| // RUN: mlir-opt %s -split-input-file -verify-diagnostics |
| |
| func.func @load_number_of_indices(%v : memref<f32>) { |
| // expected-error @+2 {{incorrect number of indices for load}} |
| %c0 = arith.constant 0 : index |
| memref.load %v[%c0] : memref<f32> |
| } |
| |
| // ----- |
| |
| func.func @store_number_of_indices(%v : memref<f32>) { |
| // expected-error @+3 {{store index operand count not equal to memref rank}} |
| %c0 = arith.constant 0 : index |
| %f0 = arith.constant 0.0 : f32 |
| memref.store %f0, %v[%c0] : memref<f32> |
| } |
| |
| // ----- |
| |
| func.func @yield_parent(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| // expected-error @+1 {{op expected parent op with LinalgOp interface}} |
| linalg.yield %arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>> |
| } |
| |
| // ----- |
| |
| func.func @index_parent() { |
| // expected-error @+1 {{op expected parent op with LinalgOp interface}} |
| linalg.index 0 : index |
| } |
| |
| // ----- |
| |
| func.func @index_dim_lower_than_number_of_loops(%arg0: memref<f32>) { |
| // expected-error @+6 {{op expected dim (2) to be lower than the number of loops (0) of the enclosing LinalgOp}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0 : memref<f32>) { |
| ^bb(%0: f32): |
| linalg.index 2 : index |
| linalg.yield %0 : f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @index_dim_negative(%arg0: memref<f32>) { |
| // expected-error @+6 {{op attribute 'dim' failed to satisfy constraint: 64-bit signless integer attribute whose minimum value is 0}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0 : memref<f32>) { |
| ^bb(%0: f32): |
| linalg.index -1 : index |
| linalg.yield %0 : f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_no_region(%arg0: memref<f32>) { |
| // expected-error @+4 {{expected '{' to begin a region}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> (0)> ], |
| iterator_types = [] |
| } ins(%arg0 : memref<f32>) |
| } |
| |
| // ----- |
| |
| func.func @generic_mismatched_num_returns(%arg0: memref<f32>) { |
| // expected-error @+6 {{op expected number of yield values (0) to match the number of inits / outs operands of the enclosing LinalgOp (1)}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0 : memref<f32>) { |
| ^bb(%0: f32): |
| linalg.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_wrong_dim_in_map(%arg0: memref<1xi32>) { |
| // expected-error @+1 {{op expected indexing_map #0 to have 1 dim(s) to match the number of loops}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> (0)> ], |
| iterator_types = ["parallel"]} |
| outs(%arg0 : memref<1xi32>) { |
| ^bb(%i : i32): |
| linalg.yield %i : i32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_wrong_iterator(%arg0: memref<1xi32>) { |
| // expected-error @+4 {{unexpected iterator_type (random)}} |
| linalg.generic { |
| indexing_maps = [ affine_map<(i) -> (i)> ], |
| iterator_types = ["random"]} |
| outs(%arg0 : memref<1xi32>) { |
| ^bb(%i : i32): |
| linalg.yield %i : i32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_one_d_view(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| // expected-error @+1 {{expected operand rank (1) to match the result rank of indexing_map #0 (2)}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> (0, 0)> ], |
| iterator_types = []} |
| outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| ^bb(%f : f32): |
| linalg.yield %f: f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_scalar_view(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| %cst = arith.constant 0.0 : f32 |
| // expected-error @+1 {{expected operand rank (0) to match the result rank of indexing_map #0 (1)}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> (0)>, affine_map<() -> (0, 0)> ], |
