SmallVector<Value, 4> lbs, ubs, steps;
unpackRanges(loopRanges, lbs, ubs, steps);
+ auto dropNonShapedValues =
+ [](ArrayRef<OpOperand *> operands) -> SmallVector<Value, 2> {
+ SmallVector<Value, 2> filteredOperands;
+ for (OpOperand *operand : operands) {
+ Type type = operand->get().getType();
+ if (type.isa<ShapedType>())
+ filteredOperands.push_back(operand->get());
+ }
+ return filteredOperands;
+ };
+ auto inputOperands = dropNonShapedValues(linalgOp.getInputOperands());
+ auto outputOperands = dropNonShapedValues(linalgOp.getOutputOperands());
+
auto wrappedBuilderFn = [&](OpBuilder &nestedBuilder, Location nestedLoc,
ValueRange ivs, ValueRange inputs,
ValueRange outputs) {
bodyBuilderFn(nestedBuilder, nestedLoc, ivs, outputTensors);
nestedBuilder.create<linalg::YieldOp>(nestedLoc, results);
};
-
- SmallVector<Value> inputOperands = linalgOp.getInputOperands();
- SmallVector<Value> outputOperands = linalgOp.getOutputOperands();
auto tiledLoop =
b.create<TiledLoopOp>(loc, lbs, ubs, steps, inputOperands, outputOperands,
b.getArrayAttr(iteratorTypes), wrappedBuilderFn);
// TLOOP-SAME: ins (%{{.*}} = %[[ARG_0]]: [[TY]], %{{.*}} = %[[ARG_1]]: [[TY]])
// TLOOP-SAME: outs (%{{.*}} = %[[INIT]]: [[TY]])
// TLOOP-SAME: distribution["block_x", "block_y", "none"] {
+
+
+func @fill(%arg0 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
+ %c0 = constant 0.0 : f32
+ %0 = linalg.fill(%c0, %arg0) : f32, tensor<?x?x?xf32> -> tensor<?x?x?xf32>
+ return %0 : tensor<?x?x?xf32>
+}
+// CHECK-LABEL: func @fill
+
+// TLOOP-LABEL: func @fill
+// TLOOP-NOT: ins
+// TLOOP: tensor.extract_slice
+// TLOOP-NEXT: linalg.fill
+// TLOOP-NEXT: tensor.insert_slice