| // 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 #0 rank (1) to match the result rank of indexing_map (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 #0 rank (0) to match the result rank of indexing_map (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 {{invalid indexing maps are non-invertible: ((d0, d1) -> (d0 + d1, d0 + d1))}} |
| 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 |
| } |
| } |
| |
| // ----- |
| |
| func.func @generic_index_rank0(%arg0: tensor<f32>) -> tensor<f32> { |
| // expected-error @+1 {{expected operand #0 rank (0) to match the result rank of indexing_map (1)}} |
| %0 = linalg.generic { |
| indexing_maps = [ |
| affine_map<(d0) -> (d0)>, |
| affine_map<(d0) -> (d0)> |
| ], |
| iterator_types = ["parallel"]} |
| ins(%arg0 : tensor<f32>) |
| outs(%arg0 : tensor<f32>) { |
| ^bb(%0: f32, %1: f32): |
| linalg.yield %1 : f32 |
| } -> tensor<f32> |
| return %0 : tensor<f32> |
| } |
| |
| // ----- |
| |
| func.func @generic_index_domain_error(%arg0: tensor<4xf32>) -> tensor<4xf32> { |
| // expected-error @+1 {{expected operand #1 rank (1) to match the result rank of indexing_map (2)}} |
| %0 = linalg.generic { |
| indexing_maps = [ |
| affine_map<(d0) -> (d0)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"]} |
| ins(%arg0 : tensor<4xf32>) |
| outs(%arg0 : tensor<4xf32>) { |
| ^bb(%0: f32): |
| linalg.yield %0 : f32 |
| } -> tensor<4xf32> |
| return %0 : tensor<4xf32> |
| } |
| |
| // ----- |
| |
| #map_with_symbol = affine_map<(d0)[s0] -> (d0 + s0)> |
| |
| func.func @generic_indexing_map_with_symbol(%arg0: tensor<8xf32>) -> tensor<8xf32> { |
| // expected-error @+1 {{unexpected symbols in indexing_map #0}} |
| %0 = linalg.generic { |
| indexing_maps = [#map_with_symbol, #map_with_symbol], |
| iterator_types = ["parallel"] |
| } ins(%arg0 : tensor<8xf32>) |
| outs(%arg0 : tensor<8xf32>) { |
| ^bb0(%in: f32, %out: f32): |
| linalg.yield %in : f32 |
| } -> tensor<8xf32> |
| return %0 : tensor<8xf32> |
| } |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| ///////////////////////////// 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 #1 rank (2) to match the result rank of indexing_map (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 @illegal_fill_element_type_truncation(%arg0 : tensor<2xf32>, %arg1 : f64) -> tensor<2xf32> |
| { |
| // expected-error @+1 {{'linalg.fill' op expected fill value type ('f64') to match output element type ('f32')}} |
| %0 = linalg.fill ins(%arg1 : f64) outs(%arg0 : tensor<2xf32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_fill_element_type_extension(%arg0 : tensor<2xi32>, %arg1 : i16) -> tensor<2xi32> |
| { |
| // expected-error @+1 {{'linalg.fill' op expected fill value type ('i16') to match output element type ('i32')}} |
| %0 = linalg.fill ins(%arg1 : i16) outs(%arg0 : tensor<2xi32>) -> tensor<2xi32> |
| return %0 : tensor<2xi32> |
| } |
| |
| // ----- |
| |
| func.func @illegal_fill_value_type(%arg0 : tensor<2x2xf32>, %arg1 : tensor<2xf32>) -> tensor<2x2xf32> |
| { |
| // expected-error @+1 {{expected op with scalar input}} |
| %0 = linalg.fill ins(%arg1 : tensor<2xf32>) outs(%arg0 : tensor<2x2xf32>) -> tensor<2x2xf32> |
| return %0 : tensor<2x2xf32> |
| } |
| |
| // ----- |
| |
| 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_scalar_input_matmul(%arg0: f32, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op expected operand #0 rank (0) to match the result rank of indexing_map (2)}} |
| linalg.matmul ins(%arg0, %arg1 : f32, memref<3x4xf32>) |
| outs(%arg2 : memref<2x4xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_scalar_output_matmul(%arg0: memref<2x3xf32>, %arg1: memref<3x4xf32>, %arg2: f32) { |
| // expected-error @+1 {{'linalg.matmul' op operand #2 must be variadic of shaped of any type values, but got 'f32'}} |
| linalg.matmul ins(%arg0, %arg1 : memref<2x3xf32>, memref<3x4xf32>) |
| outs(%arg2 : f32) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_indexing_maps_matmul(%arg0: memref<2x4xf32>, %arg1: memref<3x4xf32>, %arg2: memref<2x4xf32>) { |
| // expected-error @+1 {{expected attribute value}} |
| linalg.matmul indexing_maps = [ |
| , |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<2x4xf32>, memref<3x4xf32>) |
| outs(%arg2 :memref<2x4xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_matmul_dim_a(%arg0: memref<5x5xf32>, %arg1: memref<5x5xf32>, %arg2: memref<5x5xf32>) { |
| // expected-error @+1 {{Unexpected dim expression in map result}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d1, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<5x5xf32>, memref<5x5xf32>) outs(%arg2: memref<5x5xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_matmul_dim_b(%arg0: memref<5x5xf32>, %arg1: memref<5x5xf32>, %arg2: memref<5x5xf32>) { |
| // expected-error @+1 {{Unexpected dim expression in map result}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d0)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<5x5xf32>, memref<5x5xf32>) outs(%arg2: memref<5x5xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_transpose_a_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) -> tensor<4x64xf32> { |
| // expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 4, but found 1}} |
| %0 = linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d2, d0)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return %0: tensor<4x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @invalid_transpose_b_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) -> tensor<4x64xf32> { |
| // expected-error @+1 {{inferred input/output operand #1 has shape's dimension #1 to be 1, but found 64}} |
| %0 = linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d1, d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return %0: tensor<4x64xf32> |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_a(%arg0: memref<3xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op Invalid broadcast requested, should be (d2)}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0)>, |
| affine_map<(d0, d1, d2) -> (d1, d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<3xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_b(%arg0: memref<3x5xf32>, %arg1: memref<7xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op Invalid broadcast requested, should be (d2)}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<3x5xf32>, memref<7xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_a_rank_mismatch(%arg0: memref<3x5xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op expected operand #0 rank (2) to match the result rank of indexing_map (1)}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<3x5xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_b_rank_mismatch(%arg0: memref<3x5xf32>, %arg1: memref<5x7xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op expected operand #1 rank (2) to match the result rank of indexing_map (1)}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<3x5xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_matmul_bcast_b_transpose_a(%arg0: memref<5x3xf32>, %arg1: memref<7xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{inferred input/output operand #1 has shape's dimension #0 to be 5, but found 7}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d2, d0)>, |
| affine_map<(d0, d1, d2) -> (d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<5x3xf32>, memref<7xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_matmul_bcast_b_transpose_a_wrong_dim(%arg0: memref<3x5xf32>, %arg1: memref<5xf32>, %arg2: memref<3x7xf32>) { |
| // expected-error @+1 {{'linalg.matmul' op Unexpected dim expression in map result.}} |
| linalg.matmul indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d1, d2)>, |
| affine_map<(d0, d1, d2) -> (d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| ins(%arg0, %arg1 : memref<3x5xf32>, memref<5xf32>) outs(%arg2: memref<3x7xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_indexing_maps_placement_matmul(%lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+2 {{custom op 'indexing_maps' is unknown (tried 'func.indexing_maps' as well)}} |
| linalg.matmul ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) outs(%init : tensor<4x64xf32>) |
| indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)> |
| ] |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_indexing_maps_placement_contraction( |
| %lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+3 {{custom op 'linalg.contract' expected 'indexing_maps' attribute}} |
| // NB: indexing_maps should be provided before ins and outs |
| linalg.contract |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) |
| indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)>] |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_affine_map_in_indexing_maps_contraction( |
| %lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{provided affine_map is not a projected permutation}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1, d2) -> (d0 + d2, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)>] |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @differing_iteration_space_of_affine_maps_contraction( |
| %lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{iteration spaces of provided affine_maps differ}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)>] |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @mismatched_ranks_affine_map_and_operand_contraction( |
| %lhs: tensor<4x1x2xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{ranks of shaped operand and results of corresponding affine_map differ}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>, |
| affine_map<(d0, d1, d2) -> (d0, d1)>] |
| ins(%lhs, %rhs : tensor<4x1x2xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return |
| } |
| // ----- |
| |
| func.func @mismatch_type_affine_map_and_operand_contraction( |
| %lhs: f32, %rhs: tensor<4x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{affine_map specifies shaped access while operand has non-shaped type}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1) -> (d0)>, |
| affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>] |
| ins(%lhs, %rhs : f32, tensor<4x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @unused_iteration_space_dim_contraction( |