| iterator_types = []} |
| ins(%cst : f32) |
| outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| ^bb(%0 : f32, %1 : f32): |
| linalg.yield %0: f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_result_0_element_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| // expected-error @+7 {{'linalg.yield' op type of yield operand 1 ('i4') doesn't match the element type of the enclosing linalg.generic op ('f32')}} |
| linalg.generic { |
| indexing_maps = [ affine_map<(i) -> (i)> ], |
| iterator_types = ["parallel"]} |
| outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| ^bb(%0: f32): |
| %1 = arith.constant 1: i4 |
| linalg.yield %1: i4 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_singular_maps(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>, %arg1: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| // expected-error @+1 {{expected the shape-to-loops map to be non-null}} |
| linalg.generic { |
| indexing_maps = [ |
| affine_map<(i, j) -> (i + j)>, |
| affine_map<(i, j) -> (i + j)> |
| ], |
| iterator_types = ["parallel","parallel"]} |
| ins(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) |
| outs(%arg1 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| ^bb(%0: f32, %1: f32): |
| linalg.yield %1: f32 |
| } |
| } |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| ///////////////////////////// Region tests ///////////////////////////////////// |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| // ----- |
| |
| func.func @generic_empty_region(%arg0: memref<f32>) { |
| %f0 = arith.constant 0.0: f32 |
| // expected-error @+1 {{op expects region #0 to have 0 or 1 blocks}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ], |
| iterator_types = []} |
| ins(%arg0 : memref<f32>) |
| outs(%arg0 : memref<f32>) { |
| ^bb1: |
| linalg.yield %f0: f32 |
| ^bb2: |
| linalg.yield %f0: f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_empty_region(%arg0: memref<f32>) { |
| %f0 = arith.constant 0.0: f32 |
| // expected-error @+1 {{op expects to have 1 region with 1 block}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> , affine_map<() -> ()> ], |
| iterator_types = []} |
| ins(%arg0 : memref<f32>) |
| outs(%arg0 : memref<f32>) { |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_mismatched_num_arguments(%arg0: memref<f32>) { |
| // expected-error @+6 {{'linalg.yield' op expected number of yield values (1) to match the number of inits / outs operands of the enclosing LinalgOp (2)}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0, %arg0 : memref<f32>, memref<f32>) { |
| ^bb(%f: f32): |
| linalg.yield %f: f32 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_shaped_operand_block_arg_type(%arg0: memref<f32>) { |
| // expected-error @+6 {{'linalg.yield' op type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0 : memref<f32>) { |
| ^bb(%i: i1): |
| linalg.yield %i : i1 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_scalar_operand_block_arg_type(%arg0: tensor<f32>) { |
| // expected-error @+6 {{'linalg.yield' op type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}} |
| linalg.generic { |
| indexing_maps = [ affine_map<() -> ()> ], |
| iterator_types = []} |
| outs(%arg0 : tensor<f32>) { |
| ^bb(%i: i1): |
| linalg.yield %i : i1 |
| } -> tensor<f32> |
| } |
| |
| // ----- |
| |
| func.func @generic_result_0_element_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| // expected-error @+7 {{type of yield operand 1 ('i1') doesn't match the element type of the enclosing linalg.generic op ('f32')}} |
| linalg.generic { |
| indexing_maps = [ affine_map<(i) -> (i)> ], |
| iterator_types = ["parallel"]} |
