| // RUN: mlir-opt %s -split-input-file -verify-diagnostics |
| |
| // Asking the dimension of a 0-D shape doesn't make sense. |
| func.func @dim_0_ranked(%arg : tensor<f32>, %arg1 : index) { |
| tensor.dim %arg, %arg1 : tensor<f32> // expected-error {{'tensor.dim' op operand #0 must be non-0-ranked or unranked tensor, but got 'tensor<f32>'}} |
| return |
| } |
| |
| // ----- |
| |
| func.func @tensor.cast_mismatching_constants(%arg0: tensor<1xf32>) { |
| // expected-error@+1 {{operand type 'tensor<1xf32>' and result type 'tensor<2xf32>' are cast incompatible}} |
| %0 = tensor.cast %arg0 : tensor<1xf32> to tensor<2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_empty() { |
| // expected-error@+1 {{requires at least one input}} |
| %0 = tensor.concat dim(0) : () -> tensor<1x2x3xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_rank_mismatch(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) { |
| // expected-error@+1 {{rank of concatenated inputs must match result rank}} |
| %0 = tensor.concat dim(0) %arg0, %arg1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_dim_out_of_range(%arg0: tensor<3xf32>) { |
| // expected-error@+1 {{concatenation dim must be less than the tensor rank}} |
| %0 = tensor.concat dim(1) %arg0 : (tensor<3xf32>) -> tensor<3xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_element_type_mismatch(%arg0: tensor<3xf32>, %arg1: tensor<3xi32>) { |
| // expected-error@+1 {{inputs and result element type must match}} |
| %0 = tensor.concat dim(0) %arg0, %arg1 : (tensor<3xf32>, tensor<3xi32>) -> tensor<3xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_incompatible_input_types(%arg0: tensor<3x4xf32>, %arg1: tensor<4x5xf32>) { |
| // expected-error@+1 {{static concatenation size mismatch along non-concatenated dimension 1}} |
| %0 = tensor.concat dim(0) %arg0, %arg1 : (tensor<3x4xf32>, tensor<4x5xf32>) -> tensor<7x5xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @concat_static_shape_mismatch(%arg0: tensor<3xf32>) { |
| // expected-error@+1 {{result type 'tensor<7xf32>'does not match inferred shape 'tensor<6xf32>' static sizes}} |
| %0 = tensor.concat dim(0) %arg0, %arg0 : (tensor<3xf32>, tensor<3xf32>) -> tensor<7xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_too_many_indices(%arg0: tensor<?xf32>) { |
| // expected-error@+1 {{incorrect number of indices for extract_element}} |
| %0 = tensor.extract %arg0[] : tensor<?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_too_many_indices(%arg0: f32, %arg1: tensor<?xf32>) { |
| // expected-error@+1 {{incorrect number of indices}} |
| %0 = tensor.insert %arg0 into %arg1[] : tensor<?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @tensor.from_elements_wrong_result_type() { |
| // expected-error@+2 {{'tensor.from_elements' invalid kind of type specified: expected builtin.tensor, but found 'tensor<*xi32>'}} |
| %c0 = arith.constant 0 : i32 |
| %0 = tensor.from_elements %c0 : tensor<*xi32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @tensor.from_elements_wrong_elements_count() { |
| // expected-error@+2 {{number of operands and types do not match: got 1 operands and 2 types}} |
| %c0 = arith.constant 0 : index |
| %0 = tensor.from_elements %c0 : tensor<2xindex> |
| return |
| } |
| |
| // ----- |
| |
| func.func @tensor.generate(%m : index) |
| -> tensor<?x3x?xf32> { |
| // expected-error @+1 {{incorrect number of dynamic sizes, has 1, expected 2}} |
| %tnsr = tensor.generate %m { |
| ^bb0(%i : index, %j : index, %k : index): |
| %elem = arith.constant 8.0 : f32 |
| tensor.yield %elem : f32 |
| } : tensor<?x3x?xf32> |
| return %tnsr : tensor<?x3x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.generate(%m : index, %n : index) |
| -> tensor<?x3x?xf32> { |
| // expected-error @+1 {{must have one body argument per input dimension}} |
