diff options
author | Rafael Ubal <rubal@mathworks.com> | 2024-04-03 13:49:55 -0400 |
---|---|---|
committer | GitHub <noreply@github.com> | 2024-04-03 13:49:55 -0400 |
commit | fbcd0c65f7b2f65e0ee58e5448b88af39faf10f1 (patch) | |
tree | 8687e944da4a0c0aa4ac632c4e8e819070ed6835 | |
parent | cd29126b6333c28cc4df7b932ed0d6d6c13983d1 (diff) |
Updates to 'tosa.reshape' verifier (#87416)
This addition catches common cases of malformed `tosa.reshape` ops. This
prevents the `--tosa-to-tensor` pass from asserting when fed invalid
operations, as these will be caught ahead of time by the verifier.
Closes #87396
-rw-r--r-- | mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 17 | ||||
-rw-r--r-- | mlir/test/Dialect/Tosa/invalid.mlir | 58 |
2 files changed, 58 insertions, 17 deletions
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp index 6e6e84350738..e06ac9a27ae4 100644 --- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp +++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp @@ -955,25 +955,34 @@ LogicalResult tosa::ReshapeOp::inferReturnTypeComponents( } mlir::LogicalResult tosa::ReshapeOp::verify() { - ShapedType inputType = llvm::cast<ShapedType>(getInput1().getType()); - ShapedType outputType = llvm::cast<ShapedType>(getType()); + TensorType inputType = getInput1().getType(); + RankedTensorType outputType = getType(); if (hasZeroDimension(inputType) || hasZeroDimension(outputType)) return emitOpError() << "tensor has a dimension with size zero. Each " "dimension of a tensor must have size >= 1"; + if ((int64_t) getNewShape().size() != outputType.getRank()) + return emitOpError() << "new shape does not match result rank"; + + for (auto [newShapeDim, outputShapeDim] : + zip(getNewShape(), outputType.getShape())) + if (newShapeDim != -1 && outputShapeDim != ShapedType::kDynamic && + newShapeDim != outputShapeDim) + return emitOpError() << "new shape is inconsistent with result shape"; + if (inputType.hasStaticShape() && outputType.hasStaticShape()) { int64_t inputElementsNum = inputType.getNumElements(); int64_t outputElementsNum = outputType.getNumElements(); if (inputElementsNum != outputElementsNum) { - return emitOpError() << "Cannot reshape " << inputElementsNum + return emitOpError() << "cannot reshape " << inputElementsNum << " elements into " << outputElementsNum; } } int missingDims = llvm::count(getNewShape(), -1); if (missingDims > 1) - return emitOpError() << "At most one target dimension can be -1"; + return emitOpError() << "expected at most one target dimension to be -1"; return mlir::success(); } diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir index 38ba48f365ea..730ac41dd7a8 100644 --- a/mlir/test/Dialect/Tosa/invalid.mlir +++ b/mlir/test/Dialect/Tosa/invalid.mlir @@ -243,38 +243,70 @@ func.func @test_reshape_type_mismatch(%arg0 : tensor<13x21x3xf32>) -> () { // ----- -func.func @test_reverse_axis_out_of_range(%arg0 : tensor<13x21x3xf32>) -> () { - // expected-error@+1 {{'tosa.reverse' op expect input tensor rank (3) to be larger than reverse axis (5)}} - %0 = tosa.reverse %arg0 {axis = 5 : i32} : (tensor<13x21x3xf32>) -> tensor<?x?x?xi32> +func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<13x0x3xf32>) -> tensor<13x0x3xf32> return } // ----- -func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> { - // expected-error@+1 {{'tosa.const' op failed to verify that all of {value, output} have same shape}} - %0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32> - return %0 : tensor<100x100xf32> +func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<?x0x3xf32>) -> tensor<13x0x3xf32> + return } // ----- -func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () { - // expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}} - %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<13x0x3xf32>) -> tensor<13x0x3xf32> +func.func @test_reshape_rank_mismatch(%arg0 : tensor<?xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op new shape does not match result rank}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 4>} : (tensor<?xf32>) -> tensor<?xf32> return } // ----- -func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () { - // expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}} - %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<?x0x3xf32>) -> tensor<13x0x3xf32> +func.func @test_reshape_inconsistent_result_type(%arg0 : tensor<?xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op new shape is inconsistent with result shape}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 4, -1>} : (tensor<?xf32>) -> tensor<?x3x5xf32> + return +} + +// ----- + +func.func @test_reshape_invalid_size(%arg0 : tensor<2x4xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op cannot reshape 8 elements into 15}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 3, 5>} : (tensor<2x4xf32>) -> tensor<3x5xf32> + return +} + +// ----- + +func.func @test_reshape_invalid_placeholders(%arg0 : tensor<?xf32>) -> () { + // expected-error@+1 {{'tosa.reshape' op expected at most one target dimension to be -1}} + %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, -1, -1>} : (tensor<?xf32>) -> tensor<2x?x?xf32> return } // ----- +func.func @test_reverse_axis_out_of_range(%arg0 : tensor<13x21x3xf32>) -> () { + // expected-error@+1 {{'tosa.reverse' op expect input tensor rank (3) to be larger than reverse axis (5)}} + %0 = tosa.reverse %arg0 {axis = 5 : i32} : (tensor<13x21x3xf32>) -> tensor<?x?x?xi32> + return +} + +// ----- + +func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> { + // expected-error@+1 {{'tosa.const' op failed to verify that all of {value, output} have same shape}} + %0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32> + return %0 : tensor<100x100xf32> +} + +// ----- + func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> { // expected-error@+1 {{'tosa.conv2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}} %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} |