| outs(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) { |
| ^bb(%i: f32): |
| %0 = arith.constant 0: i1 |
| linalg.yield %0: i1 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_result_tensor_type(%arg0: memref<?xf32, affine_map<(i)[off]->(off + i)>>, |
| %arg1: tensor<?xf32>) { |
| // expected-error @+1 {{expected type of operand #1 ('tensor<?xf32>') to match type of corresponding result ('tensor<f32>')}} |
| %0 = linalg.generic { |
| indexing_maps = [ affine_map<(i) -> (i)> , affine_map<(i) -> (i)> ], |
| iterator_types = ["parallel"]} |
| ins(%arg0 : memref<?xf32, affine_map<(i)[off]->(off + i)>>) |
| outs(%arg1 : tensor<?xf32>) { |
| ^bb(%i: f32, %j: f32): |
| linalg.yield %i: f32 |
| } -> tensor<f32> |
| } |
| |
| // ----- |
| |
| func.func @generic(%arg0: memref<?x?xf32>) { |
| // expected-error @+6 {{block with no terminator, has %0 = "arith.addf"(%arg1, %arg1) <{fastmath = #arith.fastmath<none>}> : (f32, f32) -> f32}} |
| linalg.generic { |
| indexing_maps = [ affine_map<(i, j) -> (i, j)> ], |
| iterator_types = ["parallel", "parallel"]} |
| outs(%arg0 : memref<?x?xf32>) { |
| ^bb(%0: f32) : |
| %1 = arith.addf %0, %0: f32 |
| } |
| return |
| } |
| |
| // ----- |
| |
| // This test is currently disabled: subject to verifier ordering issues. |
| // Instead, when the ranks are not greater than 2, an assertion will be triggered |
| // in LinalgStructuredOps.td::ConvOp::iterator_types() for now because the |
| // verifier inspects the iterator_types. This is slated to become an |
| // autogenerated op in the future, alleviating the issue. |
| // func @conv_rank_limit(%arg0: memref<?xf32>, %arg1: memref<?xf32>, %arg2: memref<?xf32>) { |
| // // DISABLED_expected -error @+1 {{expects memref ranks to be greater than 2}} |
| // linalg.conv(%arg0, %arg1, %arg2) : memref<?xf32>, memref<?xf32>, memref<?xf32> |
| // } |
| // |
| // // ----- |
| |
| func.func @named_ops(%a3: memref<?x?x?xf32>, %b3: memref<?x?xf32>, %c3: memref<?x?x?xf32>) { |
| // expected-error @+1 {{expected operand rank (2) to match the result rank of indexing_map #1 (3)}} |
| linalg.batch_matmul ins(%a3, %b3: memref<?x?x?xf32>, memref<?x?xf32>) |
| outs(%c3 : memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @incorrect_region_arg_count(%m: memref<?x?xf32>) { |
| // expected-error @+3 {{region expects 3 args, got 2}} |
| %res = linalg.matmul ins(%m, %m : memref<?x?xf32>, memref<?x?xf32>) |
| -> (tensor<?x?xf32>, tensor<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @matching_inits(%m: memref<?x?xf32>, %t: tensor<?x?xf32>) { |
| // expected-error @+1 {{expected type of operand #2 ('tensor<?x?xf32>') to match type of corresponding result ('tensor<?xf32>')}} |
| %res = linalg.matmul ins(%m, %m : memref<?x?xf32>, memref<?x?xf32>) |
| outs(%t : tensor<?x?xf32>) |
| -> tensor<?xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @illegal_fill_tensor_no_return(%arg0 : index, %arg1 : index, %arg2 : f32) |
| { |
| %0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf32> |
| // expected-error @+1 {{expected the number of tensor results (0) to be equal to the number of output tensors (1)}} |
| linalg.fill ins(%arg2 : f32) outs(%0 : tensor<?x?xf32>) |
| } |
| |
| // ----- |
| |
| func.func @illegal_fill_memref_with_tensor_return |
| (%arg0 : memref<?x?xf32>, %arg1 : f32) -> tensor<?x?xf32> |
| { |
| // expected-error @+1 {{expected the number of tensor results (1) to be equal to the number of output tensors (0)}} |
| %0 = linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?x?xf32>) -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_fill_tensor_with_memref_return |
| (%arg0 : tensor<?x?xf32>, %arg1 : f32) -> memref<?x?xf32> |
| { |