| %lhs: tensor<4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{iteration space dim at index 3 not used to access any operand}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d2, d1)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1)>] |
| ins(%lhs, %rhs : tensor<4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| return |
| } |
| |
| // ----- |
| |
| func.func @unused_iteration_space_dim_contraction( |
| %lhs: tensor<8x4x1xf32>, %rhs: tensor<1x64xf32>, %init: tensor<4x64xf32>) { |
| // expected-error @+1 {{iteration space dim at index 3 is neither a contracting dim nor of parallel iteration type}} |
| linalg.contract |
| indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d2, d1)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1)>] |
| ins(%lhs, %rhs : tensor<8x4x1xf32>, tensor<1x64xf32>) |
| outs(%init : tensor<4x64xf32>) -> tensor<4x64xf32> |
| 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, %out: 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: 3 and 4}} |
| %add = linalg.map |
| ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) |
| outs(%init:tensor<64xf32>) |
| (%lhs_elem: f32, %rhs_elem: f32, %out: 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, %out: f32) { |
| %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, %out: f32) { |
| %0 = arith.addf %lhs_elem, %rhs_elem: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %add : tensor<32xf32> |
| } |
| |
| // ----- |
| |
| func.func @map_no_operands1() { |
| // expected-error @+1 {{'linalg.map' op expected 1 or more operands, but found 0}} |
| linalg.map { arith.addf } |
| } |
| |
| // ----- |
| |
| func.func @map_no_operands2() { |
| // expected-error @+1 {{'linalg.map' op expected 1 or more operands, but found 0}} |
| "linalg.map"() ({ |
| ^bb0: |
| }) : () -> () |
| } |
| |
| // ----- |
| |
| func.func @map_no_operands3( |
| %lhs: tensor<64xf32>, %rhs: tensor<64xf32>, %init: tensor<64xf32>) |
| -> tensor<64xf32> { |
| // expected-error @+1 {{cannot name an operation with no results}} |
| %add = linalg.map { arith.addf } |
| func.return %add : tensor<64xf32> |
| } |
| |
| // ----- |
| |
| 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_out_of_range_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, 2, 3] |
| func.return %transpose : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @transpose_negative_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, 2, -1] |
| 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 @transpose_no_operands1() { |
| // expected-error @+1 {{'linalg.transpose' op expected 2 operands, but found 0}} |
| linalg.transpose permutation = [1, 0, 2] |
| } |
| |
| // ----- |
| |
| func.func @transpose_no_operands2() { |
| // expected-error @+1 {{'linalg.transpose' op expected 2 operands, but found 0}} |
| "linalg.transpose"() <{permutation = array<i64: 1, 0, 2>}> ({ |
| ^bb0: |
| }) : () -> () |
| } |
| |
| // ----- |
| |
| func.func @transpose_no_operands3() -> tensor<32x64x16xf32> { |
| // expected-error @+1 {{cannot name an operation with no results}} |
| %transpose = linalg.transpose 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 @broadcast_no_operands1() { |
| // expected-error @+1 {{'linalg.broadcast' op expected 2 operands, but found 0}} |
| linalg.broadcast dimensions = [1] |
| } |
| |
| // ----- |
| |
| func.func @broadcast_no_operands2() { |
| // expected-error @+1 {{'linalg.broadcast' op expected 2 operands, but found 0}} |
| "linalg.broadcast"() <{dimensions = array<i64: 1>}> ({ |
| ^bb0: |
| }) : () -> () |
| } |
| |
| // ----- |
| |
| func.func @broadcast_no_operands3() |
| -> tensor<4x?x16xf32> { |
| // expected-error @+1 {{cannot name an operation with no results}} |
| %broadcast = linalg.broadcast dimensions = [1] |
| func.return %broadcast : tensor<32x64x16xf32> |
| } |
| |
| // ----- |
| |
| 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 #2 rank (2) to match the result rank of indexing_map (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 |
| } |
| |
| // ----- |
| |
| func.func @winograd_filter_transform_height(%arg0: tensor<2x4x3x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> { |
| // expected-error @+1 {{expect filter height either equals to r or 1}} |
| %0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x4x3x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> |
| return %0 : tensor<6x6x5x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_filter_transform_width(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> { |
| // expected-error @+1 {{expect filter width either equals to r or 1}} |
| %0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x3x4x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> |
| return %0 : tensor<6x6x5x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_filter_transform(%arg0: tensor<2x1x1x5xf32>, %arg1: tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> { |
| // expected-error @+1 {{expect either filter height or width equals to r}} |
| %0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<2x1x1x5xf32>) outs(%arg1 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32> |
| return %0 : tensor<6x6x5x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_filter_dyn(%arg0: tensor<?x3x3x?xf32>, %arg1: tensor<6x5x?x?xf32>) -> tensor<6x5x?x?xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg0 : tensor<?x3x3x?xf32>) outs(%arg1 : tensor<6x5x?x?xf32>) -> tensor<6x5x?x?xf32> |