| %tnsr = tensor.generate %m, %n { |
| ^bb0(%i : index, %j : index): |
| %elem = arith.constant 8.0 : f32 |
| tensor.yield %elem : f32 |
| } : tensor<?x3x?xf32> |
| return %tnsr : tensor<?x3x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.generate(%m : index, %n : index) |
| -> tensor<?x3x?xf32> { |
| // expected-error @+1 {{all body arguments must be index}} |
| %tnsr = tensor.generate %m, %n { |
| ^bb0(%i : index, %j : index, %k : i64): |
| %elem = arith.constant 8.0 : f32 |
| tensor.yield %elem : f32 |
| } : tensor<?x3x?xf32> |
| return %tnsr : tensor<?x3x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.generate(%m : index, %n : index) |
| -> tensor<?x3x?xf32> { |
| // expected-error @+4 {{'func.return' op expects parent op 'func.func'}} |
| %tnsr = tensor.generate %m, %n { |
| ^bb0(%i : index, %j : index, %k : index): |
| %elem = arith.constant 8.0 : f32 |
| func.return %elem : f32 |
| } : tensor<?x3x?xf32> |
| return %tnsr : tensor<?x3x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.generate(%m : index, %n : index) |
| -> tensor<?x3x?xf32> { |
| // expected-error @+1 {{body must be terminated with a `yield` operation of the tensor element type}} |
| %tnsr = tensor.generate %m, %n { |
| ^bb0(%i : index, %j : index, %k : index): |
| %elem = arith.constant 8 : i32 |
| tensor.yield %elem : i32 |
| } : tensor<?x3x?xf32> |
| return %tnsr : tensor<?x3x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.reshape_element_type_mismatch( |
| %buf: tensor<*xf32>, %shape: tensor<1xi32>) { |
| // expected-error @+1 {{element types of source and destination tensor types should be the same}} |
| tensor.reshape %buf(%shape) : (tensor<*xf32>, tensor<1xi32>) -> tensor<?xi32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.reshape_dst_ranked_shape_unranked( |
| %buf: tensor<*xf32>, %shape: tensor<?xi32>) { |
| // expected-error @+1 {{cannot use shape operand with dynamic length to reshape to statically-ranked tensor type}} |
| tensor.reshape %buf(%shape) : (tensor<*xf32>, tensor<?xi32>) -> tensor<?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.reshape_dst_shape_rank_mismatch( |
| %buf: tensor<*xf32>, %shape: tensor<1xi32>) { |
| // expected-error @+1 {{length of shape operand differs from the result's tensor rank}} |
| tensor.reshape %buf(%shape) |
| : (tensor<*xf32>, tensor<1xi32>) -> tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @tensor.reshape_num_elements_mismatch( |
| %buf: tensor<1xf32>, %shape: tensor<1xi32>) { |
| // expected-error @+1 {{source and destination tensor should have the same number of elements}} |
| tensor.reshape %buf(%shape) |
| : (tensor<1xf32>, tensor<1xi32>) -> tensor<10xf32> |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_wrong_result_rank(%t: tensor<?xf32>, %idx : index) { |
| // expected-error @+1 {{expected rank to be smaller or equal to the other rank.}} |
| %0 = tensor.extract_slice %t[0][4][1] : tensor<?xf32> to tensor<?x?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_wrong_result_rank(%t: tensor<?xf32>, %idx : index) { |
| // expected-error @+1 {{expected element type to be 'f32'}} |
| %0 = tensor.extract_slice %t[0][4][1] : tensor<?xf32> to tensor<4xi8> |
| return |
| } |
| |
| |
| // ----- |
| |
| func.func @extract_slice_size_and_output_dim_mismatch_static_size(%t: tensor<16xf32>) { |
| // expected-error @+1 {{expected type to be 'tensor<4xf32>' or a rank-reduced version. (size mismatch)}} |
| %0 = tensor.extract_slice %t[0][4][1] |
| : tensor<16xf32> to tensor<6xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_size_and_output_dim_mismatch_dynamic_size(%t: tensor<?xf32>, %idx : index) { |
| // expected-error @+2 {{expected type to be 'tensor<?xf32>' or a rank-reduced version. (size mismatch)}} |
| %c4 = arith.constant 4 : index |
