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[mlir][tosa] Allow unranked input/output tensors in resize ops #141608

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Merged
merged 1 commit into from
May 29, 2025
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[mlir][tosa] Allow unranked input/output tensors in resize ops #141608

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20 changes: 10 additions & 10 deletions mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2496,16 +2496,6 @@ LogicalResult tosa::ResizeOp::verify() {
const RankedTensorType outputType =
llvm::dyn_cast<RankedTensorType>(output.getType());

if (!inputType)
return emitOpError("expect a ranked input tensor");
if (!outputType)
return emitOpError("expect a ranked output tensor");

const int64_t oh = outputType.getDimSize(1);
const int64_t ow = outputType.getDimSize(2);
const int64_t ih = inputType.getDimSize(1);
const int64_t iw = inputType.getDimSize(2);

SmallVector<int64_t> scaleValues;
SmallVector<int64_t> offsetValues;
SmallVector<int64_t> borderValues;
Expand All @@ -2531,6 +2521,16 @@ LogicalResult tosa::ResizeOp::verify() {
const int64_t borderY = borderValues[0];
const int64_t borderX = borderValues[1];

if (!inputType)
return success();
if (!outputType)
return success();

const int64_t oh = outputType.getDimSize(1);
const int64_t ow = outputType.getDimSize(2);
const int64_t ih = inputType.getDimSize(1);
const int64_t iw = inputType.getDimSize(2);

// Don't check with input height that could be broadcast (ih != 1)
// since Linalg, a consumer of TOSA, expects broadcasting support
// in resize to be available. Taking the cautious approach for now,
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20 changes: 20 additions & 0 deletions mlir/test/Dialect/Tosa/ops.mlir
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Expand Up @@ -743,6 +743,26 @@ func.func @test_resize(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32> {
return %1 : tensor<1x64x64x8xf32>
}

// -----
// CHECK-LABEL: resize_unranked_output
func.func @test_resize_unranked_output(%arg0: tensor<1x32x32x8xf32>) -> tensor<*xf32> {
%scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
%offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
%border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
%1 = tosa.resize %arg0, %scale, %offset, %border { mode = "BILINEAR" } : (tensor<1x32x32x8xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<*xf32>
return %1 : tensor<*xf32>
}

// -----
// CHECK-LABEL: resize_unranked_input
func.func @test_resize_unranked_input(%arg0: tensor<*xf32>) -> tensor<1x64x64x8xf32> {
%scale = tosa.const_shape { values = dense<[4, 2, 4, 2]> : tensor<4xindex> } : () -> !tosa.shape<4>
%offset = tosa.const_shape { values = dense<[-1, -1]> : tensor<2xindex> } : () -> !tosa.shape<2>
%border = tosa.const_shape { values = dense<[1, 1]> : tensor<2xindex> } : () -> !tosa.shape<2>
%1 = tosa.resize %arg0, %scale, %offset, %border { mode = "BILINEAR" } : (tensor<*xf32>, !tosa.shape<4>, !tosa.shape<2>, !tosa.shape<2>) -> tensor<1x64x64x8xf32>
return %1 : tensor<1x64x64x8xf32>
}

// -----
// CHECK-LABEL: cast
func.func @test_cast1(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
Expand Down