// RUN: mlir-opt --split-input-file -pass-pipeline="builtin.module(func.func(tosa-to-linalg))" %s -verify-diagnostics -o -| FileCheck %s // CHECK: #[[$MAP0:.*]] = affine_map<() -> ()> // CHECK-LABEL: @test_abs_scalar // CHECK-SAME: ([[ARG0:%[0-9a-zA-Z_]*]] func.func @test_abs_scalar(%arg0: tensor) -> tensor { // CHECK: [[INIT:%.+]] = tensor.empty() : tensor // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = []} ins([[ARG0]] : tensor) outs([[INIT]] : tensor) { // CHECK: ^bb0([[ARG1:%.*]]: f32, [[ARG2:%.*]]: f32): // CHECK: [[ELEMENT:%.*]] = math.absf [[ARG1]] : f32 // CHECK: linalg.yield [[ELEMENT]] : f32 // CHECK: } -> tensor %0 = tosa.abs %arg0 : (tensor) -> tensor // CHECK: return [[GENERIC]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_abs_1d_cast_static_to_dynamic // CHECK-SAME: ([[ARG0:%[0-9a-zA-Z_]*]] func.func @test_abs_1d_cast_static_to_dynamic(%arg0: tensor<5xf32>) -> tensor { // CHECK: [[EMPTY:%.+]] = tensor.empty() : tensor<5xf32> // CHECK: [[RESULT:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins([[ARG0]] : tensor<5xf32>) outs([[EMPTY]] : tensor<5xf32>) { // CHECK: ^bb0([[IN0:%.+]]: f32, [[OUT0:%.+]]: f32): // CHECK: [[ABS:%.+]] = math.absf [[IN0]] : f32 // CHECK: linalg.yield [[ABS]] : f32 // CHECK: } -> tensor<5xf32> // CHECK: [[CAST_RESULT:%.+]] = tensor.cast [[RESULT]] : tensor<5xf32> to tensor %0 = "tosa.abs"(%arg0) : (tensor<5xf32>) -> tensor // CHECK: return [[CAST_RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_abs_1d_cast_dynamic_to_static // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]] func.func @test_abs_1d_cast_dynamic_to_static(%arg0: tensor) -> tensor<5xf32> { // CHECK: %[[ZERO:.*]] = arith.constant 0 : index // CHECK: %[[DIM_SIZE:.*]] = tensor.dim %[[ARG0]], %[[ZERO]] : tensor // CHECK: %[[EMPTY:.*]] = tensor.empty(%[[DIM_SIZE]]) : tensor // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor) outs(%[[EMPTY]] : tensor) { // CHECK: ^bb0(%[[VAL_0:.*]]: f32, %[[VAL_1:.*]]: f32): // CHECK: %[[VAL_2:.*]] = math.absf %[[VAL_0]] : f32 // CHECK: linalg.yield %[[VAL_2]] : f32 // CHECK: } -> tensor // CHECK: %[[CAST_RESULT:.*]] = tensor.cast %[[RESULT]] : tensor to tensor<5xf32> %0 = "tosa.abs"(%arg0) : (tensor) -> tensor<5xf32> // CHECK: return %[[CAST_RESULT]] : tensor<5xf32> return %0 : tensor<5xf32> } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_abs_1d_dynamic // CHECK-SAME: ([[ARG0:%[0-9a-zA-Z_]*]] func.func @test_abs_1d_dynamic(%arg0: tensor) -> tensor { // CHECK: [[ZERO:%.+]] = arith.constant 0 : index // CHECK: [[DIM:%.+]] = tensor.dim [[ARG0]], [[ZERO]] : tensor // CHECK: [[EMPTY:%.+]] = tensor.empty([[DIM]]) : tensor // CHECK: [[RESULT:%.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%arg0 : tensor) outs([[EMPTY]] : tensor) { // CHECK: ^bb0([[IN0:%.+]]: f32, [[OUT0:%.+]]: f32): // CHECK: [[ABSF:%.+]] = math.absf [[IN0]] : f32 // CHECK: linalg.yield [[ABSF]] : f32 // CHECK: } -> tensor %0 = tosa.abs %arg0 : (tensor) -> tensor // CHECK: return [[RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<() -> ()> // CHECK-LABEL: @test_add_0d // CHECK-SAME: [[ARG0:%[0-9a-zA-Z_]*]]: // CHECK-SAME: [[ARG1:%[0-9a-zA-Z_]*]]: func.func @test_add_0d(%arg0: tensor, %arg1: tensor) -> tensor { // CHECK: [[EMPTY:%.+]] = tensor.empty() : tensor // CHECK: [[RESULT:%.+]] = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = []} ins([[ARG0]], [[ARG1]] : tensor, tensor) outs([[EMPTY]] : tensor) { // CHECK: ^bb0([[IN0:%.+]]: f32, [[IN1:%.+]]: f32, [[OUT0:%.+]]: f32): // CHECK: [[ADDF:%.+]] = arith.addf [[IN0]], [[IN1]] : f32 // CHECK: linalg.yield [[ADDF]] : f32 // CHECK: } -> tensor %0 = tosa.add %arg0, %arg1 : (tensor, tensor) -> tensor // CHECK: return [[RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (0, d1)> // CHECK-LABEL: func.func @test_add_2d_broadcast( // CHECK-SAME: %[[ARG0:.*]]: tensor<2x1xf32>, // CHECK-SAME: %[[ARG1:.*]]: tensor<1x1xf32>) -> tensor<2x1xf32> { // CHECK: %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<2x1xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<2x1xf32>, tensor<1x1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<2x1xf32>) { // CHECK: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32): // CHECK: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]] : f32 // CHECK: linalg.yield %[[ADD]] : f32 // CHECK: } -> tensor<2x1xf32> // CHECK: return %[[RESULT]] : tensor<2x1xf32> // CHECK: } func.func @test_add_2d_broadcast(%arg0: tensor<2x1xf32>, %arg1: tensor<1x1xf32>) -> tensor<2x1xf32> { // tosa element-wise operators now require operands of equal ranks %0 = tosa.add %arg0, %arg1 : (tensor<2x1xf32>, tensor<1x1xf32>) -> tensor<2x1xf32> return %0 : tensor<2x1xf32> } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_all_dynamic // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_all_dynamic(%arg0: tensor, %arg1: tensor) -> tensor { // CHECK: %[[CONST0:.*]] = arith.constant 0 : index // CHECK: %[[ARG0_DIM0:.*]] = tensor.dim %[[ARG0]], %[[CONST0]] : tensor // CHECK: %[[ARG1_DIM0:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[ARG0_MAX_DIM:.*]] = arith.maxui %[[ARG0_DIM0]], %[[ARG1_DIM0]] : index // CHECK: %[[CONST1:.*]] = arith.constant 1 : index // CHECK: %[[VAL_0:.*]] = tensor.dim %[[ARG0]], %[[CONST0]] : tensor // CHECK: %[[VAL_1:.*]] = arith.cmpi eq, %[[VAL_0]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM0_BROADCAST:.*]] = scf.if %[[VAL_1]] -> (tensor) { // CHECK: %[[VAL_2:.*]] = tensor.empty(%[[ARG0_MAX_DIM]]) : tensor // CHECK: %[[VAL_3:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor) outs(%[[VAL_2]] : tensor) { // CHECK: ^bb0(%[[VAL_4:.*]]: f32, %[[VAL_5:.*]]: f32): // CHECK: linalg.yield %[[VAL_4]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_3]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG0]] : tensor // CHECK: } // CHECK: %[[VAL_6:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[VAL_7:.*]] = arith.cmpi eq, %[[VAL_6]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM1_BROADCAST:.*]] = scf.if %[[VAL_7]] -> (tensor) { // CHECK: %[[VAL_8:.*]] = tensor.empty(%[[ARG0_MAX_DIM]]) : tensor // CHECK: %[[VAL_9:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG1]] : tensor) outs(%[[VAL_8]] : tensor) { // CHECK: ^bb0(%[[VAL_10:.*]]: f32, %[[VAL_11:.*]]: f32): // CHECK: linalg.yield %[[VAL_10]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_9]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG1]] : tensor // CHECK: } // CHECK: %[[VAL_12:.*]] = tensor.empty(%[[ARG0_MAX_DIM]]) : tensor // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0_DIM0_BROADCAST]], %[[ARG0_DIM1_BROADCAST]] : tensor, tensor) outs(%[[VAL_12]] : tensor) { // CHECK: ^bb0(%[[VAL_13:.*]]: f32, %[[VAL_14:.*]]: f32, %[[VAL_15:.*]]: f32): // CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32 // CHECK: linalg.yield %[[VAL_16]] : f32 // CHECK: } -> tensor %0 = tosa.add %arg0, %arg1 : (tensor, tensor) -> tensor // CHECK: return %[[RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_broadcast_dynamic_to_static // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_broadcast_dynamic_to_static(%arg0: tensor<5xf32>, %arg1: tensor) -> tensor<5xf32> { // CHECK: %[[CONST1:.*]] = arith.constant 1 : index // CHECK: %[[CONST0:.*]] = arith.constant 0 : index // CHECK: %[[ARG1_DIM0:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[VAL_0:.*]] = arith.cmpi eq, %[[ARG1_DIM0]], %[[CONST1]] : index // CHECK: %[[ARG1_DIM0_BROADCAST:.*]] = scf.if %[[VAL_0]] -> (tensor) { // CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<5xf32> // CHECK: %[[VAL_2:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG1]] : tensor) outs(%[[VAL_1]] : tensor<5xf32>) { // CHECK: ^bb0(%[[VAL_3:.*]]: f32, %[[VAL_4:.*]]: f32): // CHECK: linalg.yield %[[VAL_3]] : f32 // CHECK: } -> tensor<5xf32> // CHECK: %[[VAL_5:.*]] = tensor.cast %[[VAL_2]] : tensor<5xf32> to tensor // CHECK: scf.yield %[[VAL_5]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG1]] : tensor // CHECK: } // CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<5xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1_DIM0_BROADCAST]] : tensor<5xf32>, tensor) outs(%[[VAL_6]] : tensor<5xf32>) { // CHECK: ^bb0(%[[VAL_7:.*]]: f32, %[[VAL_8:.*]]: f32, %[[VAL_9:.*]]: f32): // CHECK: %[[VAL_10:.*]] = arith.addf %[[VAL_7]], %[[VAL_8]] : f32 // CHECK: linalg.yield %[[VAL_10]] : f32 // CHECK: } -> tensor<5xf32> %0 = tosa.add %arg0, %arg1 : (tensor<5xf32>, tensor) -> tensor<5xf32> // CHECK: return %[[RESULT]] : tensor<5xf32> return %0 : tensor<5xf32> } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_broadcast_static_to_dynamic // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_broadcast_static_to_dynamic(%arg0: tensor<1xf32>, %arg1: tensor) -> tensor { // CHECK: %[[CONST0:.*]] = arith.constant 0 : index // CHECK: %[[ARG1_DIM0:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[VAL_0:.