| %0 = tensor.extract_slice %t[0][%c4][1] : tensor<?xf32> to tensor<4xi8> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_wrong_static_type(%t: tensor<8x16x4xf32>, %idx : index) { |
| // expected-error @+1 {{expected type to be 'tensor<?x4x4xf32>' or a rank-reduced version. (size mismatch)}} |
| %0 = tensor.extract_slice %t[0, 0, 0][%idx, 4, 4][1, 1, 1] |
| : tensor<8x16x4xf32> to tensor<4x4x4xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_wrong_dynamic_type(%t: tensor<8x16x4xf32>, %idx : index) { |
| // expected-error @+1 {{expected type to be 'tensor<4x4x4xf32>' or a rank-reduced version. (size mismatch)}} |
| %0 = tensor.extract_slice %t[0, 2, 0][4, 4, 4][1, 1, 1] |
| : tensor<8x16x4xf32> to tensor<?x4x4xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @illegal_num_offsets(%arg0 : tensor<?x?x?xf32>, %arg1 : index, %arg2 : index) { |
| // expected-error@+1 {{expected 3 offset values}} |
| %0 = tensor.extract_slice %arg0[0, 0] [%arg1, %arg2] [1, 1] : tensor<?x?x?xf32> to tensor<?x?x?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_offset_out_of_bounds(%arg0: tensor<10xf32>) { |
| // expected-error@+1 {{offset 0 is out-of-bounds: 10 >= 10}} |
| %0 = tensor.extract_slice %arg0 [10][1][1] : tensor<10xf32> to tensor<1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @extract_slice_runs_out_of_bounds(%arg0: tensor<9xf32>) { |
| // expected-error@+1 {{slice along dimension 0 runs out-of-bounds: 9 >= 9}} |
| %0 = tensor.extract_slice %arg0 [3][4][2] : tensor<9xf32> to tensor<4xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_wrong_result_rank(%t1: tensor<?xf32>, %t2: tensor<?x?xf32>, %idx : index) { |
| // expected-error @+1 {{expected rank to be smaller or equal to the other rank.}} |
| %0 = tensor.insert_slice %t2 into %t1[0][4][1] : tensor<?x?xf32> into tensor<?xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_wrong_result_rank(%t1: tensor<4xi8>, %t2: tensor<?xf32>, %idx : index) { |
| // expected-error @+1 {{expected element type to be 'f32'}} |
| %0 = tensor.insert_slice %t1 into %t2[0][4][1] : tensor<4xi8> into tensor<?xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_wrong_static_type(%t1: tensor<4x4x4xf32>, %t2: tensor<8x16x4xf32>, %idx : index) { |
| // expected-error @+1 {{expected type to be 'tensor<?x4x4xf32>' or a rank-reduced version. (size mismatch)}} |
| %0 = tensor.insert_slice %t1 into %t2[0, 0, 0][%idx, 4, 4][1, 1, 1] |
| : tensor<4x4x4xf32> into tensor<8x16x4xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_wrong_dynamic_type(%t1: tensor<?x4x4xf32>, %t2: tensor<8x16x4xf32>, %idx : index) { |
| // expected-error @+1 {{expected type to be 'tensor<4x4x4xf32>' or a rank-reduced version. (size mismatch)}} |
| %0 = tensor.insert_slice %t1 into %t2[0, 2, 0][4, 4, 4][1, 1, 1] |
| : tensor<?x4x4xf32> into tensor<8x16x4xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_offset_out_of_bounds(%arg0: tensor<1xf32>, %arg1: tensor<10xf32>) { |
| // expected-error@+1 {{offset 0 is out-of-bounds: 10 >= 10}} |
| %0 = tensor.insert_slice %arg0 into %arg1 [10][1][1] : tensor<1xf32> into tensor<10xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @insert_slice_runs_out_of_bounds(%arg0: tensor<4xf32>, %arg1: tensor<9xf32>) { |
| // expected-error@+1 {{slice along dimension 0 runs out-of-bounds: 9 >= 9}} |
| %0 = tensor.insert_slice %arg0 into %arg1 [3][4][2] : tensor<4xf32> into tensor<9xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @illegal_expanding_reshape_static_tensor |
| (%arg0: tensor<2x3x20xf32>) -> tensor<2x3x2x4x5xf32> { |
| // expected-error @+1 {{expected dimension 2 of collapsed type to be static value of 40}} |
| %0 = tensor.expand_shape %arg0 [[0], [1], [2, 3, 4]] output_shape [2, 3, 2, 4, 5] |
| : tensor<2x3x20xf32> into tensor<2x3x2x4x5xf32> |
| return %0 : tensor<2x3x2x4x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_collapsing_reshape_static_tensor |