| // expected-error @+1 {{result #0 must be variadic of ranked tensor of any type values, but got 'memref<?x?xf32>'}} |
| %0 = linalg.fill ins(%arg1 : f32) outs(%arg0 : tensor<?x?xf32>) -> memref<?x?xf32> |
| return %0 : memref<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @invalid_static_matmul(%arg0: memref<2x4xf32>, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) { |
| // expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 4, but found 3}} |
| linalg.matmul ins(%arg0, %arg1 : memref<2x4xf32>, memref<3x4xf32>) |
| outs(%arg2 :memref<2x4xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_static_2d_conv(%input : memref<1x3x4x2xf32>, %filter: memref<3x2x2x1xf32>, %output: memref<1x2x3x1xf32>) { |
| // expected-error @+1 {{inferred input/output operand #0 has shape's dimension #1 to be greater than or equal to 4, but found 3}} |
| linalg.conv_2d_nhwc_hwcf |
| { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %filter : memref<1x3x4x2xf32>, memref<3x2x2x1xf32>) |
| outs(%output : memref<1x2x3x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| #attrs = { |
| indexing_maps = [ |
| affine_map<(i) -> (3 - i)>, |
| affine_map<(i) -> (i)> |
| ], |
| iterator_types = ["parallel"] |
| } |
| |
| func.func @invalid_reverse(%A: memref<5xf32>, %B: memref<5xf32>) { |
| // expected-error @+1 {{unexpected result less than 0 at expression #0 in}} |
| linalg.generic #attrs ins(%A: memref<5xf32>) outs(%B: memref<5xf32>) { |
| ^bb0(%a: f32, %b: f32): |
| linalg.yield %a : f32 |
| } |
| return |
| } |
| |
| // ----- |
| |
| func.func @map_binary_wrong_yield_operands( |
| %lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>) |
| -> tensor<64xf32> { |
| %add = linalg.map |
| ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) |
| outs(%init:tensor<64xf32>) |
| (%lhs_elem: f32, %rhs_elem: f32) { |
| %0 = arith.addf %lhs_elem, %rhs_elem: f32 |
| // expected-error @+1{{'linalg.yield' op expected number of yield values (2) to match the number of inits / outs operands of the enclosing LinalgOp (1)}} |
| linalg.yield %0, %0: f32, f32 |
| } |
| func.return %add : tensor<64xf32> |
| } |
| |
| // ----- |
| |
| func.func @map_input_mapper_arity_mismatch( |
| %lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>) |
| -> tensor<64xf32> { |
| // expected-error@+1{{'linalg.map' op expects number of operands to match the arity of mapper, but got: 2 and 3}} |
| %add = linalg.map |
| ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) |
| outs(%init:tensor<64xf32>) |
| (%lhs_elem: f32, %rhs_elem: f32, %extra_elem: f32) { |
| %0 = arith.addf %lhs_elem, %rhs_elem: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %add : tensor<64xf32> |
| } |
| |
| // ----- |
| |
| func.func @map_input_mapper_type_mismatch( |
| %lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>) |
| -> tensor<64xf32> { |
| // expected-error@+1{{'linalg.map' op expected element type of input 'f32' to match bbArg type 'f64'}} |
| %add = linalg.map |
| ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) |
| outs(%init:tensor<64xf32>) |
| (%lhs_elem: f64, %rhs_elem: f64) { |
| %0 = arith.addf %lhs_elem, %rhs_elem: f64 |
| linalg.yield %0: f64 |
| } |
| func.return %add : tensor<64xf32> |
| } |
| |
| // ----- |
| |
| func.func @map_input_output_shape_mismatch( |
| %lhs: tensor<64x64xf32>, %rhs: tensor<64x64xf32>, %init: tensor<32xf32>) |
| -> tensor<32xf32> { |
| // expected-error@+1{{'linalg.map' op expected shape of input (64, 64) to match shape of output (32)}} |
| %add = linalg.map |
| ins(%lhs, %rhs : tensor<64x64xf32>, tensor<64x64xf32>) |
| outs(%init:tensor<32xf32>) |
| (%lhs_elem: f32, %rhs_elem: f32) { |