*]] = tensor.empty(%[[ARG1_DIM0]]) : tensor // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<1xf32>, tensor) outs(%[[VAL_0]] : tensor) { // CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32): // CHECK: %[[VAL_4:.*]] = arith.addf %[[VAL_1]], %[[VAL_2]] : f32 // CHECK: linalg.yield %[[VAL_4]] : f32 // CHECK: } -> tensor %0 = tosa.add %arg0, %arg1 : (tensor<1xf32>, tensor) -> tensor // CHECK: return %[[RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_broadcast_static_to_static // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_broadcast_static_to_static(%arg0: tensor<1xf32>, %arg1: tensor<3xf32>) -> tensor<3xf32> { // CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<3xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<1xf32>, tensor<3xf32>) outs(%[[VAL_0]] : tensor<3xf32>) { // CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32): // CHECK: %[[VAL_4:.*]] = arith.addf %[[VAL_1]], %[[VAL_2]] : f32 // CHECK: linalg.yield %[[VAL_4]] : f32 // CHECK: } -> tensor<3xf32> %0 = tosa.add %arg0, %arg1 : (tensor<1xf32>, tensor<3xf32>) -> tensor<3xf32> // CHECK: return %[[RESULT]] : tensor<3xf32> return %0 : tensor<3xf32> } // ----- // CHECK: #[[$MAP:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_matching_no_broadcast // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_matching_no_broadcast(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { // CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<1xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP]], #[[$MAP]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<1xf32>, tensor<1xf32>) outs(%[[VAL_0]] : tensor<1xf32>) { // CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32): // CHECK: %[[VAL_4:.*]] = arith.addf %[[VAL_1]], %[[VAL_2]] : f32 // CHECK: linalg.yield %[[VAL_4]] : f32 // CHECK: } -> tensor<1xf32> %0 = tosa.add %arg0, %arg1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: return %[[RESULT]] : tensor<1xf32> return %0 : tensor<1xf32> } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @test_add_1d_matching_static // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_1d_matching_static(%arg0: tensor<3xf32>, %arg1: tensor<3xf32>) -> tensor<3xf32> { // CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<3xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<3xf32>, tensor<3xf32>) outs(%[[VAL_0]] : tensor<3xf32>) { // CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32): // CHECK: %[[VAL_4:.*]] = arith.addf %[[VAL_1]], %[[VAL_2]] : f32 // CHECK: linalg.yield %[[VAL_4]] : f32 // CHECK: } -> tensor<3xf32> %0 = tosa.add %arg0, %arg1 : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32> // CHECK: return %[[RESULT]] : tensor<3xf32> return %0 : tensor<3xf32> } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (0, d1)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, 0)> // CHECK-LABEL: @test_add_2d_all_dynamic // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @test_add_2d_all_dynamic(%arg0: tensor, %arg1: tensor) -> tensor { // CHECK: %[[CONST0:.*]] = arith.constant 0 : index // CHECK: %[[ARG0_DIM0:.*]] = tensor.dim %[[ARG0]], %[[CONST0]] : tensor // CHECK: %[[ARG1_DIM0:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[MAX_DIM0:.*]] = arith.maxui %[[ARG0_DIM0]], %[[ARG1_DIM0]] : index // CHECK: %[[CONST1:.*]] = arith.constant 1 : index // CHECK: %[[ARG0_DIM1:.*]] = tensor.dim %[[ARG0]], %[[CONST1]] : tensor // CHECK: %[[ARG1_DIM1:.*]] = tensor.dim %[[ARG1]], %[[CONST1]] : tensor // CHECK: %[[MAX_DIM1:.*]] = arith.maxui %[[ARG0_DIM1]], %[[ARG1_DIM1]] : index // CHECK: %[[VAL_0:.*]] = tensor.dim %[[ARG0]], %[[CONST0]] : tensor // CHECK: %[[VAL_1:.*]] = arith.cmpi eq, %[[VAL_0]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM0_BROADCAST:.*]] = scf.if %[[VAL_1]] -> (tensor) { // CHECK: %[[LOCAL_CONST1:.*]] = arith.constant 1 : index // CHECK: %[[VAL_2:.*]] = tensor.dim %[[ARG0]], %[[LOCAL_CONST1]] : tensor // CHECK: %[[VAL_3:.*]] = tensor.empty(%[[MAX_DIM0]], %[[VAL_2]]) : tensor // CHECK: %[[VAL_4:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor) outs(%[[VAL_3]] : tensor) { // CHECK: ^bb0(%[[VAL_5:.*]]: f32, %[[VAL_6:.*]]: f32): // CHECK: linalg.yield %[[VAL_5]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_4]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG0]] : tensor // CHECK: } // CHECK: %[[VAL_7:.*]] = tensor.dim %[[ARG0_DIM0_BROADCAST]], %[[CONST1]] : tensor // CHECK: %[[VAL_8:.*]] = arith.cmpi eq, %[[VAL_7]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM1_BROADCAST:.*]] = scf.if %[[VAL_8]] -> (tensor) { // CHECK: %[[LOCAL_CONST0:.*]] = arith.constant 0 : index // CHECK: %[[VAL_9:.*]] = tensor.dim %[[ARG0_DIM0_BROADCAST]], %[[LOCAL_CONST0]] : tensor // CHECK: %[[VAL_10:.*]] = tensor.empty(%[[VAL_9]], %[[MAX_DIM1]]) : tensor // CHECK: %[[VAL_11:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0_DIM0_BROADCAST]] : tensor) outs(%[[VAL_10]] : tensor) { // CHECK: ^bb0(%[[VAL_12:.*]]: f32, %[[VAL_13:.*]]: f32): // CHECK: linalg.yield %[[VAL_12]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_11]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG0_DIM0_BROADCAST]] : tensor // CHECK: } // CHECK: %[[VAL_14:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor // CHECK: %[[VAL_15:.*]] = arith.cmpi eq, %[[VAL_14]], %[[CONST1]] : index // CHECK: %[[ARG1_DIM0_BROADCAST:.*]] = scf.if %[[VAL_15]] -> (tensor) { // CHECK: %[[LOCAL_CONST1:.*]] = arith.constant 1 : index // CHECK: %[[VAL_16:.*]] = tensor.dim %[[ARG1]], %[[LOCAL_CONST1]] : tensor // CHECK: %[[VAL_17:.*]] = tensor.empty(%[[MAX_DIM0]], %[[VAL_16]]) : tensor // CHECK: %[[VAL_18:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG1]] : tensor) outs(%[[VAL_17]] : tensor) { // CHECK: ^bb0(%[[VAL_19:.*]]: f32, %[[VAL_20:.*]]: f32): // CHECK: linalg.yield %[[VAL_19]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_18]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG1]] : tensor // CHECK: } // CHECK: %[[VAL_21:.*]] = tensor.dim %[[ARG1_DIM0_BROADCAST]], %[[CONST1]] : tensor // CHECK: %[[VAL_22:.*]] = arith.cmpi eq, %[[VAL_21]], %[[CONST1]] : index // CHECK: %[[ARG1_DIM1_BROADCAST:.*]] = scf.if %[[VAL_22]] -> (tensor) { // CHECK: %[[LOCAL_CONST0:.*]] = arith.constant 0 : index // CHECK: %[[VAL_23:.*]] = tensor.dim %[[ARG1_DIM0_BROADCAST]], %[[LOCAL_CONST0]] : tensor // CHECK: %[[VAL_24:.*]] = tensor.empty(%[[VAL_23]], %[[MAX_DIM1]]) : tensor // CHECK: %[[VAL_25:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG1_DIM0_BROADCAST]] : tensor) outs(%[[VAL_24]] : tensor) { // CHECK: ^bb0(%[[VAL_26:.*]]: f32, %[[VAL_27:.*]]: f32): // CHECK: linalg.yield %[[VAL_26]] : f32 // CHECK: } -> tensor // CHECK: scf.yield %[[VAL_25]] : tensor // CHECK: } else { // CHECK: scf.yield %[[ARG1_DIM0_BROADCAST]] : tensor // CHECK: } // CHECK: %[[VAL_28:.*]] = tensor.empty(%[[MAX_DIM0]], %[[MAX_DIM1]]) : tensor // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0_DIM1_BROADCAST]], %[[ARG1_DIM1_BROADCAST]] : tensor, tensor) outs(%[[VAL_28]] : tensor) { // CHECK: ^bb0(%[[VAL_29:.*]]: f32, %[[VAL_30:.*]]: f32, %[[VAL_31:.*]]: f32): // CHECK: %[[VAL_32:.*]] = arith.addf %[[VAL_29]], %[[VAL_30]] : f32 // CHECK: linalg.yield %[[VAL_32]] : f32 // CHECK: } -> tensor %0 = tosa.add %arg0, %arg1 : (tensor, tensor) -> tensor // CHECK: return %[[RESULT]] : tensor return %0 : tensor } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (d0, 0)> // CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK-LABEL: @test_select_2d_one_dynamic // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG2:[0-9a-zA-Z_]*]]: func.func @test_select_2d_one_dynamic(%arg0: tensor<2x?xi1>, %arg1: tensor<2x?xf32>, %arg2: tensor<2x?xf32>) -> tensor<2x?xf32> { // CHECK: %[[CONST1:.*]] = arith.constant 1 : index // CHECK: %[[ARG0_DIM1:.*]] = tensor.dim %[[ARG0]], %[[CONST1]] : tensor<2x?xi1> // CHECK: %[[ARG1_DIM1:.*]] = tensor.dim %[[ARG1]], %[[CONST1]] : tensor<2x?xf32> // CHECK: %[[VAL_0:.*]] = arith.maxui %[[ARG0_DIM1]], %[[ARG1_DIM1]] : index // CHECK: %[[ARG2_DIM1:.*]] = tensor.dim %[[ARG2]], %[[CONST1]] : tensor<2x?xf32> // CHECK: %[[MAX_DIM1:.*]] = arith.maxui %[[VAL_0]], %[[ARG2_DIM1]] : index // CHECK: %[[VAL_1:.*]] = tensor.dim %[[ARG0]], %[[CONST1]] : tensor<2x?xi1> // CHECK: %[[VAL_2:.*]] = arith.cmpi eq, %[[VAL_1]], %[[CONST1]] : index // CHECK: %[[ARG0_BROADCAST:.*]] = scf.if %[[VAL_2]] -> (tensor<2x?xi1>) { // CHECK: %[[VAL_3:.*]] = tensor.empty(%[[MAX_DIM1]]) : tensor<2x?xi1> // CHECK: %[[VAL_4:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x?xi1>) outs(%[[VAL_3]] : tensor<2x?xi1>) { // CHECK: ^bb0(%[[VAL_5:.*]]: i1, %[[VAL_6:.*]]: i1): // CHECK: linalg.yield %[[VAL_5]] : i1 // CHECK: } -> tensor<2x?xi1> // CHECK: scf.yield %[[VAL_4]] : tensor<2x?xi1> // CHECK: } else { // CHECK: scf.yield %[[ARG0]] : tensor<2x?xi1> // CHECK: } // CHECK: %[[VAL_7:.*]] = tensor.dim %[[ARG1]], %[[CONST1]] : tensor<2x?xf32> // CHECK: %[[VAL_8:.*]] = arith.cmpi eq, %[[VAL_7]], %[[CONST1]] : index // CHECK: %[[ARG1_BROADCAST:.*]] = scf.if %[[VAL_8]] -> (tensor<2x?xf32>) { // CHECK: %[[VAL_9:.