| (%arg0: tensor<2x3x2x4x5xf32>) -> tensor<2x3x20xf32> { |
| // expected-error @+1 {{expected dimension 2 of collapsed type to be static value of 40}} |
| %0 = tensor.collapse_shape %arg0 [[0], [1], [2, 3, 4]] |
| : tensor<2x3x2x4x5xf32> into tensor<2x3x20xf32> |
| return %0 : tensor<2x3x20xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_expanding_reshape_mixed_tensor(%arg0 : tensor<?x?xf32>, %sz0: index) |
| -> tensor<?x4x5xf32> { |
| // expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 5}} |
| %0 = tensor.expand_shape %arg0 [[0, 1], [2]] output_shape [%sz0, 4, 5] |
| : tensor<?x?xf32> into tensor<?x4x5xf32> |
| return %0 : tensor<?x4x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_expanding_reshape_mixed_tensor_2(%arg0 : tensor<?x?xf32>, %sz0: index) |
| -> tensor<?x4x5xf32> { |
| // expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 20}} |
| %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%sz0, 4, 5] |
| : tensor<?x?xf32> into tensor<?x4x5xf32> |
| return %0 : tensor<?x4x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @expand_shape_illegal_output_shape(%arg0: tensor<2xf32>) { |
| // expected-error @+1 {{expected number of static shape dims to be equal to the output rank (3) but found 2 inputs instead}} |
| %0 = tensor.expand_shape %arg0 [[0, 1, 2]] output_shape [1, 2] : tensor<2xf32> into tensor<1x1x2xf32> |
| return |
| } |
| |
| |
| // ----- |
| |
| func.func @illegal_collapsing_reshape_mixed_tensor(%arg0 : tensor<?x4x5xf32>) -> tensor<?x?xf32> { |
| // expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 5}} |
| %0 = tensor.collapse_shape %arg0 [[0, 1], [2]] |
| : tensor<?x4x5xf32> into tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_collapsing_reshape_mixed_tensor_2(%arg0 : tensor<?x4x5xf32>) |
| -> tensor<?x?xf32> { |
| // expected-error @+1 {{expected dimension 1 of collapsed type to be static value of 20}} |
| %0 = tensor.collapse_shape %arg0 [[0], [1, 2]] |
| : tensor<?x4x5xf32> into tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @rank(%0: f32) { |
| // expected-error@+1 {{'tensor.rank' op operand #0 must be tensor of any type values}} |
| "tensor.rank"(%0): (f32)->index |
| return |
| } |
| |
| // ----- |
| |
| func.func @illegal_num_offsets(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?x?xf32>, |
| %arg2 : index, %arg3 : index) { |
| // expected-error@+1 {{expected 3 offset values}} |
| %0 = tensor.insert_slice %arg0 into %arg1[0, 0] [%arg2, %arg3] [1, 1] : tensor<?x?xf32> into tensor<?x?x?xf32> |
| return |
| } |
| |
| // ----- |
| |
| |
| func.func @pad_result_type(%arg0: tensor<?x2x3x4xi32>, %arg1: index, %arg2: i32) -> tensor<?x?x?x8xf32> { |
| // expected-error @+1 {{specified type 'tensor<?x?x?x8xf32>' does not match the inferred type 'tensor<?x?x?x9xi32>}} |
| %0 = tensor.pad %arg0 low[1, %arg1, 2, 2] high[1, 2, %arg1, 3] { |
| ^bb0(%arg3: index, %arg4: index): |
| tensor.yield %arg2 : i32 |
| } : tensor<?x2x3x4xi32> to tensor<?x?x?x8xf32> |
| return %0 : tensor<?x?x?x8xf32> |
| } |
| |
| // ----- |
| |
| func.func @pad_number_of_block_args(%arg0: tensor<?x4xi32>, %arg1: i32) -> tensor<?x9xi32> { |
| // expected-error @+1 {{expected the block to have 2 arguments}} |
| %0 = tensor.pad %arg0 low[1, 2] high[2, 3] { |
| ^bb0(%arg2: index, %arg3: index, %arg4: index): |
| tensor.yield %arg1 : i32 |
| } : tensor<?x4xi32> to tensor<?x9xi32> |
| return %0 : tensor<?x9xi32> |
| } |
| |
| // ----- |
| |
| func.func @pad_block_args(%arg0: tensor<?x4xi32>, %arg1: i32) -> tensor<?x9xi32> { |
| // expected-error @+1 {{op expected block argument 1 to be an index}} |
| %0 = tensor.pad %arg0 low[1, 2] high[2, 3] { |
| ^bb0(%arg2: i32, %arg3: i32): |
| tensor.yield %arg1 : i32 |
| } : tensor<?x4xi32> to tensor<?x9xi32> |
| return %0 : tensor<?x9xi32> |
| } |
| |
| // ----- |
| |