| %0 = arith.addf %lhs_elem, %rhs_elem: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %add : tensor<32xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_input_vs_init_dimension_mismatch( |
| %input: tensor<16x32x64xf32>, |
| %init: tensor<16x64xf32>) -> tensor<16x64xf32> { |
| // expected-error @+1 {{'linalg.reduce' op init dimensions [16, 64] doesn't match input dimensions after reduction [16, 32]}} |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16x64xf32>) |
| dimensions = [2] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_dimensions_out_of_range(%input: tensor<16x32x64xf32>, |
| %init: tensor<16x64xf32>) -> tensor<16x64xf32> { |
| // expected-error @+1 {{'linalg.reduce' op dimensions for reduction should be in the range [0, 2].}} |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16x64xf32>) |
| dimensions = [3] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_duplicate_dimensions(%input: tensor<16x32x64xf32>, |
| %init: tensor<16xf32>) -> tensor<16xf32> { |
| // expected-error @+1 {{'linalg.reduce' op attribute 'dimensions' failed to satisfy constraint: i64 dense array attribute should be in increasing order}} |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16xf32>) |
| dimensions = [1, 1] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_non_increasing_dimensions(%input: tensor<16x32x64xf32>, |
| %init: tensor<16xf32>) -> tensor<16xf32> { |
| // expected-error @+1 {{'linalg.reduce' op attribute 'dimensions' failed to satisfy constraint: i64 dense array attribute should be in increasing order}} |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16xf32>) |
| dimensions = [2, 1] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_reduced_input_init_rank_mismatch(%input: tensor<16x32x64xf32>, |
| %init: tensor<16x64xf32>) -> tensor<16x64xf32> { |
| // expected-error @+1 {{'linalg.reduce' op number of dimensions after reduction 1 doesn't match the init rank 2}} |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16x64xf32>) |
| dimensions = [1, 2] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_wrong_number_of_block_arguments( |
| %input1: tensor<16x32x64xf32>, |
| %init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>, |
| %init2: tensor<16x64xf32>) -> (tensor<16x64xf32>, tensor<16x64xf32>) { |
| // expected-error @+1{{'linalg.reduce' op mismatching number of operands and block arguments}} |
| %reduce, %reduce2 = linalg.reduce |
| ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>) |
| outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf32>) |
| dimensions = [1] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %in, %out: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_wrong_block_argument_input_type( |
| %input1: tensor<16x32x64xf32>, |
| %init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>, |
| %init2: tensor<16x64xf32>) -> (tensor<16x64xf32>, tensor<16x64xf32>) { |
| // expected-error @+1{{'linalg.reduce' op input element type 'f32' does not match corresponding block argument type 'f64'}} |
| %reduce, %reduce2 = linalg.reduce |
| ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>) |
| outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf32>) |
| dimensions = [1] |
| (%in1: f32, %in2: f64, %out1: f32, %out2: f64) { |
| %0 = arith.addf %in1, %out1: f32 |
| %1 = arith.addf %in2, %out2: f64 |
| linalg.yield %0, %1: f32, f64 |
| } |
| func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_wrong_block_argument_output_type( |
| %input1: tensor<16x32x64xf32>, |
| %init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>, |
| %init2: tensor<16x64xf64>) -> (tensor<16x64xf32>, tensor<16x64xf32>) { |
| // expected-error @+1{{'linalg.reduce' op output element type 'f64' does not match corresponding block argument type 'f32'}} |