*]] = tensor.empty(%[[MAX_DIM1]]) : tensor<2x?xf32> // CHECK: %[[VAL_10:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG1]] : tensor<2x?xf32>) outs(%[[VAL_9]] : tensor<2x?xf32>) { // CHECK: ^bb0(%[[VAL_11:.*]]: f32, %[[VAL_12:.*]]: f32): // CHECK: linalg.yield %[[VAL_11]] : f32 // CHECK: } -> tensor<2x?xf32> // CHECK: scf.yield %[[VAL_10]] : tensor<2x?xf32> // CHECK: } else { // CHECK: scf.yield %[[ARG1]] : tensor<2x?xf32> // CHECK: } // CHECK: %[[VAL_13:.*]] = tensor.dim %[[ARG2]], %[[CONST1]] : tensor<2x?xf32> // CHECK: %[[VAL_14:.*]] = arith.cmpi eq, %[[VAL_13]], %[[CONST1]] : index // CHECK: %[[ARG2_BROADCAST:.*]] = scf.if %[[VAL_14]] -> (tensor<2x?xf32>) { // CHECK: %[[VAL_15:.*]] = tensor.empty(%[[MAX_DIM1]]) : tensor<2x?xf32> // CHECK: %[[VAL_16:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG2]] : tensor<2x?xf32>) outs(%[[VAL_15]] : tensor<2x?xf32>) { // CHECK: ^bb0(%[[VAL_17:.*]]: f32, %[[VAL_18:.*]]: f32): // CHECK: linalg.yield %[[VAL_17]] : f32 // CHECK: } -> tensor<2x?xf32> // CHECK: scf.yield %[[VAL_16]] : tensor<2x?xf32> // CHECK: } else { // CHECK: scf.yield %[[ARG2]] : tensor<2x?xf32> // CHECK: } // CHECK: %[[VAL_19:.*]] = tensor.empty(%[[MAX_DIM1]]) : tensor<2x?xf32> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0_BROADCAST]], %[[ARG1_BROADCAST]], %[[ARG2_BROADCAST]] : tensor<2x?xi1>, tensor<2x?xf32>, tensor<2x?xf32>) outs(%[[VAL_19]] : tensor<2x?xf32>) { // CHECK: ^bb0(%[[VAL_20:.*]]: i1, %[[VAL_21:.*]]: f32, %[[VAL_22:.*]]: f32, %[[VAL_23:.*]]: f32): // CHECK: %[[VAL_24:.*]] = arith.select %[[VAL_20]], %[[VAL_21]], %[[VAL_22]] : f32 // CHECK: linalg.yield %[[VAL_24]] : f32 // CHECK: } -> tensor<2x?xf32> %0 = tosa.select %arg0, %arg1, %arg2 : (tensor<2x?xi1>, tensor<2x?xf32>, tensor<2x?xf32>) -> tensor<2x?xf32> // CHECK: return %[[RESULT]] : tensor<2x?xf32> return %0 : tensor<2x?xf32> } // ----- // CHECK-LABEL: @test_simple_f32 func.func @test_simple_f32(%arg0: tensor<1xf32>) -> () { // CHECK: linalg.generic // CHECK: tanh %0 = tosa.tanh %arg0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: math.absf %1 = tosa.abs %arg0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.addf %2 = tosa.add %0, %0 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.subf %3 = tosa.sub %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.mulf %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %4 = tosa.mul %0, %1, %shift : (tensor<1xf32>, tensor<1xf32>, tensor<1xi8>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.negf %in_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32> %out_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32> %5 = tosa.negate %0, %in_zp, %out_zp : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: pow %6 = tosa.pow %1, %2 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: rsqrt %7 = tosa.rsqrt %1 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: log %8 = tosa.log %arg0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: exp %9 = tosa.exp %arg0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.cmpf %10 = tosa.greater %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.cmpf %11 = tosa.greater_equal %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.cmpf %12 = tosa.equal %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: select %13 = tosa.select %10, %0, %1 : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.maximumf %14 = tosa.maximum %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.minimumf %15 = tosa.minimum %0, %1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: ceil %16 = tosa.ceil %0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: floor %17 = tosa.floor %0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.minimumf // CHECK: arith.maximumf %18 = tosa.clamp %0 {min_val = 1.0 : f32, max_val = 5.0 : f32} : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.negf // CHECK: exp // CHECK: arith.addf // CHECK: arith.divf %19 = tosa.sigmoid %0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: [[ROUND:%.+]] = math.roundeven {{%.+}} : f32 // CHECK: [[CSTMIN:%.+]] = arith.constant -2.14748365E+9 : f32 // CHECK: [[CSTMAXP1:%.+]] = arith.constant 2.14748365E+9 : f32 // CHECK: [[CSTMAX:%.+]] = arith.constant 2147483647 : i32 // CHECK: [[MAX:%.+]] = arith.maximumf [[ROUND]], [[CSTMIN]] : f32 // CHECK: [[CONV:%.+]] = arith.fptosi [[MAX]] : f32 to i32 // CHECK: [[CMP:%.+]] = arith.cmpf uge, [[ROUND]], [[CSTMAXP1]] : f32 // CHECK: arith.select [[CMP]], [[CSTMAX]], [[CONV]] : i32 %20 = tosa.cast %0 : (tensor<1xf32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.constant 0 // CHECK: arith.cmpf %21 = tosa.cast %0 : (tensor<1xf32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.truncf %22 = tosa.cast %0 : (tensor<1xf32>) -> tensor<1xf16> // CHECK: linalg.generic // CHECK: arith.divf %23 = tosa.reciprocal %0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: math.erf %24 = tosa.erf %0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: math.sin %25 = tosa.sin %arg0 : (tensor<1xf32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: math.cos %26 = tosa.cos %arg0 : (tensor<1xf32>) -> tensor<1xf32> return } // ----- // CHECK-LABEL: @test_simple_f16 func.func @test_simple_f16(%arg0: tensor<1xf16>) -> () { // CHECK: linalg.generic // CHECK: arith.extf %0 = tosa.cast %arg0 : (tensor<1xf16>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: [[ROUND:%.+]] = math.roundeven {{%.+}} : f16 // CHECK: [[CSTMIN:%.+]] = arith.constant -1.280000e+02 : f16 // CHECK: [[CSTMAX:%.+]] = arith.constant 1.270000e+02 : f16 // CHECK: [[MIN:%.+]] = arith.minimumf [[ROUND]], [[CSTMAX]] : f16 // CHECK: [[CLAMP:%.+]] = arith.maximumf [[MIN]], [[CSTMIN]] : f16 // CHECK: arith.fptosi [[CLAMP]] : f16 to i8 %1 = "tosa.cast"(%arg0) : (tensor<1xf16>) -> tensor<1xi8> // CHECK: linalg.generic // CHECK: [[ROUND:%.+]] = math.roundeven {{%[a-z0-9_]+}} : f16 // CHECK: [[CONV:%.+]] = arith.fptosi [[ROUND]] : f16 to i32 // CHECK: [[POSINF:%.+]] = arith.constant 0x7C00 : f16 // CHECK: [[NEGINF:%.+]] = arith.constant 0xFC00 : f16 // CHECK: [[OVERFLOW:%.+]] = arith.cmpf ueq, [[ROUND]], [[POSINF]] : f16 // CHECK: [[UNDERFLOW:%.+]] = arith.cmpf ueq, [[ROUND]], [[NEGINF]] : f16 // CHECK: [[MININT:%.+]] = arith.constant -2147483648 : i32 // CHECK: [[MAXINT:%.+]] = arith.constant 2147483647 : i32 // CHECK: [[CLAMPPOSINF:%.+]] = arith.select [[OVERFLOW]], [[MAXINT]], [[CONV]] : i32 // CHECK: arith.select [[UNDERFLOW]], [[MININT]], [[CLAMPPOSINF]] : i32 %2 = "tosa.cast"(%arg0) : (tensor<1xf16>) -> tensor<1xi32> return } // ----- // CHECK-LABEL: @test_simple_i16 func.func @test_simple_i16(%arg0: tensor<1xi16>) -> () { // CHECK: linalg.generic // CHECK: arith.extsi // CHECK: arith.extsi // CHECK: arith.muli %shift = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %0 = tosa.mul %arg0, %arg0, %shift : (tensor<1xi16>, tensor<1xi16>, tensor<1xi8>) -> tensor<1xi32> return } // ----- // CHECK-LABEL: @test_simple_ui8 func.func @test_simple_ui8(%arg0: tensor<1xui8>) -> () { // CHECK: arith.uitofp %0 = tosa.cast %arg0 : (tensor<1xui8>) -> tensor<1xf32> return } // ----- // CHECK-LABEL: @test_simple_i32 func.func @test_simple_i32(%arg0: tensor<1xi32>, %unsigned: tensor<1xui32>, %unsigned64: tensor<1xui64>) -> () { // CHECK: linalg.generic // CHECK: arith.addi %0 = tosa.add %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.subi %1 = tosa.sub %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.muli %shift1 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %2 = tosa.mul %arg0, %arg0, %shift1 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.constant 2 // CHECK: apply_scale %shift2 = "tosa.const"() <{values = dense<2> : tensor<1xi8>}> : () -> tensor<1xi8> %3 = tosa.mul %arg0, %arg0, %shift2: (tensor<1xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.divsi %4 = tosa.intdiv %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: ^bb0(%[[ARG1:.*]]: i32, %[[ARG2:.*]]: i32): // CHECK: [[ZERO:%.+]] = arith.constant 0 // CHECK: arith.subi [[ZERO]], %[[ARG1]] %in_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32> %out_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32> %5 = tosa.negate %arg0, %in_zp, %out_zp : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: and %6 = tosa.bitwise_and %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: or %7 = tosa.bitwise_or %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.xori %8 = tosa.bitwise_xor %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.shli %9 = tosa.logical_left_shift %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.shrui %10 = tosa.logical_right_shift %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.shrsi %11 = tosa.arithmetic_right_shift %arg0, %arg0 {round = 0 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.constant 1 // CHECK: arith.constant 0 // CHECK: arith.constant true // CHECK: arith.cmpi // CHECK: arith.subi // CHECK: arith.shrsi // CHECK: arith.trunci // CHECK: and // CHECK: and // CHECK: arith.extui // CHECK: arith.addi %12 = tosa.arithmetic_right_shift %arg0, %arg0 {round = 1 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: math.ctlz %13 = tosa.clz %arg0 : (tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.cmpi %14 = tosa.greater %0, %1 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.cmpi %15 = tosa.greater_equal %0, %1 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: select %16 = tosa.select %14, %0, %1 : (tensor<1xi1>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.maxsi %17 = tosa.maximum %0, %1 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK: arith.minsi %18 = tosa.minimum %0, %1 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK-DAG: arith.maxsi // CHECK-DAG: arith.minsi %19 = tosa.clamp %0 {min_val = 1 : i32, max_val = 5 : i32} : (tensor<1xi32>) -> tensor<1xi32> // CHECK: linalg.generic // CHECK-DAG: %[[LB:.