| func.func @pad_yield_type(%arg0: tensor<?x4xi32>, %arg1: i8) -> tensor<?x9xi32> { |
| // expected-error @+1 {{op expected yield type to match shape element type}} |
| %0 = tensor.pad %arg0 low[1, 2] high[2, 3] { |
| ^bb0(%arg2: index, %arg3: index): |
| tensor.yield %arg1 : i8 |
| } : tensor<?x4xi32> to tensor<?x9xi32> |
| return %0 : tensor<?x9xi32> |
| } |
| |
| // ----- |
| |
| func.func @invalid_splat(%v : f32) { |
| // expected-error@+1 {{invalid kind of type specified: expected builtin.tensor, but found 'memref<8xf32>'}} |
| tensor.splat %v : memref<8xf32> |
| return |
| } |
| |
| // ----- |
| |
| // expected-note@+1 {{prior use here}} |
| func.func @invalid_splat(%v : f32) { |
| // expected-error@+1 {{expects different type than prior uses: 'i32' vs 'f32'}} |
| %w = tensor.splat %v : tensor<1xi32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_splat(%v: f32, %m: index) { |
| // expected-error@+1 {{incorrect number of dynamic sizes, has 1, expected 2}} |
| %w = tensor.splat %v[%m] : tensor<?x8x?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_empty_dims( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x3xindex>) { |
| // expected-error@+1 {{gather_dims must be non-empty}} |
| %out = tensor.gather %source[%indices] gather_dims([]): |
| (tensor<4x5x6xf32>, tensor<1x2x3xindex>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_rank_overflow( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x3xindex>) { |
| // expected-error@+1 {{gather_dims overflow source rank}} |
| %out = tensor.gather %source[%indices] gather_dims([0, 1, 2, 3]): |
| (tensor<4x5x6xf32>, tensor<1x2x3xindex>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_rank_mismatch0( |
| %source: tensor<4x5x6xf32>, %indices: tensor<index>) { |
| // expected-error@+1 {{gather_dims length must match the size of last dimension of indices}} |
| %out = tensor.gather %source[%indices] gather_dims([0, 1, 2]): |
| (tensor<4x5x6xf32>, tensor<index>) -> tensor<1x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_rank_mismatch1( |
| %source: tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{gather_dims length must match the size of last dimension of indices}} |
| %out = tensor.gather %source[%indices] gather_dims([0, 1, 2]): |
| (tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_negative( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x1xindex>) { |
| // expected-error@+1 {{gather_dims value must be non-negative}} |
| %out = tensor.gather %source[%indices] gather_dims([-1]): |
| (tensor<4x5x6xf32>, tensor<1x2x1xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_overflow( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x1xindex>) { |
| // expected-error@+1 {{gather_dims value must be smaller than source rank}} |
| %out = tensor.gather %source[%indices] gather_dims([42]): |
| (tensor<4x5x6xf32>, tensor<1x2x1xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_coordinate_increase( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{gather_dims values must be strictly increasing}} |
| %out = tensor.gather %source[%indices] gather_dims([1, 0]): |
| (tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2x1x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @gather_wrong_result_type( |
| %source : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{result type mismatch: expected 'tensor<1x2x1x5x1xf32>' or its rank-reduced variant 'tensor<1x2x5xf32>' (got: 'tensor<1x2x1xf32>')}} |
| %out = tensor.gather %source[%indices] gather_dims([0, 2]): |
| (tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_empty_dims( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x3xindex>) { |
| // expected-error@+1 {{scatter_dims must be non-empty}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x3xindex>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_rank_overflow( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x3xindex>) { |