| %reduce, %reduce2 = linalg.reduce |
| ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>) |
| outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xf64>) |
| dimensions = [1] |
| (%in1: f32, %in2: f32, %out1: f32, %out2: f32) { |
| %0 = arith.addf %in1, %out1: f32 |
| linalg.yield %0, %out2: f32, f32 |
| } |
| func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xf64> |
| } |
| |
| // ----- |
| |
| func.func @reduce_different_input_shapes(%input1: tensor<16x32x64xf32>, |
| %init1: tensor<16x64xf32>, %input2: tensor<17x32x64xf32>, |
| %init2: tensor<17x64xf32>) -> (tensor<16x64xf32>, tensor<17x64xf32>) { |
| // expected-error @+1{{'linalg.reduce' op expects all inputs to have the same shapes. Shape at input-index 1 is not equal to the shape at input-index 0.}} |
| %reduce, %reduce2 = linalg.reduce |
| ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<17x32x64xf32>) |
| outs(%init1, %init2 : tensor<16x64xf32>, tensor<17x64xf32>) |
| dimensions = [1] |
| (%in1: f32, %in2: f32, %out1: f32, %out2: f32) { |
| %0 = arith.addf %in1, %out1: f32 |
| %1 = arith.addf %in2, %out2: f32 |
| linalg.yield %0, %1: f32, f32 |
| } |
| func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<17x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @reduce_different_output_shapes(%input1: tensor<16x32x64xf32>, |
| %init1: tensor<16x64xf32>, %input2: tensor<16x32x64xf32>, |
| %init2: tensor<17x64xf32>) -> (tensor<16x64xf32>, tensor<17x64xf32>) { |
| // expected-error @+1{{'linalg.reduce' op expects all outputs to have the same shapes. Shape at output-index 1 is not equal to the shape at output-index 0.}} |
| %reduce, %reduce2 = linalg.reduce |
| ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xf32>) |
| outs(%init1, %init2 : tensor<16x64xf32>, tensor<17x64xf32>) |
| dimensions = [1] |
| (%in1: f32, %in2: f32, %out1: f32, %out2: f32) { |
| %0 = arith.addf %in1, %out1: f32 |
| %1 = arith.addf %in2, %out2: f32 |
| linalg.yield %0, %1: f32, f32 |
| } |
| func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<17x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @transpose_invalid_permutation(%input: tensor<16x32x64xf32>, |
| %init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> { |
| // expected-error @+1 {{'linalg.transpose' op permutation is not valid}} |
| %transpose = linalg.transpose |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<32x64x16xf32>) |
| permutation = [1, 1, 2] |
| func.return %transpose : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @transpose_permutated_dims_mismatch(%input: tensor<16x32x64xf32>, |
| %init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> { |
| // expected-error @+1 {{'linalg.transpose' op dim(result, 0) = 32 doesn't match dim(input, permutation[0]) = 16}} |
| %transpose = linalg.transpose |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<32x64x16xf32>) |
| permutation = [0, 1, 2] |
| func.return %transpose : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @transpose_rank_permutation_size_mismatch( |
| %input: tensor<16x32x64xf32>, |
| %init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> { |
| // expected-error @+1 {{'linalg.transpose' op size of permutation 2 does not match the argument rank 3}} |
| %transpose = linalg.transpose |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<32x64x16xf32>) |
| permutation = [1, 0] |
| func.return %transpose : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @transpose_input_init_rank_mismatch(%input: tensor<16x32xf32>, |
| %init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> { |
| // expected-error @+1 {{'linalg.transpose' op input rank 2 does not match init rank 3}} |
| %transpose = linalg.transpose |