| return %0 : tensor<6x5x?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_height(%arg0: tensor<2x13x14x5xf32>, %arg1: tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x13x14x5xf32>) outs(%arg1 : tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> |
| return %0 : tensor<6x6x3x3x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_width(%arg0: tensor<2x14x13x5xf32>, %arg1: tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x13x5xf32>) outs(%arg1 : tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32> |
| return %0 : tensor<6x6x3x3x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_output_tileH(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x6x2x3x2x5xf32>) -> tensor<6x6x2x3x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x6x2x3x2x5xf32>) -> tensor<6x6x2x3x2x5xf32> |
| return %0 : tensor<6x6x2x3x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_output_tileW(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x6x3x2x2x5xf32>) -> tensor<6x6x3x2x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x6x3x2x2x5xf32>) -> tensor<6x6x3x2x2x5xf32> |
| return %0 : tensor<6x6x3x2x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_output_height(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<5x6x3x3x2x5xf32>) -> tensor<5x6x3x3x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<5x6x3x3x2x5xf32>) -> tensor<5x6x3x3x2x5xf32> |
| return %0 : tensor<5x6x3x3x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_output_width(%arg0: tensor<2x14x14x5xf32>, %arg1: tensor<6x5x3x3x2x5xf32>) -> tensor<6x5x3x3x2x5xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x14x14x5xf32>) outs(%arg1 : tensor<6x5x3x3x2x5xf32>) -> tensor<6x5x3x3x2x5xf32> |
| return %0 : tensor<6x5x3x3x2x5xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_input_dyn(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<6x5x?x?x?x?xf32>) -> tensor<6x5x?x?x?x?xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<?x?x?x?xf32>) outs(%arg1 : tensor<6x5x?x?x?x?xf32>) -> tensor<6x5x?x?x?x?xf32> |
| return %0 : tensor<6x5x?x?x?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_input_height(%arg0: tensor<5x6x3x3x2x2xf32>, %arg1: tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> { |
| // expected-error @+1 {{expect input height equals to input tile size}} |
| %0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<5x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> |
| return %0 : tensor<2x12x12x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_input_width(%arg0: tensor<6x5x3x3x2x2xf32>, %arg1: tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> { |
| // expected-error @+1 {{expect input width equals to input tile size}} |
| %0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x5x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32> |
| return %0 : tensor<2x12x12x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_output_height(%arg0: tensor<6x6x3x3x2x2xf32>, %arg1: tensor<2x11x12x2xf32>) -> tensor<2x11x12x2xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x11x12x2xf32>) -> tensor<2x11x12x2xf32> |
| return %0 : tensor<2x11x12x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_output_width(%arg0: tensor<6x6x3x3x2x2xf32>, %arg1: tensor<2x12x11x2xf32>) -> tensor<2x12x11x2xf32> { |
| // expected-error @+1 {{the output shape is not expected}} |
| %0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<6x6x3x3x2x2xf32>) outs(%arg1 : tensor<2x12x11x2xf32>) -> tensor<2x12x11x2xf32> |
| return %0 : tensor<2x12x11x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @indexing_map_size_mismatch_batch_matmul(%arg0: memref<?x?x?xf32>, |
| %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{Indexing_map attribute must have 3 affine maps}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @indexing_map_size_one_batch_matmul(%arg0: memref<?x?x?xf32>, |
| %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{Indexing_map attribute must have 3 affine maps}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?x?xf32>) |
| return |
| |
| } |
| |
| // ----- |
| |
| func.func @missing_indexing_map_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{expected attribute value}} |
| linalg.batch_matmul indexing_maps = [ |
| , |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2 :memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_dim_expr_batch_matmul_a(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_dim_expr_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d1)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_batch_matmul_a(%arg0: memref<?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_single_dim_bcast_expr_batch_matmul_a(%arg0: memref<?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d3, d0)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_single_dim_bcast_expr_batch_matmul_B(%A: memref<?x?