*]] = arith.constant 4 : i32 // CHECK-DAG: %[[UB:.*]] = arith.constant 32 : i32 // CHECK-DAG: arith.maxui %[[LB]], // CHECK-DAG: arith.minui %[[UB]], %u0 = tosa.clamp %unsigned {min_val = 4 : ui32, max_val = 32 : ui32} : (tensor<1xui32>) -> tensor<1xui32> // CHECK: linalg.generic // CHECK: arith.trunci %20 = tosa.cast %0 : (tensor<1xi32>) -> tensor<1xi16> // CHECK: linalg.generic // CHECK: arith.extsi %21 = tosa.cast %0 : (tensor<1xi32>) -> tensor<1xi64> // CHECK: linalg.generic // CHECK: arith.constant 0 // CHECK: arith.cmpi %22 = tosa.cast %0 : (tensor<1xi32>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.sitofp %23 = tosa.cast %0 : (tensor<1xi32>) -> tensor<1xf32> // CHECK: linalg.generic // CHECK: arith.constant 0 // CHECK: arith.subi // CHECK: arith.maxsi %24 = tosa.abs %arg0 : (tensor<1xi32>) -> tensor<1xi32> return } // ----- // CHECK-LABEL: @test_simple_ui8 func.func @test_simple_ui8(%arg0: tensor<1xi8>) -> () { // CHECK: linalg.generic // CHECK: sitofp %0 = tosa.cast %arg0 : (tensor<1xi8>) -> tensor<1xf32> return } // ----- // CHECK-LABEL: @test_i8 func.func @test_i8(%arg0: tensor<1xi8>) -> () { // CHECK: linalg.generic // CHECK: ^bb0(%[[ARG1:.+]]: i8, // CHECK-DAG: %[[C127:.+]] = arith.constant -127 // CHECK-DAG: %[[C126:.+]] = arith.constant 126 // CHECK-DAG: %[[LOWER:.+]] = arith.maxsi %[[C127]], %[[ARG1]] // CHECK-DAG: %[[CLAMPED:.+]] = arith.minsi %[[C126]], %[[LOWER]] %0 = tosa.clamp %arg0 {min_val = -127 : i8, max_val = 126 : i8} : (tensor<1xi8>) -> tensor<1xi8> return } // ----- // CHECK-LABEL: @test_i64 func.func @test_i64(%arg0: tensor<1xi64>) -> () { // CHECK: linalg.generic // CHECK: ^bb0(%[[ARG1:.+]]: i64, // CHECK-DAG: %[[C127:.+]] = arith.constant -9223372036854775808 // CHECK-DAG: %[[C126:.+]] = arith.constant 9223372036854775807 // CHECK-DAG: %[[LOWER:.+]] = arith.maxsi %[[C127]], %[[ARG1]] // CHECK-DAG: %[[CLAMPED:.+]] = arith.minsi %[[C126]], %[[LOWER]] %0 = tosa.clamp %arg0 {min_val = -9223372036854775808 : i64, max_val = 9223372036854775807 : i64} : (tensor<1xi64>) -> tensor<1xi64> return } // ----- // CHECK-LABEL: @test_clamp_f16 func.func @test_clamp_f16(%arg0: tensor<1xf16>) -> () { // CHECK: linalg.generic // CHECK: ^bb0(%[[ARG1:.+]]: f16, // CHECK-DAG: %[[C0:.+]] = arith.constant 0.0 // CHECK-DAG: %[[C6:.+]] = arith.constant 6.0 // CHECK-DAG: %[[MIN:.+]] = arith.minimumf %[[ARG1]], %[[C6]] // CHECK-DAG: %[[MAX:.+]] = arith.maximumf %[[MIN]], %[[C0]] %0 = tosa.clamp %arg0 {min_val = 0.0 : f16, max_val = 6.0 : f16} : (tensor<1xf16>) -> tensor<1xf16> return } // ----- // CHECK-LABEL: @test_bool func.func @test_bool(%arg0: tensor<1xi1>, %arg1: tensor<1xi1>) -> () { // CHECK: linalg.generic // CHECK: and %0 = tosa.logical_and %arg0, %arg1 : (tensor<1xi1>, tensor<1xi1>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: or %1 = tosa.logical_or %arg0, %arg1 : (tensor<1xi1>, tensor<1xi1>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.xori %2 = tosa.logical_xor %arg0, %arg1 : (tensor<1xi1>, tensor<1xi1>) -> tensor<1xi1> // CHECK: linalg.generic // CHECK: arith.constant true // CHECK: arith.xori %3 = tosa.logical_not %arg0 : (tensor<1xi1>) -> tensor<1xi1> return } // ----- // CHECK-LABEL: @test_negate_quantized func.func @test_negate_quantized(%arg0: tensor<1xi8>) -> () { // CHECK: linalg.generic // CHECK: ^bb0(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8 // CHECK: [[CNST:%.+]] = arith.constant 7 // CHECK: [[EXT:%.+]] = arith.extsi %[[BBARG0]] : i8 to i16 // CHECK: [[SUB:%.+]] = arith.subi [[CNST]], [[EXT]] // CHECK: [[MIN:%.+]] = arith.constant -128 // CHECK: [[MAX:%.+]] = arith.constant 127 // CHECK: [[LBOUND:%.+]] = arith.maxsi [[MIN]], [[SUB]] // CHECK: [[UBOUND:%.+]] = arith.minsi [[MAX]], [[LBOUND]] // CHECK: [[TRUNC:%.+]] = arith.trunci [[UBOUND]] // CHECK: linalg.yield [[TRUNC]] %in_zp0 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %out_zp0 = "tosa.const"() <{values = dense<7> : tensor<1xi8>}> : () -> tensor<1xi8> %0 = tosa.negate %arg0, %in_zp0, %out_zp0 : (tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1xi8> // CHECK: linalg.generic // CHECK: ^bb0(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8 // CHECK: [[C_128:%.+]] = arith.constant -128 // CHECK: [[EXT:%.+]] = arith.extsi %[[BBARG0]] : i8 to i16 // CHECK: [[SUB:%.+]] = arith.subi [[C_128]], [[EXT]] // CHECK: [[MIN:%.+]] = arith.constant -128 // CHECK: [[MAX:%.+]] = arith.constant 127 // CHECK: [[LBOUND:%.+]] = arith.maxsi [[MIN]], [[SUB]] // CHECK: [[UBOUND:%.+]] = arith.minsi [[MAX]], [[LBOUND]] // CHECK: [[TRUNC:%.+]] = arith.trunci [[UBOUND]] // CHECK: linalg.yield [[TRUNC]] %in_zp3 = "tosa.const"() <{values = dense<-128> : tensor<1xi8>}> : () -> tensor<1xi8> %out_zp3 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %3 = tosa.negate %arg0, %in_zp3, %out_zp3 : (tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1xi8> // CHECK: linalg.generic // CHECK: ^bb0(%[[BBARG0:.+]]: i8, // CHECK: [[ZERO:%.+]] = arith.constant 0 // CHECK: [[SUB:%.+]] = arith.subi [[ZERO]], // CHECK: linalg.yield [[SUB]] %in_zp4 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %out_zp4 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %4 = tosa.negate %arg0, %in_zp4, %out_zp4 : (tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1xi8> return } // ----- // CHECK-LABEL: @test_identity // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: tensor<1xf32>, // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: tensor<1xi32> func.func @test_identity(%arg0: tensor<1xf32>, %arg1: tensor<1xi32>) -> (tensor<1xf32>, tensor<1xi32>) { %0 = tosa.identity %arg0 : (tensor<1xf32>) -> tensor<1xf32> %1 = tosa.identity %arg1 : (tensor<1xi32>) -> tensor<1xi32> // CHECK: return %[[ARG0]], %[[ARG1]] return %0, %1 : tensor<1xf32>, tensor<1xi32> } // ----- // CHECK-LABEL: @reduce_float // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xf32> func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () { // CHECK: [[INIT:%.+]] = tensor.empty() : tensor<4xf32> // CHECK: [[CST0:%.+]] = arith.constant 0.0 // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]] // CHECK: [[REDUCE:%.+]] = linalg.reduce ins([[ARG0]] : tensor<5x4xf32>) outs([[FILL]] : tensor<4xf32>) dimensions = [0] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: [[RES:%.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield [[RES]] : f32 // CHECK: } // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xf32> into tensor<1x4xf32> %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32> // CHECK: [[INIT:%.+]] = tensor.empty() : tensor<5xf32> // CHECK: [[CST0:%.+]] = arith.constant 0.0 // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]] // CHECK: [[REDUCE:%.+]] = linalg.reduce ins([[ARG0]] : tensor<5x4xf32>) outs([[FILL]] : tensor<5xf32>) dimensions = [1] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: [[RES:%.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield [[RES]] : f32 // CHECK: } // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xf32> into tensor<5x1xf32> %1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xf32>) -> tensor<5x1xf32> // CHECK: arith.constant 1.0 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.mulf %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32> // CHECK: arith.constant 3.40282347E+38 : f32 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.minimumf %3 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32> // CHECK: arith.constant -3.40282347E+38 : f32 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.maximumf %4 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32> return } // ----- // CHECK-LABEL: @reduce_float_dyn // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: tensor func.func @reduce_float_dyn(%arg0: tensor) -> () { // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) : tensor // CHECK: %[[CST0:.+]] = arith.constant 0.0 // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[CST0]]{{.*}}outs(%[[INIT]] // CHECK: %[[REDUCE:.+]] = linalg.reduce ins(%[[ARG0]] : tensor) outs(%[[FILL]] : tensor) dimensions = [1] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: %[[RES:.