| // expected-error@+1 {{scatter_dims overflow dest rank}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([0, 1, 2, 3]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x3xindex>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_rank_mismatch0( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<index>) { |
| // expected-error@+1 {{scatter_dims length must match the size of last dimension of indices}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([0, 1, 2]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<index>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_rank_mismatch1( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{scatter_dims length must match the size of last dimension of indices}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([0, 1, 2]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_negative( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x1xindex>) { |
| // expected-error@+1 {{scatter_dims value must be non-negative}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([-1]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x1xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_overflow( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x1xindex>) { |
| // expected-error@+1 {{scatter_dims value must be smaller than dest rank}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([42]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x1xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_coordinate_increase( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{scatter_dims values must be strictly increasing}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([1, 0]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2x1x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_missing_unique( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{requires 'unique' attribute to be set}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([0, 2]): |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @scatter_wrong_result_type( |
| %source : tensor<f32>, |
| %dest : tensor<4x5x6xf32>, %indices: tensor<1x2x2xindex>) { |
| // expected-error@+1 {{source type mismatch: expected 'tensor<1x2x1x5x1xf32>' or its rank-reduced variant 'tensor<1x2x5xf32>' (got: 'tensor<f32>')}} |
| %out = tensor.scatter %source into %dest[%indices] scatter_dims([0, 2]) unique: |
| (tensor<f32>, tensor<4x5x6xf32>, tensor<1x2x2xindex>) -> tensor<1x2x1xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @empty_wrong_number_of_operands(%sz : index) { |
| // expected-error@+1 {{incorrect number of dynamic sizes, has 1, expected 2}} |
| %out = tensor.empty(%sz) : tensor<2x?x?x5xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @bitcast_index_0(%arg0 : tensor<?xi64>) -> tensor<?xindex> { |
| // expected-error @+1 {{'tensor.bitcast' op result #0 must be tensor of signless integer or unsigned integer or signed integer or floating-point values, but got 'tensor<?xindex>'}} |
| %0 = tensor.bitcast %arg0 : tensor<?xi64> to tensor<?xindex> |
| return %0 : tensor<?xindex> |
| } |
| |
| // ----- |
| |
| func.func @bitcast_index_1(%arg0 : tensor<?xindex>) -> tensor<?xi64> { |
| // expected-error @+1 {{'tensor.bitcast' op operand #0 must be tensor of signless integer or unsigned integer or signed integer or floating-point values, but got 'tensor<?xindex>'}} |
| %0 = tensor.bitcast %arg0 : tensor<?xindex> to tensor<?xi64> |
| return %0 : tensor<?xi64> |
| } |
| |
| // ----- |
| |
| func.func @test_empty_reassociation(%arg0: tensor<1x?xf32>) -> tensor<?x10xf32> { |
| // expected-error@below {{'tensor.collapse_shape' op reassociation indices must not be empty}} |
| %0 = tensor.collapse_shape %arg0 [[0, 1], []] : tensor<1x?xf32> into tensor<?x10xf32> |
| return %0 : tensor<?x10xf32> |
| } |
| |