| ins(%input:tensor<16x32xf32>) |
| outs(%init:tensor<32x64x16xf32>) |
| permutation = [1, 0, 2] |
| func.return %transpose : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @broadcast_input_dims_rank_mismatch( |
| %input: tensor<4x16xf32>, %init: tensor<4x8x16xf32>) |
| -> tensor<4x8x16xf32> { |
| // expected-error @+1 {{'linalg.broadcast' op input rank plus added dimensions does not match init rank. }} |
| %bcast = linalg.broadcast |
| ins(%input:tensor<4x16xf32>) |
| outs(%init:tensor<4x8x16xf32>) |
| dimensions = [1, 2] |
| func.return %bcast : tensor<4x8x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @broadcast_unsorted_dims( |
| %input: tensor<4x16xf32>, %init: tensor<4x8x16xf32>) |
| -> tensor<4x8x16xf32> { |
| // expected-error @+1 {{'linalg.broadcast' op dimension 0 is out of range. expected range: [0, 2], got: 5}} |
| %bcast = linalg.broadcast |
| ins(%input:tensor<4x16xf32>) |
| outs(%init:tensor<4x8x16xf32>) |
| dimensions = [5] |
| func.return %bcast : tensor<4x8x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @broadcast_mapped_dim_mismatch( |
| %input: tensor<4x16xf32>, %init: tensor<5x8x16xf32>) |
| -> tensor<5x8x16xf32> { |
| // expected-error @+1 {{'linalg.broadcast' op input dim 0 should match init dim 0. input: 4, init: 5}} |
| %bcast = linalg.broadcast |
| ins(%input:tensor<4x16xf32>) |
| outs(%init:tensor<5x8x16xf32>) |
| dimensions = [1] |
| func.return %bcast : tensor<5x8x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @broadcast_size_1_extension_not_supported( |
| %input: tensor<1x16xf32>, %init: tensor<4x?x16xf32>) |
| -> tensor<4x?x16xf32> { |
| // expected-error @+1 {{'linalg.broadcast' op input dim 0 should match init dim 0. input: 1, init: 4}} |
| %bcast = linalg.broadcast |
| ins(%input:tensor<1x16xf32>) |
| outs(%init:tensor<4x?x16xf32>) |
| dimensions = [1] |
| func.return %bcast : tensor<4x?x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @missing_iterator_types() { |
| // expected-error @below {{expected "iterator_types" array attribute}} |
| linalg.generic {} ins() outs() |
| return |
| } |
| |
| // ----- |
| |
| func.func @illegal_softmax_output_shape(%arg0: tensor<2x16x32xf32>) -> tensor<2x16xf32> { |
| %0 = tensor.empty() : tensor<2x16xf32> |
| // expected-error @+1 {{incompatible output shape}} |
| %1 = linalg.softmax dimension(2) ins(%arg0 : tensor<2x16x32xf32>) |
| outs(%0: tensor<2x16xf32>) |
| -> tensor<2x16xf32> |
| return %1 : tensor<2x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @mmt4d_dims_mismatch(%A: tensor<16x16x8x1xf32>, |
| %B: tensor<16x16x8x1xf32>, |
| %C_in: tensor<16x16x8x1xf32>) -> tensor<16x16x8x1xf32> { |
| // expected-error @+1 {{inferred input/output operand #2 has shape's dimension #3 to be 8, but found 1}} |
| %res = linalg.mmt4d |
| ins(%A, %B: tensor<16x16x8x1xf32>, tensor<16x16x8x1xf32>) |
| outs(%C_in: tensor<16x16x8x1xf32>) |
| -> tensor<16x16x8x1xf32> |
| return %res : tensor<16x16x8x1xf32> |
| } |
| |
| // ----- |
| |
| func.func @mmt4d_rank_mismatch(%A: tensor<16x16x8x1xf32>, |
| %B: tensor<16x16x8x1xf32>, |
| %C_in: tensor<8x8xf32>) -> tensor<8x8xf32> { |
| // expected-error @+1 {{expected operand rank (2) to match the result rank of indexing_map #2 (4)}} |
| %res = linalg.mmt4d |
| ins(%A, %B: tensor<16x16x8x1xf32>, tensor<16x16x8x1xf32>) |
| outs(%C_in: tensor<8x8xf32>) |
| -> tensor<8x8xf32> |
| return %res : tensor<8x8xf32> |
| } |
| |
| // ----- |
| |
| func.func @mixed_semantics(%a: tensor<?x?xf32>, %b: tensor<?x?xf32>, %c: memref<?x?xf32>) { |
| // expected-error @+1 {{expected to have pure tensor or buffer semantics}} |
| linalg.matmul ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%c: memref<?x?xf32>) |
| return |
| } |
| |