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield %[[RES]] : f32 // CHECK: } // CHECK: %[[C0_0:.+]] = arith.constant 0 : index // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor // CHECK: %[[C1:.+]] = arith.constant 1 : index // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [%[[DIM_1]], 1, 4] : tensor into tensor %0 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor) -> tensor return } // ----- // CHECK-LABEL: @reduce_float_dyn_rank_1 // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]: tensor func.func @reduce_float_dyn_rank_1(%arg0: tensor) -> () { // CHECK-DAG: %[[INIT:.+]] = tensor.empty() : tensor // CHECK-DAG: %[[CST0:.+]] = arith.constant 0.0 // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[CST0]]{{.*}}outs(%[[INIT]] // CHECK: %[[REDUCE:.+]] = linalg.reduce ins(%[[ARG0]] : tensor) outs(%[[FILL]] : tensor) dimensions = [0] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: %[[RES:.+]] = arith.addf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield %[[RES]] : f32 // CHECK: } // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}] output_shape [1] : tensor into tensor<1xf32> %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor) -> tensor<1xf32> return } // ----- // CHECK-LABEL: @reduce_float_dyn_nonzero_batch // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @reduce_float_dyn_nonzero_batch(%arg0: tensor<5x?x4xf32>) -> () { // CHECK: %[[C1:.+]] = arith.constant 1 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[C1]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) : tensor<5x?xf32> // CHECK: %[[CST1:.+]] = arith.constant 1.0 // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[CST1]]{{.*}}outs(%[[INIT]] // CHECK: %[[REDUCE:.+]] = linalg.reduce ins(%[[ARG0]] : tensor<5x?x4xf32>) outs(%[[FILL]] : tensor<5x?xf32>) dimensions = [2] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: %[[RES:.+]] = arith.mulf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield %[[RES]] : f32 // CHECK: } // CHECK: %[[C1_0:.+]] = arith.constant 1 : index // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C1_0]] : tensor<5x?xf32> // CHECK: %[[C1_2:.+]] = arith.constant 1 : index // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [5, %[[DIM_1]], 1] : tensor<5x?xf32> into tensor<5x?x1xf32> %0 = tosa.reduce_product %arg0 {axis = 2 : i32} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32> return } // ----- // CHECK-LABEL: @reduce_float_dyn_multiple // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @reduce_float_dyn_multiple(%arg0: tensor) -> () { // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[CMIN:.+]] = arith.constant -3.40282347E+38 // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[CMIN]]{{.*}}outs(%[[INIT]] // CHECK: %[[REDUCE:.+]] = linalg.reduce ins(%[[ARG0]] : tensor) outs(%[[FILL]] : tensor) dimensions = [1] // CHECK: (%[[ARG1:.*]]: f32, %[[ARG2:.*]]: f32) { // CHECK: %[[MAX:.+]] = arith.maximumf %[[ARG1]], %[[ARG2]] : f32 // CHECK: linalg.yield %[[MAX]] : f32 // CHECK: } // CHECK: %[[C0_0:.+]] = arith.constant 0 : index // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C0_0]] : tensor // CHECK: %[[C1_2:.+]] = arith.constant 1 : index // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0, 1]] output_shape [%[[DIM_1]], 1] : tensor into tensor %0 = tosa.reduce_max %arg0 {axis = 1 : i32} : (tensor) -> tensor return } // ----- // CHECK-LABEL: @reduce_int // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xi32> func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () { // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[CST0:%.+]] = arith.constant 0 // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]] // CHECK: [[REDUCE:%.+]] = linalg.reduce ins([[ARG0]] : tensor<5x4xi32>) outs([[FILL]] : tensor<4xi32>) dimensions = [0] // CHECK: (%[[ARG1:.*]]: i32, %[[ARG2:.*]]: i32) { // CHECK: [[RES:%.+]] = arith.addi %[[ARG1]], %[[ARG2]] : i32 // CHECK: linalg.yield [[RES]] : i32 // CHECK: } // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi32> into tensor<1x4xi32> %0 = tosa.reduce_sum %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32> // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[CST0:%.+]] = arith.constant 0 // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]] // CHECK: [[REDUCE:%.+]] = linalg.reduce ins([[ARG0]] : tensor<5x4xi32>) outs([[FILL]] : tensor<5xi32>) dimensions = [1] // CHECK: (%[[ARG1:.*]]: i32, %[[ARG2:.*]]: i32) { // CHECK: [[RES:%.+]] = arith.addi %[[ARG1]], %[[ARG2]] : i32 // CHECK: linalg.yield [[RES]] : i32 // CHECK: } // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [5, 1] : tensor<5xi32> into tensor<5x1xi32> %1 = tosa.reduce_sum %arg0 {axis = 1 : i32} : (tensor<5x4xi32>) -> tensor<5x1xi32> // CHECK: arith.constant 1 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.muli %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32> // CHECK: arith.constant 2147483647 : i32 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.minsi %3 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32> // CHECK: arith.constant -2147483648 : i32 // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: arith.maxsi %4 = tosa.reduce_max %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32> return } // ----- // CHECK-LABEL: @reduce_bool // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xi1> func.func @reduce_bool(%arg0: tensor<5x4xi1>) -> () { // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[CST0:%.+]] = arith.constant true // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]] // CHECK: [[REDUCE:%.+]] = linalg.reduce ins([[ARG0]] : tensor<5x4xi1>) outs([[FILL]] : tensor<4xi1>) dimensions = [0] // CHECK: (%[[ARG1:[0-9a-zA-Z_]+]]: i1, %[[ARG2:[0-9a-zA-Z_]+]]: i1) { // CHECK: [[RES:%.+]] = arith.andi %[[ARG1]], %[[ARG2]] : i1 // CHECK: linalg.yield [[RES]] : i1 // CHECK: } // CHECK: tensor.expand_shape [[REDUCE]] {{\[}}[0, 1]] output_shape [1, 4] : tensor<4xi1> into tensor<1x4xi1> %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<5x4xi1>) -> tensor<1x4xi1> // CHECK: arith.constant false // CHECK: linalg.fill // CHECK: linalg.reduce // CHECK: or %1 = tosa.reduce_any %arg0 {axis = 0 : i32} : (tensor<5x4xi1>) -> tensor<1x4xi1> return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @rescale_i8 // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_i8(%arg0 : tensor<2xi8>) -> () { // CHECK: [[C0:%.+]] = arith.constant 19689 // CHECK: [[C1:%.+]] = arith.constant 15 // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): // CHECK: [[C17:%.+]] = arith.constant 17 // CHECK: [[C22:%.+]] = arith.constant 22 // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = "SINGLE_ROUND"} // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 // CHECK-DAG: [[LOWER:%.+]] = arith.maxsi [[CMIN]], [[SCALED_ZEROED]] // CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]] // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] // CHECK-DAG: linalg.yield [[TRUNC]] %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16> %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8> %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> // CHECK: return return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @rescale_i8_unsigned_output // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_i8_unsigned_output(%arg0 : tensor<2xi8>) -> () { // CHECK: [[C0:%.+]] = arith.constant 19689 // CHECK: [[C1:%.+]] = arith.constant 15 // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): // CHECK: [[C17:%.+]] = arith.constant 17 // CHECK: [[C22:%.+]] = arith.constant 22 // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = "SINGLE_ROUND"} // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] // CHECK-DAG: [[CMIN:%.+]] = arith.constant 0 // CHECK-DAG: [[CMAX:%.+]] = arith.constant 255 // CHECK-DAG: [[LOWER:%.+]] = arith.maxsi [[CMIN]], [[SCALED_ZEROED]] // CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]] // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] // CHECK: linalg.yield [[TRUNC]] %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16> %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8> %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> // CHECK: return return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK-LABEL: @rescale_i8_dyn_batch // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_i8_dyn_batch(%arg0 : tensor) -> () { %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16>} : () -> tensor<1xi16> %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8> // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor) outs(%[[INIT]] : tensor) %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor) outs(%[[INIT]] : tensor) %1 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor return } // ----- // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> // CHECK-LABEL: @rescale_dyn // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_dyn(%arg0 : tensor<1x?x?x32xi32>) -> () { %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8> // CHECK: %[[C1:.