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d3, d0)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?xf32>) outs(%C: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid broadcast requested}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?xf32>) outs(%arg2: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_batch_dim_batch_matmul_a(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid batch dimension expression}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d1, d0, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_batch_dim_batch_matmul_b(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid batch dimension expression}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d2, d3, d0)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2 :memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_A_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op no. of result dim expressions exceeds 3.}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_B_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op no. of result dim expressions exceeds 3.}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| ] |
| ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_C_map_result_num_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op expects 3 dims, but got (2).}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d1, d2)> |
| ] |
| ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_C_map_result_dim_batch_matmul(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) { |
| // expected-error @+1 {{'linalg.batch_matmul' op Invalid output map result dimension.}} |
| linalg.batch_matmul indexing_maps = [ |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>, |
| affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)> |
| ] |
| ins(%arg0, %arg1: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?x?xf32>) |
| return |
| } |
| |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.batch_reduce_matmul |
| //===----------------------------------------------------------------------===// |
| |
| func.func @missing_one_indexing_map(%arg0: memref<?x?x?xf32>, |
| %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{Indexing_map attribute must have 3 affine maps}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (batch, n, k)>] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @missing_two_indexing_map(%arg0: memref<?x?x?xf32>, |
| %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{Indexing_map attribute must have 3 affine maps}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2: memref<?x?xf32>) |
| return |
| |
| } |
| |
| // ----- |
| |
| func.func @missing_indexing_map(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?xf32>) { |
| // expected-error @+1 {{expected attribute value}} |
| linalg.batch_reduce_matmul indexing_maps = [ |
| , |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%arg2 :memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_dim_expr_A(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, n, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C :memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_dim_expr_B(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Unexpected result dim expression (outside the set of default result dims)}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, m)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C :memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_A(%A: memref<?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid broadcast requested}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_multi_dim_bcast_expr_A(%A: memref<?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid broadcast requested}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (k, batch)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_multi_dim_bcast_expr_B(%A: memref<?x?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid broadcast requested}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (k, batch)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_bcast_B(%A: memref<?x?x?xf32>, %B: memref<?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid broadcast requested}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (n)>, |
| affine_map<(batch, m, n, k) -> (batch, m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_batch_dim_A(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid batch dimension expression}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (m, batch, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C :memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_batch_dim_B(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid batch dimension expression}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (n, k, batch)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C :memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_A_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{no. of result dim expressions exceeds 3.}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_B_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{no. of result dim expressions exceeds 3.}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n, k)>, |
| affine_map<(batch, m, n, k) -> (m, n)>] |
| ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_C_map_result_num(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{expects 2 dims, but got (1).}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m)>] |
| ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func.func @invalid_C_map_result_dim(%A: memref<?x?x?xf32>, %B: memref<?x?x?xf32>, %C: memref<?x?xf32>) { |
| // expected-error @+1 {{Invalid output map result dimension.}} |
| linalg.batch_reduce_matmul |
| indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, |
| affine_map<(batch, m, n, k) -> (batch, k, n)>, |
| affine_map<(batch, m, n, k) -> (m, k)>] |
| ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.pack |
| //===----------------------------------------------------------------------===// |
| |
| func.func @pack_invalid_no_padding_no_full_tiles(%input: tensor<256x128xf32>, %output: tensor<8x8x16x33xf32>) -> tensor<8x8x16x33xf32> { |
| // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 33] into %output : tensor<256x128xf32> -> tensor<8x8x16x33xf32> |
| return %0 : tensor<8x8x16x33xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles(%input: tensor<256x128xf32>, %output: tensor<10x8x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<10x8x?x?xf32> { |
| // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32> -> tensor<10x8x?x?xf32> |
| return %0 : tensor<10x8x?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles_outperm(%input: tensor<256x128xf32>, %output: tensor<8x10x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<8x10x?x?xf32> { |
| // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}} |
| %0 = linalg.pack %input outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32> -> tensor<8x10x?x?xf32> |
| return %0 : tensor<8x10x?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @pad_and_pack_invalid_type(%input: tensor<13x15xf32>, %output: tensor<2x8x8x2xf32>, %pad: i32) -> tensor<2x8x8x2xf32> { |
| // expected-error@+1 {{expected padding_value has 'f32' but got: 'i32'}} |
| %0 = linalg.pack %input padding_value(%pad: i32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<13x15xf32> -> tensor<2x8x8x2xf32> |
| return %0 : tensor<2x8x8x2xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_inner_dims_pos_vector(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> { |
| // expected-error@+1 {{invalid inner_dims_pos vector}} |
| %0 = linalg.pack %input inner_dims_pos = [2, 0] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32> |
| return %0 : tensor<8x8x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_duplicate_element_in_inner_dims(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> { |
| // expected-error@+1 {{invalid inner_dims_pos vector}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 1] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32> |
| return %0 : tensor<8x8x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_duplicate_element_in_outer_perm(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> { |
| // expected-error@+1 {{invalid outer_dims_perm vector}} |
| %0 = linalg.pack %input outer_dims_perm = [1, 1] inner_dims_pos = [0, 1] inner_tiles = [2, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32> |
| return %0 : tensor<8x8x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_output_rank(%input: tensor<256x128xf32>, %output: tensor<64x32x16xf32>) -> tensor<64x32x16xf32> { |
| // expected-error@+1 {{packed rank != (unpacked rank + num tiling factors), got 3 != 4}} |
| %0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %output : tensor<256x128xf32> -> tensor<64x32x16xf32> |
| return %0 : tensor<64x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> { |
| // expected-error@+1 {{invalid zero tile factor}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [0, 2] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32> |
| return %0 : tensor<8x8x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_mismatch_inner_tile_size_and_output_shape( |
| %input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> { |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32> |
| return %0 : tensor<?x?x8x8xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_dynamic_inner_tile_size_and_static_output_shape( |
| %input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> { |
| %c8 = arith.constant 8 : index |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, %c8] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32> |
| return %0 : tensor<?x?x8x8xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_static_inner_tile_size_and_dynamic_output_shape( |
| %input : tensor<?x?xf32>, %output : tensor<?x?x8x?xf32>) -> tensor<?x?x8x?xf32> { |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %output : tensor<?x?xf32> -> tensor<?x?x8x?xf32> |
| return %0 : tensor<?x?x8x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_outer_dims_perm(%source: tensor<128x256xf32>, %dest: tensor<16x4x32x16xf32>) -> tensor<16x4x32x16xf32> { |
| // expected-error@+1 {{outer_dims_perm must be a permutation or empty}} |
| %0 = linalg.pack %source outer_dims_perm = [0] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<128x256xf32> -> tensor<16x4x32x16xf32> |
| return %0 : tensor<16x4x32x16xf32> |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.unpack |
| //===----------------------------------------------------------------------===// |
