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[mlir][vector] Fix TransferOpReduceRank for 0-D tensors 

We cannot unconditionally generate memref.load ops for such cases;
need to check the source's type.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D114376
This commit is contained in:
Lei Zhang 2021-11-22 12:28:39 -05:00
parent 9c5982ef8e
commit 93284120f2
2 changed files with 27 additions and 5 deletions
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8
mlir/lib/Dialect/Vector/VectorTransferPermutationMapRewritePatterns.cpp
View File

@ -224,9 +224,15 @@ struct TransferOpReduceRank : public OpRewritePattern<vector::TransferReadOp> {
// https://llvm.discourse.group/t/should-we-have-0-d-vectors/3097.
// In the meantime, lower these to a scalar load when they pop up.
if (reducedShapeRank == 0) {
Value newRead = rewriter.create<memref::LoadOp>(
Value newRead;
if (op.getShapedType().isa<TensorType>()) {
newRead = rewriter.create<tensor::ExtractOp>(op.getLoc(), op.source(),
op.indices());
} else {
newRead = rewriter.create<memref::LoadOp>(
op.getLoc(), originalVecType.getElementType(), op.source(),
op.indices());
}
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(op, originalVecType,
newRead);
return success();

20
mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir
View File

@ -1,9 +1,9 @@
// RUN: mlir-opt %s -test-vector-transfer-lowering-patterns -canonicalize -split-input-file | FileCheck %s
// CHECK-LABEL: func @vector_transfer_ops_0d(
// CHECK-LABEL: func @vector_transfer_ops_0d_memref(
// CHECK-SAME: %[[MEM:.*]]: memref<f32>
// CHECK-SAME: %[[VV:.*]]: vector<1x1x1xf32>
func @vector_transfer_ops_0d(%M: memref<f32>, %v: vector<1x1x1xf32>) {
func @vector_transfer_ops_0d_memref(%M: memref<f32>, %v: vector<1x1x1xf32>) {
%f0 = arith.constant 0.0 : f32
// CHECK-NEXT: %[[V:.*]] = memref.load %[[MEM]][] : memref<f32>
@ -23,6 +23,22 @@ func @vector_transfer_ops_0d(%M: memref<f32>, %v: vector<1x1x1xf32>) {
// -----
// CHECK-LABEL: func @vector_transfer_ops_0d_tensor(
// CHECK-SAME: %[[SOURCE:.*]]: tensor<f32>
func @vector_transfer_ops_0d_tensor(%M: tensor<f32>) -> vector<1xf32> {
%f0 = arith.constant 0.0 : f32
// CHECK-NEXT: %[[S:.*]] = tensor.extract %[[SOURCE]][] : tensor<f32>
// CHECK-NEXT: %[[V:.*]] = vector.broadcast %[[S]] : f32 to vector<1xf32>
%0 = vector.transfer_read %M[], %f0 {permutation_map = affine_map<()->(0)>} :
tensor<f32>, vector<1xf32>
// CHECK-NEXT: return %[[V]]
return %0: vector<1xf32>
}
// -----
// transfer_read/write are lowered to vector.load/store
// CHECK-LABEL: func @transfer_to_load(
// CHECK-SAME: %[[MEM:.*]]: memref<8x8xf32>,