+]] = arith.constant 1 // CHECK: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]] // CHECK: %[[C2:.+]] = arith.constant 2 // CHECK: %[[DIM2:.+]] = tensor.dim %[[ARG0]], %[[C2]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM2]]) // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<1x?x?x32xi32>) outs(%[[INIT]] : tensor<1x?x?x32xi8>) %multiplier = "tosa.const"() {values = dense<1376784203> : tensor<1xi32> } : () -> tensor<1xi32> %shift = "tosa.const"() {values = dense<38> : tensor<1xi8> } : () -> tensor<1xi8> %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = "DOUBLE_ROUND", input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x?x?x32xi32>, tensor<1xi32>, tensor<1xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x?x?x32xi8> return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @rescale_i8_unsigned_input // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_i8_unsigned_input(%arg0 : tensor<2xi8>) -> () { // CHECK: [[C0:%.+]] = arith.constant 19689 // CHECK: [[C1:%.+]] = arith.constant 15 // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): // CHECK: [[C17:%.+]] = arith.constant 17 // CHECK: [[C22:%.+]] = arith.constant 22 // CHECK-DAG: [[IN32:%.+]] = arith.extui [[IN]] // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[C0]], [[C1]] {rounding_mode = "SINGLE_ROUND"} // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 // CHECK-DAG: [[LOWER:%.+]] = arith.maxsi [[CMIN]], [[SCALED_ZEROED]] // CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]] // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] // CHECK: linalg.yield [[TRUNC]] %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16> %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<17> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<22> : tensor<1xi8>} : () -> tensor<1xi8> %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @rescale_per_channel // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) { // CHECK: [[MULTIPLIERS:%.+]] = arith.constant dense<[42, 43, 0]> // CHECK: [[SHIFTS:%.+]] = arith.constant dense<[14, 15, 0]> // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]], [[MULTIPLIERS]], [[SHIFTS]] : tensor<3xi8>, tensor<3xi32>, tensor<3xi8>) outs([[INIT]] : tensor<3xi8>) // CHECK: ^bb0([[IN:%.+]]: i8, [[MULTIPLIER:%.+]]: i32, [[SHIFT:%.+]]: i8, [[UNUSED:%.+]]: i8): // CHECK: [[C243:%.+]] = arith.constant 43 // CHECK: [[C252:%.+]] = arith.constant 52 // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C243]] // CHECK-DAG: [[SCALED:%.+]] = tosa.apply_scale [[IN_ZEROED]], [[MULTIPLIER]], [[SHIFT]] {rounding_mode = "SINGLE_ROUND"} // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C252]] // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 // CHECK-DAG: [[LOWER:%.+]] = arith.maxsi [[CMIN]], [[SCALED_ZEROED]] // CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]] // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] // CHECK-DAG: linalg.yield [[TRUNC]] %multiplier = "tosa.const"() {values = dense<[42, 43, 44]> : tensor<3xi16>} : () -> tensor<3xi16> %shift = "tosa.const"() {values = dense<[14, 15, 64]> : tensor<3xi8>} : () -> tensor<3xi8> %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8> %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = false, rounding_mode = "SINGLE_ROUND", per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<3xi8>, tensor<3xi16>, tensor<3xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<3xi8> // CHECK: return [[GENERIC]] return %0 : tensor<3xi8> } // ----- // CHECK-LABEL: @rescaleDoubleRound func.func @rescaleDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32>} : () -> tensor<1xi32> %shift = "tosa.const"() {values = dense<33> : tensor<1xi8>} : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8> // CHECK: linalg.generic // CHECK: tosa.apply_scale // CHECK-SAME: {rounding_mode = "DOUBLE_ROUND"} %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = "DOUBLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> return %0 : tensor<2xi8> } // ----- // CHECK-LABEL: @rescaleUnnecessaryDoubleRound func.func @rescaleUnnecessaryDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32>} : () -> tensor<1xi32> %shift = "tosa.const"() {values = dense<15> : tensor<1xi8>} : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<43> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<52> : tensor<1xi8>} : () -> tensor<1xi8> // CHECK: linalg.generic // CHECK: tosa.apply_scale // CHECK-SAME: {rounding_mode = "SINGLE_ROUND"} %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {scale32 = true, rounding_mode = "DOUBLE_ROUND", per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> return %0 : tensor<2xi8> } // ----- func.func @unsupportedRescaleInexactRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32> %shift = "tosa.const"() {values = dense<33> : tensor<1xi8> } : () -> tensor<1xi8> %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8> %output_zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8> // expected-error@+1 {{failed to legalize operation 'tosa.rescale'}} %0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {input_zp = 243 : i32, output_zp = 252 : i32, scale32 = true, rounding_mode = "INEXACT_ROUND", per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<2xi8> return %0 : tensor<2xi8> } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK-LABEL: @reverse // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @reverse(%arg0: tensor<5x4xi32>) -> () { // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[RDIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]]], iterator_types = ["parallel", "parallel"]} outs(%[[INIT]] : tensor<5x4xi32>) // CHECK-DAG: %[[I0:.+]] = linalg.index 0 // CHECK-DAG: %[[I1:.+]] = linalg.index 1 // CHECK-DAG: %[[SUB1:.+]] = arith.constant 1 // CHECK-DAG: %[[RDIM_MINUS_C1:.+]] = arith.subi %[[RDIM]], %[[SUB1]] // CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I0]] // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[READ_DIM]], %[[I1]]] : tensor<5x4xi32> // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<5x4xi32> // CHECK: %[[C1:.+]] = arith.constant 1 // CHECK: %[[RDIM:.+]] = tensor.dim %[[ARG0]], %[[C1]] // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]]], iterator_types = ["parallel", "parallel"]} outs(%[[INIT]] : tensor<5x4xi32>) // CHECK-DAG: %[[I0:.+]] = linalg.index 0 // CHECK-DAG: %[[I1:.+]] = linalg.index 1 // CHECK-DAG: %[[SUB1:.+]] = arith.constant 1 // CHECK-DAG: %[[RDIM_MINUS_C1:.+]] = arith.subi %[[RDIM]], %[[SUB1]] // CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I1]] // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[I0]], %[[READ_DIM]]] : tensor<5x4xi32> // CHECK: linalg.yield %[[EXTRACT]] %1 = tosa.reverse %arg0 {axis = 1 : i32} : (tensor<5x4xi32>) -> tensor<5x4xi32> return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: @reverse_dyn // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @reverse_dyn(%arg0: tensor) -> () { // CHECK: %[[C0_1:.+]] = arith.constant 0 // CHECK: %[[D0_1:.+]] = tensor.dim %[[ARG0]], %[[C0_1]] // CHECK: %[[C0_2:.+]] = arith.constant 0 // CHECK: %[[D0_2:.+]] = tensor.dim %[[ARG0]], %[[C0_2]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[D0_1]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]]], iterator_types = ["parallel"]} outs(%[[INIT]] : tensor) // CHECK-DAG: %[[I0:.+]] = linalg.index 0 // CHECK-DAG: %[[SUB1:.+]] = arith.constant 1 // CHECK-DAG: %[[RDIM_MINUS_C1:.+]] = arith.subi %[[D0_2]], %[[SUB1]] // CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I0]] // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[READ_DIM]]] : tensor // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.reverse %arg0 {axis = 0 : i32} : (tensor) -> tensor return } // ----- // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d3)> // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> // CHECK-LABEL: @tile // CHECK-SAME: %[[ARG0:.+]]: tensor<2x3xi8> func.func @tile(%arg0 : tensor<2x3xi8>) -> () { // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<2x2x1x3xi8>) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8 // CHECK: linalg.yield %[[ARG1]] : i8 // CHECK: [[CONST3:%.+]] = tosa.const_shape {values = dense<[4, 3]> : tensor<2xindex>} : () -> !tosa.shape<2> // CHECK: tosa.reshape [[GENERIC]], [[CONST3]] %cst21 = tosa.const_shape { values = dense<[2, 1]> : tensor<2xindex> } : () -> !tosa.shape<2> %0 = tosa.tile %arg0, %cst21: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<4x3xi8> // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<1x2x2x3xi8>) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8 // CHECK: linalg.yield %[[ARG1]] : i8 // CHECK: [[CONST8:%.+]] = tosa.const_shape {values = dense<[2, 6]> : tensor<2xindex>} : () -> !tosa.shape<2> // tosa.reshape [[GENERIC]], [[CONST8]] %cst12 = tosa.const_shape { values = dense<[1, 2]> : tensor<2xindex> } : () -> !tosa.shape<2> %1 = tosa.tile %arg0, %cst12: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<2x6xi8> // CHECK: [[INIT:%.