| |
| func.func @unpack_invalid_output_rank(%input: tensor<256x128xf32>, %output: tensor<64x32x16xf32>) -> tensor<256x128xf32> { |
| // expected-error@+1 {{packed rank != (unpacked rank + num tiling factors), got 3 != 4}} |
| %0 = linalg.unpack %output inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %input : tensor<64x32x16xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_invalid_out_of_bound_outer_perm(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> { |
| // expected-error@+1 {{invalid outer_dims_perm vector}} |
| %0 = linalg.unpack %output outer_dims_perm = [2, 1] inner_dims_pos = [0, 1] inner_tiles = [2, 2] into %input : tensor<8x8x32x16xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_invalid_outer_dims_perm(%source: tensor<128x256xf32>, %dest: tensor<16x4x32x16xf32>) -> tensor<128x256xf32> { |
| // expected-error@+1 {{outer_dims_perm must be a permutation or empty}} |
| %0 = linalg.unpack %dest outer_dims_perm = [1] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %source : tensor<16x4x32x16xf32> -> tensor<128x256xf32> |
| return %0 : tensor<128x256xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_with_artificial_padding(%input: tensor<9xf32>, %output: tensor<3x8xf32>) -> tensor<3x8xf32> { |
| %cst = arith.constant 0.0 : f32 |
| // expected-error@+1 {{expected 'tensor<2x8xf32>' for the packed domain value, got 'tensor<3x8xf32>'}} |
| %0 = linalg.pack %input padding_value(%cst : f32) inner_dims_pos = [0] |
| inner_tiles = [8] into %output |
| : tensor<9xf32> -> tensor<3x8xf32> |
| return %0 : tensor<3x8xf32> |
| } |
| |
| // ----- |
| |
| // The outer dims in the output tensor are incorrectly/unexpectedly transposed. |
| // This could be fixed by adding `outer_dims_perm = [1, 0]` (the default value assumes no transpose). |
| func.func @pack_invalid_result_shape(%input: tensor<256x128xf32>, %output: tensor<4x16x32x16xf32>) -> tensor<4x16x32x16xf32> { |
| // expected-error@+1 {{expected 'tensor<16x4x32x16xf32>' for the packed domain value, got 'tensor<4x16x32x16xf32>'}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [32, 16] into %output : tensor<256x128xf32> -> tensor<4x16x32x16xf32> |
| return %0 : tensor<4x16x32x16xf32> |
| } |
| |
| // ----- |
| |
| func.func @pack_invalid_result_shape(%input: tensor<256x128xf32>, %output: tensor<8x7x16x32xf32>) -> tensor<8x7x16x32xf32> { |
| // expected-error@+1 {{expected 'tensor<8x8x16x32xf32>' for the packed domain value, got 'tensor<8x7x16x32xf32>'}} |
| %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 32] into %output : tensor<256x128xf32> -> tensor<8x7x16x32xf32> |
| return %0 : tensor<8x7x16x32xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_with_artifical_tiles_that_are_dropped(%input: tensor<3x8xf32>, %output: tensor<9xf32>) -> tensor<9xf32> { |
| // expected-error@+1 {{expected 'tensor<2x8xf32>' for the packed domain value, got 'tensor<3x8xf32>'}} |
| %0 = linalg.unpack %input inner_dims_pos = [0] inner_tiles = [8] into %output |
| : tensor<3x8xf32> -> tensor<9xf32> |
| return %0 : tensor<9xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_invalid_source_shape(%output: tensor<256x128xf32>, %input: tensor<8x8x4x32xf32>) -> tensor<256x128xf32> { |
| // expected-error@+1 {{expected 'tensor<8x32x4x32xf32>' for the packed domain value, got 'tensor<8x8x4x32xf32>'}} |
| %0 = linalg.unpack %input inner_dims_pos = [1, 0] inner_tiles = [4, 32] into %output : tensor<8x8x4x32xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_mismatch_inner_tile_size_and_output_shape( |
| %input : tensor<?x?x8x8xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x8x8xf32> -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_dynamic_inner_tile_size_and_static_output_shape( |
| %input : tensor<?x?x8x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %c8 = arith.constant 8 : index |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%c8, 4] into %output : tensor<?x?x8x4xf32> -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @unpack_static_inner_tile_size_and_dynamic_output_shape( |
| %input : tensor<?x?x?x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // expected-error@+1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}} |
| %0 = linalg.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x?x4xf32> -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.reduce |
| //===----------------------------------------------------------------------===// |
| |
| |
| func.func @reduce_non_operation_name(%arg0: tensor<4xf32>, %arg1: tensor<f32>) -> tensor<f32> { |
| // expected-error @below {{expected bare identifier or keyword}} |
| %0 = linalg.reduce {@reduce_fusion_elementwise} ins( |
| %arg0: tensor<4xf32>) outs(%arg1: tensor<f32>) dimensions = [0] |
| return %0 : tensor<f32> |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.pooling_nhwc_* |
| //===----------------------------------------------------------------------===// |
| |
| func.func @pooling_nhwc_max_unsigned_float_type( |
| %input: tensor<1x4x4x1xf32>, |
| %filter: tensor<2x2xf32>, |
| %init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> { |
| // expected-error @+1 {{unsupported operation: unsigned max not on uint}} |
