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[mlir][arith][bufferize] Fix tensors with different layouts after bufferization 

Insert a cast if the two tensors with identical layout (that are passed to `arith.select`) have different layout maps after bufferization.

Differential Revision: https://reviews.llvm.org/D123321
This commit is contained in:
Matthias Springer 2022-04-08 18:03:18 +09:00
parent 5626bd4289
commit 8b09141909
2 changed files with 35 additions and 0 deletions
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21
mlir/lib/Dialect/Arithmetic/Transforms/BufferizableOpInterfaceImpl.cpp
View File

@ -129,6 +129,7 @@ struct SelectOpInterface
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
BufferizationState &state) const {
auto selectOp = cast<arith::SelectOp>(op);
Location loc = selectOp.getLoc();
// `getBuffer` introduces copies if an OpOperand bufferizes out-of-place.
// TODO: It would be more efficient to copy the result of the `select` op
@ -139,6 +140,26 @@ struct SelectOpInterface
*state.getBuffer(rewriter, selectOp->getOpOperand(1) /*true_value*/);
Value falseBuffer =
*state.getBuffer(rewriter, selectOp->getOpOperand(2) /*false_value*/);
// The "true" and the "false" operands must have the same type. If the
// buffers have different types, they differ only in their layout map. Cast
// both of them to the most dynamic MemRef type.
if (trueBuffer.getType() != falseBuffer.getType()) {
auto trueType = trueBuffer.getType().cast<MemRefType>();
auto tensorType = selectOp.getTrueValue().getType().cast<TensorType>();
int64_t dynamicOffset = ShapedType::kDynamicStrideOrOffset;
SmallVector<int64_t> dynamicStrides(tensorType.getRank(),
ShapedType::kDynamicStrideOrOffset);
AffineMap stridedLayout = makeStridedLinearLayoutMap(
dynamicStrides, dynamicOffset, op->getContext());
BaseMemRefType castedType = bufferization::getMemRefType(
tensorType, state.getOptions(), AffineMapAttr::get(stridedLayout),
trueType.getMemorySpace());
trueBuffer = rewriter.create<memref::CastOp>(loc, castedType, trueBuffer);
falseBuffer =
rewriter.create<memref::CastOp>(loc, castedType, falseBuffer);
}
replaceOpWithNewBufferizedOp<arith::SelectOp>(
rewriter, op, selectOp.getCondition(), trueBuffer, falseBuffer);
return success();

14
mlir/test/Dialect/Bufferization/Transforms/one-shot-bufferize.mlir
View File

@ -105,4 +105,18 @@ func @copy_deallocated() -> tensor<10xf32> {
return %0 : tensor<10xf32>
}
// -----
// CHECK-LABEL: func @select_different_tensors(
// CHECK-SAME: %[[t:.*]]: tensor<?xf32>
func @select_different_tensors(%t: tensor<?xf32>, %sz: index, %c: i1) -> tensor<?xf32> {
// CHECK-DAG: %[[m:.*]] = bufferization.to_memref %[[t]] : memref<?xf32, #{{.*}}>
// CHECK-DAG: %[[alloc:.*]] = memref.alloc(%{{.*}}) {{.*}} : memref<?xf32>
%0 = linalg.init_tensor [%sz] : tensor<?xf32>
// A cast must be inserted because %t and %0 have different memref types.
// CHECK: %[[casted:.*]] = memref.cast %[[alloc]] : memref<?xf32> to memref<?xf32, #{{.*}}>
// CHECK: arith.select %{{.*}}, %[[casted]], %[[m]]
%1 = arith.select %c, %0, %t : tensor<?xf32>
return %1 : tensor<?xf32>
}