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[mlir][LowerToAffineLoops] Handle tensors of rank 0

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This will fix the case:

  $ toyc -emit=jit test.toy
  $ cat test.toy
  def main() {
    var a = 1;
    print(a);
  }

Without this patch it would trigger an assertion.

Differential Revision: https://reviews.llvm.org/D77464
This commit is contained in:
Djordje Todorovic 2020-04-04 18:32:43 +02:00 committed by Djordje Todorovic
parent 6a800f6f62
commit da5fe23e84
3 changed files with 26 additions and 9 deletions
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12
mlir/examples/toy/Ch5/mlir/LowerToAffineLoops.cpp
View File

@ -155,9 +155,15 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// operations.
auto valueShape = memRefType.getShape();
SmallVector<Value, 8> constantIndices;
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
// will need to generate a store for each of the elements. The following

11
mlir/examples/toy/Ch6/mlir/LowerToAffineLoops.cpp
View File

@ -155,10 +155,15 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// operations.
auto valueShape = memRefType.getShape();
SmallVector<Value, 8> constantIndices;
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
// will need to generate a store for each of the elements. The following
// functor recursively walks the dimensions of the constant shape,

12
mlir/examples/toy/Ch7/mlir/LowerToAffineLoops.cpp
View File

@ -155,9 +155,15 @@ struct ConstantOpLowering : public OpRewritePattern<toy::ConstantOp> {
// operations.
auto valueShape = memRefType.getShape();
SmallVector<Value, 8> constantIndices;
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
if (!valueShape.empty()) {
for (auto i : llvm::seq<int64_t>(
0, *std::max_element(valueShape.begin(), valueShape.end())))
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, i));
} else {
// This is the case of a tensor of rank 0.
constantIndices.push_back(rewriter.create<ConstantIndexOp>(loc, 0));
}
// The constant operation represents a multi-dimensional constant, so we
// will need to generate a store for each of the elements. The following