+]] = tensor.empty() // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs([[INIT]] : tensor<5x2x7x3xi8>) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i8 // CHECK: linalg.yield %[[ARG1]] : i8 %cst57 = tosa.const_shape { values = dense<[5, 7]> : tensor<2xindex> } : () -> !tosa.shape<2> // CHECK: [[CONST13:%.+]] = tosa.const_shape {values = dense<[10, 21]> : tensor<2xindex>} : () -> !tosa.shape<2> // CHECK: tosa.reshape [[GENERIC]], [[CONST13]] %2 = tosa.tile %arg0, %cst57: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<10x21xi8> return } // ----- // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d3)> // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> // CHECK-LABEL: @tile_dyn_input // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @tile_dyn_input(%arg0 : tensor) -> () { // CHECK: %[[CST0:.+]] = arith.constant 0 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[CST0]] : tensor // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor) outs(%[[INIT]] : tensor<2x?x1x3xi8>) // CHECK: ^bb0(%[[ARG1:.+]]: i8, // CHECK: linalg.yield %[[ARG1]] : i8 // CHECK: %[[CONST3:.+]] = tosa.const_shape {values = dense<[-1, 3]> : tensor<2xindex>} : () -> !tosa.shape<2> // CHECK: tosa.reshape %[[GENERIC]], %[[CONST3]] %cst21 = tosa.const_shape { values = dense<[2, 1]> : tensor<2xindex> } : () -> !tosa.shape<2> %0 = tosa.tile %arg0, %cst21: (tensor, !tosa.shape<2>) -> tensor return } // ----- // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d3)> // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> // CHECK-LABEL: @tile_dyn_multiples // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: func.func @tile_dyn_multiples(%arg0 : tensor<2x3xi8>) -> () { // CHECK: %[[CST1:.+]] = arith.constant 1 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[CST1]] : tensor<2x3xi8> // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<2x3xi8>) outs(%[[INIT]] : tensor<2x2x?x3xi8>) // CHECK: ^bb0(%[[ARG1:.+]]: i8, // CHECK: linalg.yield %[[ARG1]] : i8 // CHECK: %[[CONST2:.+]] = tosa.const_shape {values = dense<[2, -1]> : tensor<2xindex>} : () -> !tosa.shape<2> // CHECK: tosa.reshape %[[GENERIC]], %[[CONST2]] %cst = tosa.const_shape { values = dense<[2, -1]> : tensor<2xindex> } : () -> !tosa.shape<2> %0 = tosa.tile %arg0, %cst: (tensor<2x3xi8>, !tosa.shape<2>) -> tensor<2x?xi8> return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> // CHECK: #[[$MAP3:.*]] = affine_map<(d0) -> (d0)> // CHECK: #[[$MAP4:.*]] = affine_map<(d0) -> ()> func.func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () { // CHECK: [[IDX_INIT:%.+]] = tensor.empty() // CHECK: [[IDX_MIN:%.+]] = arith.constant 0 : i32 // CHECK: [[IDX_FILL:%.+]] = linalg.fill ins([[IDX_MIN]]{{.*}}outs([[IDX_INIT]] // CHECK: [[VAL_INIT:%.+]] = tensor.empty() // CHECK: [[VAL_MIN:%.+]] = arith.constant -2147483648 // CHECK: [[VAL_FILL:%.+]] = linalg.fill ins([[VAL_MIN]]{{.*}}outs([[VAL_INIT]] // CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins(%[[ARG0]] : tensor<3x2xi32>) outs([[IDX_FILL]], [[VAL_FILL]] : tensor<2xi32>, tensor<2xi32>) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i32, %[[ARG2:[0-9a-zA-Z_]+]]: i32, %[[ARG3:[0-9a-zA-Z_]+]]: i32 // CHECK: [[IDX:%.+]] = linalg.index 0 // CHECK: [[CAST:%.+]] = arith.index_cast [[IDX]] // CHECK: [[CMP:%.+]] = arith.cmpi sgt, %[[ARG1]], %[[ARG3]] // CHECK: [[SELECT_VAL:%.+]] = arith.select [[CMP]], %[[ARG1]], %[[ARG3]] // CHECK: [[SELECT_IDX:%.+]] = arith.select [[CMP]], [[CAST]], %[[ARG2]] // CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]] %0 = tosa.argmax %arg0 { axis = 0 : i32} : (tensor<3x2xi32>) -> tensor<2xi32> // CHECK: [[IDX_INIT:%.+]] = tensor.empty() // CHECK: [[IDX_MIN:%.+]] = arith.constant 0 : i32 // CHECK: [[IDX_FILL:%.+]] = linalg.fill ins([[IDX_MIN]]{{.*}}outs([[IDX_INIT]] // CHECK: [[VAL_INIT:%.+]] = tensor.empty() // CHECK: [[VAL_MIN:%.+]] = arith.constant -2147483648 // CHECK: [[VAL_FILL:%.+]] = linalg.fill ins([[VAL_MIN]]{{.*}}outs([[VAL_INIT]] // CHECK: linalg.generic {indexing_maps = [#map, #map2, #map2], iterator_types = ["parallel", "reduction"]} ins(%[[ARG0]] : tensor<3x2xi32>) outs([[IDX_FILL]], [[VAL_FILL]] : tensor<3xi32>, tensor<3xi32>) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i32, %[[ARG2:[0-9a-zA-Z_]+]]: i32, %[[ARG3:[0-9a-zA-Z_]+]]: i32 // CHECK: [[IDX:%.+]] = linalg.index 1 // CHECK: [[CAST:%.+]] = arith.index_cast [[IDX]] // CHECK: [[CMP:%.+]] = arith.cmpi sgt, %[[ARG1]], %[[ARG3]] // CHECK: [[SELECT_VAL:%.+]] = arith.select [[CMP]], %[[ARG1]], %[[ARG3]] // CHECK: [[SELECT_IDX:%.+]] = arith.select [[CMP]], [[CAST]], %[[ARG2]] // CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]] %1 = tosa.argmax %arg0 { axis = 1 : i32} : (tensor<3x2xi32>) -> tensor<3xi32> // CHECK: arith.constant -3.40282347E+38 : f32 // CHECK: linalg.index // CHECK: arith.index_cast // CHECK: arith.cmpf ugt // CHECK: arith.cmpf ord // CHECK: andi // CHECK: select // CHECK: select // CHECK: linalg.yield %2 = tosa.argmax %arg1 { axis = 0 : i32} : (tensor<6xf32>) -> tensor return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> func.func @argmax_dyn_non_axis(%arg0 : tensor<3x?xi32>) -> () { // CHECK: %[[CST1:.+]] = arith.constant 1 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[CST1]] // CHECK: %[[IDX_INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[IDX_MIN:.+]] = arith.constant 0 : i32 // CHECK: %[[IDX_FILL:.+]] = linalg.fill ins(%[[IDX_MIN]]{{.*}}outs(%[[IDX_INIT]] // CHECK: %[[VAL_INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[VAL_MIN:.+]] = arith.constant -2147483648 // CHECK: %[[VAL_FILL:.+]] = linalg.fill ins(%[[VAL_MIN]]{{.*}}outs(%[[VAL_INIT]] // CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins(%[[ARG0]] : tensor<3x?xi32>) outs(%[[IDX_FILL]], %[[VAL_FILL]] : tensor, tensor) // CHECK: ^bb0(%[[ARG1:[0-9a-zA-Z_]+]]: i32, %[[ARG2:[0-9a-zA-Z_]+]]: i32, %[[ARG3:[0-9a-zA-Z_]+]]: i32 // CHECK: %[[IDX:.+]] = linalg.index 0 // CHECK: %[[CAST:.+]] = arith.index_cast %[[IDX]] // CHECK: %[[CMP:.+]] = arith.cmpi sgt, %[[ARG1]], %[[ARG3]] // CHECK: %[[SELECT_VAL:.+]] = arith.select %[[CMP]], %[[ARG1]], %[[ARG3]] // CHECK: %[[SELECT_IDX:.+]] = arith.select %[[CMP]], %[[CAST]], %[[ARG2]] // CHECK: linalg.yield %[[SELECT_IDX]], %[[SELECT_VAL]] %0 = tosa.argmax %arg0 { axis = 0 : i32} : (tensor<3x?xi32>) -> tensor return } // ----- // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0)> func.func @argmax_dyn_axis(%arg0 : tensor<3x?xi32>) -> () { // CHECK: %[[IDX_INIT:.+]] = tensor.empty() // CHECK: %[[IDX_MIN:.+]] = arith.constant 0 : i32 // CHECK: %[[IDX_FILL:.+]] = linalg.fill ins(%[[IDX_MIN]]{{.*}}outs(%[[IDX_INIT]] // CHECK: %[[VAL_INIT:.+]] = tensor.empty() // CHECK: %[[VAL_MIN:.+]] = arith.constant -2147483648 // CHECK: %[[VAL_FILL:.+]] = linalg.fill ins(%[[VAL_MIN]]{{.*}}outs(%[[VAL_INIT]] // CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "reduction"]} ins(%[[ARG0]] : tensor<3x?xi32>) outs(%[[IDX_FILL]], %[[VAL_FILL]] : tensor<3xi32>, tensor<3xi32>) // CHECK: %[[IDX:.+]] = linalg.index 1 // CHECK: %[[CAST:.+]] = arith.index_cast %[[IDX]] // CHECK: %[[CMP:.+]] = arith.cmpi sgt, %[[ARG1]], %[[ARG3]] // CHECK: %[[SELECT_VAL:.+]] = arith.select %[[CMP]], %[[ARG1]], %[[ARG3]] // CHECK: %[[SELECT_IDX:.+]] = arith.select %[[CMP]], %[[CAST]], %[[ARG2]] // CHECK: linalg.yield %[[SELECT_IDX]], %[[SELECT_VAL]] %0 = tosa.argmax %arg0 { axis = 1 : i32} : (tensor<3x?xi32>) -> tensor<3xi32> return } // ----- // CHECK-LABEL: @gather_float // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]] // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]] func.func @gather_float(%arg0: tensor<2x3x2xf32>, %arg1: tensor<2x3xi32>) -> () { // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[ARG1]] : tensor<2x3xi32>) outs(%[[INIT]] : tensor<2x3x2xf32>) // CHECK: ^bb0(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: f32) // CHECK: %[[IDX0:.+]] = linalg.index 0 // CHECK: %[[CAST:.+]] = arith.index_cast %[[BBARG0]] // CHECK: %[[IDX2:.+]] = linalg.index 2 // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG0]][%[[IDX0]], %[[CAST]], %[[IDX2]]] : tensor<2x3x2xf32> // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.gather %arg0, %arg1 : (tensor<2x3x2xf32>, tensor<2x3xi32>) -> tensor<2x3x2xf32> return } // ----- // CHECK-LABEL: @gather_float_dyn // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]] // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]] func.func @gather_float_dyn(%arg0: tensor, %arg1: tensor) -> () { // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[ARG1]] : tensor) outs(%[[INIT]] : tensor) // CHECK: ^bb0(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: f32) // CHECK: %[[IDX0:.+]] = linalg.index 0 // CHECK: %[[CAST:.+]] = arith.index_cast %[[BBARG0]] // CHECK: %[[IDX2:.