| %0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: tensor<1x4x4x1xf32>, tensor<2x2xf32>) |
| outs (%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| return %0 : tensor<1x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nhwc_max_unsigned_i1( |
| %input: tensor<1x4x4x1xi1>, |
| %filter: tensor<2x2xi1>, |
| %init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> { |
| // expected-error @+1 {{unsupported operation: unsigned max not on uint}} |
| %0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: tensor<1x4x4x1xi1>, tensor<2x2xi1>) |
| outs (%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> |
| return %0 : tensor<1x2x2x1xi1> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nhwc_min_unsigned_float_type( |
| %input: tensor<1x4x4x1xf32>, |
| %filter: tensor<2x2xf32>, |
| %init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> { |
| // expected-error @+1 {{unsupported operation: unsigned min not on uint}} |
| %0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: tensor<1x4x4x1xf32>, tensor<2x2xf32>) |
| outs (%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| return %0 : tensor<1x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nhwc_min_unsigned_i1( |
| %input: tensor<1x4x4x1xi1>, |
| %filter: tensor<2x2xi1>, |
| %init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> { |
| // expected-error @+1 {{unsupported operation: unsigned min not on uint}} |
| %0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: tensor<1x4x4x1xi1>, tensor<2x2xi1>) |
| outs (%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> |
| return %0 : tensor<1x2x2x1xi1> |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // linalg.pooling_nwc_* |
| //===----------------------------------------------------------------------===// |
| |
| func.func @pooling_nwc_max_unsigned_float_type( |
| %input: tensor<1x4x1xf32>, |
| %filter: tensor<2xf32>, |
| %init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> { |
| // expected-error @+1 {{unsupported operation: unsigned max not on uint}} |
| %0 = linalg.pooling_nwc_max_unsigned {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: tensor<1x4x1xf32>, tensor<2xf32>) |
| outs (%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> |
| return %0 : tensor<1x2x1xf32> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nwc_max_unsigned_i1( |
| %input: tensor<1x4x1xi1>, |
| %filter: tensor<2xi1>, |
| %init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> { |
| // expected-error @+1 {{unsupported operation: unsigned max not on uint}} |
| %0 = linalg.pooling_nwc_max_unsigned {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: tensor<1x4x1xi1>, tensor<2xi1>) |
| outs (%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> |
| return %0 : tensor<1x2x1xi1> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nwc_min_unsigned_float_type( |
| %input: tensor<1x4x1xf32>, |
| %filter: tensor<2xf32>, |
| %init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> { |
| // expected-error @+1 {{unsupported operation: unsigned min not on uint}} |
| %0 = linalg.pooling_nwc_min_unsigned {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: tensor<1x4x1xf32>, tensor<2xf32>) |
| outs (%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> |
| return %0 : tensor<1x2x1xf32> |
| } |
| |
| // ----- |
| |
| func.func @pooling_nwc_min_unsigned_i1( |
| %input: tensor<1x4x1xi1>, |
| %filter: tensor<2xi1>, |
| %init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> { |
| // expected-error @+1 {{unsupported operation: unsigned min not on uint}} |
| %0 = linalg.pooling_nwc_min_unsigned {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: tensor<1x4x1xi1>, tensor<2xi1>) |
| outs (%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> |
| return %0 : tensor<1x2x1xi1> |
| } |
| |
| // ----- |
| |
| //===----------------------------------------------------------------------===// |
| // Tests for generic infrastructure for named Ops. The actual Ops used are |
| // secondary - we merely want to ensure that the diagnostic infra triggers |
| // correctly. |
| //===----------------------------------------------------------------------===// |
| |
| module { |
| func.func @add_invalid_mixed_types(%in_f32: memref<3xf32>, %in_i32 : memref< 3xi32>, %out_f32: memref<3xf32>, %arg3: memref<3xf32>) { |
| // expected-error @below {{Cannot build binary Linalg operation: expects allComplex, allFloatingPoint, or allInteger, got 'f32' and 'i32'}} |
| linalg.add ins(%in_f32, %in_i32 : memref<3xf32>, memref< 3xi32>) outs(%out_f32 : memref<3xf32>) |
| return |
| } |
| } |
| |
| // ----- |
| |
| func.func @matmul_invalid_mixed_types(%t: tensor<?xf16>, %f: vector<4xf16>) |
| -> (tensor<?xf16>, vector<4xf16>) |
| { |
| // expected-warning @unknown {{could not cast operand of type 'f16' to 'vector<4xf16>'}} |
| // expected-error @below {{Cannot build binary Linalg operation: expects allComplex, allFloatingPoint, or allInteger, got 'vector<4xf16>' and 'f16'}} |
| %0 = linalg.matmul ins(%t, %t : tensor<?xf16>, tensor<?xf16>) |
| outs(%f : vector<4xf16>) -> tensor<?xf16> |
| func.return %0, %f : tensor<?xf16>, vector<4xf16> |
| } |
