ struct CanonicalizeCastExtentTensorOperandsPattern
return success();
}
};
struct BroadcastConcretizeResultTypePattern
: public OpRewritePattern<BroadcastOp> {
using OpRewritePattern<BroadcastOp>::OpRewritePattern;
LogicalResult matchAndRewrite(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<BroadcastOp>(
op.getLoc(), getExtentTensorType(getContext(), maxRank), op.shapes());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
return success();
}
};
} // namespace
void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
MLIRContext *context) {
patterns.add<BroadcastFoldConstantOperandsPattern,
patterns.add<BroadcastConcretizeResultTypePattern,
BroadcastFoldConstantOperandsPattern,
BroadcastForwardSingleOperandPattern,
CanonicalizeCastExtentTensorOperandsPattern<BroadcastOp>,
RemoveDuplicateOperandsPattern<BroadcastOp>,

17
mlir/test/Dialect/Shape/canonicalize.mlir
View File

@ -1344,7 +1344,8 @@ func @cast_extent_tensor_operands(%arg0 : tensor<?xindex>,
%arg1 : tensor<3xindex>) -> (!shape.witness, tensor<?xindex>) {
// CHECK: %[[CAST_ARG0:.*]] = tensor.cast %[[ARG0]] : tensor<?xindex> to tensor<3xindex>
// CHECK: %[[WIT:.*]] = shape.cstr_broadcastable %[[CAST_ARG0]], %[[ARG1]] : tensor<3xindex>, tensor<3xindex>
// CHECK: %[[RES:.*]] = shape.broadcast %[[CAST_ARG0]], %[[ARG1]] : tensor<3xindex>, tensor<3xindex>
// CHECK: %[[UNCAST_RES:.*]] = shape.broadcast %[[CAST_ARG0]], %[[ARG1]] : tensor<3xindex>, tensor<3xindex> -> tensor<3xindex>
// CHECK: %[[RES:.*]] = tensor.cast %[[UNCAST_RES]] : tensor<3xindex> to tensor<?xindex>
// CHECK: return %[[WIT]], %[[RES]]
%0 = tensor.cast %arg0 : tensor<?xindex> to tensor<3xindex>
%1 = tensor.cast %arg1 : tensor<3xindex> to tensor<?xindex>
@ -1353,3 +1354,17 @@ func @cast_extent_tensor_operands(%arg0 : tensor<?xindex>,
-> tensor<?xindex>
return %2, %3 : !shape.witness, tensor<?xindex>
}
// -----
// CHECK-LABEL: @concretize_broadcast_result_type
// CHECK-SAME: (%[[ARG0:.*]]: tensor<2xindex>, %[[ARG1:.*]]: tensor<3xindex>)
func @concretize_broadcast_result_type(%arg0 : tensor<2xindex>,
%arg1 : tensor<3xindex>) -> tensor<?xindex> {
// CHECK: %[[CONCR:.*]] = shape.broadcast %[[ARG0]], %[[ARG1]] : tensor<2xindex>, tensor<3xindex> -> tensor<3xindex>
// CHECK: %[[RES:.*]] = tensor.cast %[[CONCR]] : tensor<3xindex> to tensor<?xindex>
// CHECK: return %[[RES]]
%0 = shape.broadcast %arg0, %arg1 : tensor<2xindex>, tensor<3xindex>
-> tensor<?xindex>
return %0 : tensor<?xindex>
}