+]] = linalg.index 2 // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG0]][%[[IDX0]], %[[CAST]], %[[IDX2]]] : tensor // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.gather %arg0, %arg1 : (tensor, tensor) -> tensor return } // ----- // CHECK-LABEL: @gather_float_all_dynamic // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]] // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]] func.func @gather_float_all_dynamic(%arg0: tensor, %arg1: tensor) -> () { // CHECK: %[[C0:.+]] = arith.constant 0 // CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK: %[[C1:.+]] = arith.constant 1 // CHECK: %[[INDEX:.+]] = tensor.dim %[[ARG1]], %[[C1]] // CHECK: %[[C2:.+]] = arith.constant 2 // CHECK: %[[CHANNEL:.+]] = tensor.dim %[[ARG0]], %[[C2]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]], %[[INDEX]], %[[CHANNEL]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[ARG1]] : tensor) outs(%[[INIT]] : tensor) // CHECK: ^bb0(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: f32) // CHECK: %[[IDX0:.+]] = linalg.index 0 // CHECK: %[[CAST:.+]] = arith.index_cast %[[BBARG0]] // CHECK: %[[IDX2:.+]] = linalg.index 2 // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG0]][%[[IDX0]], %[[CAST]], %[[IDX2]]] : tensor // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.gather %arg0, %arg1 : (tensor, tensor) -> tensor return } // ----- // CHECK-LABEL: @gather_int // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]] // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]] func.func @gather_int(%arg0: tensor<2x3x2xi32>, %arg1: tensor<2x3xi32>) -> () { // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[ARG1]] : tensor<2x3xi32>) outs(%[[INIT]] : tensor<2x3x2xi32>) // CHECK: ^bb0(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: i32) // CHECK: %[[IDX0:.+]] = linalg.index 0 // CHECK: %[[CAST:.+]] = arith.index_cast %[[BBARG0]] // CHECK: %[[IDX2:.+]] = linalg.index 2 // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG0]][%[[IDX0]], %[[CAST]], %[[IDX2]]] : tensor<2x3x2xi32> // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.gather %arg0, %arg1 : (tensor<2x3x2xi32>, tensor<2x3xi32>) -> tensor<2x3x2xi32> return } // ----- // CHECK-LABEL: @table8 // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @table8(%arg0: tensor<6xi8>, %arg1: tensor<512xi8>) -> () { // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<6xi8>) outs(%[[INIT]] : tensor<6xi8>) // CHECK: ^bb0(%[[ARG_IN:.+]]: i8, %[[ARG_INIT:.+]]: i8) // CHECK: %[[CAST:.+]] = arith.index_cast %[[ARG_IN]] // CHECK: %[[OFFSET:.+]] = arith.constant 128 // CHECK: %[[ADD:.+]] = arith.addi %[[CAST]], %[[OFFSET]] // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG1]][%[[ADD]]] // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.table %arg0, %arg1 : (tensor<6xi8>, tensor<512xi8>) -> tensor<6xi8> return } // ----- // CHECK-LABEL: @table16 // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @table16(%arg0: tensor<6xi16>, %arg1: tensor<513xi16>) -> () { // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<6xi16>) outs(%[[INIT]] : tensor<6xi32>) // CHECK: ^bb0(%[[ARG2:.*]]: i16, %[[ARG3:.*]]: i32) // CHECK: %[[EXT_IN:.+]] = arith.extsi %[[ARG2]] // CHECK: %[[C32768:.+]] = arith.constant 32768 // CHECK: %[[C7:.+]] = arith.constant 7 // CHECK: %[[C1:.+]] = arith.constant 1 // CHECK: %[[C127:.+]] = arith.constant 127 // CHECK: %[[INADD:.+]] = arith.addi %[[EXT_IN]], %[[C32768]] // CHECK: %[[IDX:.+]] = arith.shrui %[[INADD]], %[[C7]] // CHECK: %[[FRACTION:.+]] = arith.andi %[[INADD]], %[[C127]] // CHECK: %[[IDXPLUS1:.+]] = arith.addi %[[IDX]], %[[C1]] // CHECK: %[[IDX_CAST:.+]] = arith.index_cast %[[IDX]] // CHECK: %[[IDXPLUS1_CAST:.+]] = arith.index_cast %[[IDXPLUS1]] // CHECK: %[[BASE:.+]] = tensor.extract %[[ARG1]][%[[IDX_CAST]]] // CHECK: %[[NEXT:.+]] = tensor.extract %[[ARG1]][%[[IDXPLUS1_CAST]]] // CHECK: %[[BASE_EXT:.+]] = arith.extsi %[[BASE]] // CHECK: %[[NEXT_EXT:.+]] = arith.extsi %[[NEXT]] // CHECK: %[[BASE_MUL:.+]] = arith.shli %[[BASE_EXT]], %[[C7]] // CHECK: %[[DIFF:.+]] = arith.subi %[[NEXT_EXT]], %[[BASE_EXT]] // CHECK: %[[DIFF_MUL:.+]] = arith.muli %[[DIFF]], %[[FRACTION]] // CHECK: %[[RESULT:.+]] = arith.addi %[[BASE_MUL]], %[[DIFF_MUL]] // CHECK: linalg.yield %[[RESULT]] %0 = tosa.table %arg0, %arg1 : (tensor<6xi16>, tensor<513xi16>) -> tensor<6xi32> return } // ----- // CHECK-LABEL: @table8_dyn // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @table8_dyn(%arg0: tensor, %arg1: tensor<512xi8>) -> () { // CHECK: %[[CST0:.+]] = arith.constant 0 // CHECK: %[[DYN:.+]] = tensor.dim %[[ARG0]], %[[CST0]] // CHECK: %[[INIT:.+]] = tensor.empty(%[[DYN]]) // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor) outs(%[[INIT]] : tensor) // CHECK: ^bb0(%[[ARG_IN:.+]]: i8, %[[ARG_INIT:.+]]: i8) // CHECK: %[[CAST:.+]] = arith.index_cast %[[ARG_IN]] // CHECK: %[[OFFSET:.+]] = arith.constant 128 // CHECK: %[[ADD:.+]] = arith.addi %[[CAST]], %[[OFFSET]] // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG1]][%[[ADD]]] // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.table %arg0, %arg1 : (tensor, tensor<512xi8>) -> tensor return } // ----- // CHECK-LABEL: @table8_dyn_table // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]: // CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]: func.func @table8_dyn_table(%arg0: tensor<6xi8>, %arg1: tensor) -> () { // CHECK: %[[INIT:.+]] = tensor.empty() // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<6xi8>) outs(%[[INIT]] : tensor<6xi8>) // CHECK: ^bb0(%[[ARG_IN:.+]]: i8, %[[ARG_INIT:.+]]: i8) // CHECK: %[[CAST:.+]] = arith.index_cast %[[ARG_IN]] // CHECK: %[[OFFSET:.+]] = arith.constant 128 // CHECK: %[[ADD:.+]] = arith.addi %[[CAST]], %[[OFFSET]] // CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG1]][%[[ADD]]] // CHECK: linalg.yield %[[EXTRACT]] %0 = tosa.table %arg0, %arg1 : (tensor<6xi8>, tensor) -> tensor<6xi8> return } // ----- // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // CHECK: #[[$ATTR_0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d3, d4)> // CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> // CHECK-LABEL: func.func @test_static_rfft2d( // CHECK-SAME: %[[VAL_0:.*]]: tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>) { // CHECK: %[[VAL_1:.*]] = arith.constant 1 : index // CHECK: %[[VAL_2:.*]] = arith.constant 2 : index // CHECK: %[[VAL_3:.*]] = arith.constant 8 : index // CHECK: %[[VAL_4:.*]] = arith.constant 4 : index // CHECK: %[[VAL_5:.*]] = arith.constant 5 : index // CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<5x4x5xf32> // CHECK: %[[VAL_7:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_8:.*]] = linalg.fill ins(%[[VAL_7]] : f32) outs(%[[VAL_6]] : tensor<5x4x5xf32>) -> tensor<5x4x5xf32> // CHECK: %[[VAL_9:.*]] = tensor.empty() : tensor<5x4x5xf32> // CHECK: %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_11:.*]] = linalg.fill ins(%[[VAL_10]] : f32) outs(%[[VAL_9]] : tensor<5x4x5xf32>) -> tensor<5x4x5xf32> // CHECK: %[[VAL_12:.*]] = arith.constant 1 : index // CHECK: %[[VAL_13:.*]] = arith.constant 4 : index // CHECK: %[[VAL_14:.*]] = arith.constant 2 : index // CHECK: %[[VAL_15:.*]] = arith.constant 8 : index // CHECK: %[[VAL_16:.*]] = arith.constant 6.28318548 : f32 // CHECK: %[[VAL_17:.*]] = arith.index_castui %[[VAL_13]] : index to i32 // CHECK: %[[VAL_18:.*]] = arith.uitofp %[[VAL_17]] : i32 to f32 // CHECK: %[[VAL_19:.*]] = arith.index_castui %[[VAL_15]] : index to i32 // CHECK: %[[VAL_20:.*]] = arith.uitofp %[[VAL_19]] : i32 to f32 // CHECK: %[[VAL_21:.*]]:2 = linalg.generic {indexing_maps = [#[[$ATTR_0]], #[[$ATTR_1]], #[[$ATTR_1]]], iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]} ins(%[[VAL_0]] : tensor<5x4x8xf32>) outs(%[[VAL_8]], %[[VAL_11]] : tensor<5x4x5xf32>, tensor<5x4x5xf32>) { // CHECK: ^bb0(%[[VAL_22:.*]]: f32, %[[VAL_23:.*]]: f32, %[[VAL_24:.*]]: f32): // CHECK: %[[VAL_25:.*]] = linalg.index 1 : index // CHECK: %[[VAL_26:.*]] = linalg.index 2 : index // CHECK: %[[VAL_27:.*]] = linalg.index 3 : index // CHECK: %[[VAL_28:.*]] = linalg.index 4 : index // CHECK: %[[VAL_29:.*]] = index.mul %[[VAL_27]], %[[VAL_25]] // CHECK: %[[VAL_30:.*]] = index.mul %[[VAL_28]], %[[VAL_26]] // CHECK: %[[VAL_31:.*]] = index.remu %[[VAL_29]], %[[VAL_13]] // CHECK: %[[VAL_32:.*]] = index.remu %[[VAL_30]], %[[VAL_15]] // CHECK: %[[VAL_33:.*]] = arith.index_castui %[[VAL_31]] : index to i32 // CHECK: %[[VAL_34:.*]] = arith.uitofp %[[VAL_33]] : i32 to f32 // CHECK: %[[VAL_35:.*]] = arith.index_castui %[[VAL_32]] : index to i32 // CHECK: %[[VAL_36:.*]] = arith.uitofp %[[VAL_35]] : i32 to f32 // CHECK: %[[VAL_37:.*]] = arith.divf %[[VAL_34]], %[[VAL_18]] : f32 // CHECK: %[[VAL_38:.*]] = arith.divf %[[VAL_36]], %[[VAL_20]] : f32 // CHECK: %[[VAL_39:.*]] = arith.addf %[[VAL_37]], %[[VAL_38]] : f32 // CHECK: %[[VAL_40:.*]] = arith.mulf %[[VAL_16]], %[[VAL_39]] : f32 // CHECK: %[[VAL_41:.*]] = math.cos %[[VAL_40]] : f32 // CHECK: %[[VAL_42:.*]] = math.sin %[[VAL_40]] : f32 // CHECK: %[[VAL_43:.*]] = arith.mulf %[[VAL_22]], %[[VAL_41]] : f32 // CHECK: %[[VAL_44:.*]] = arith.mulf %[[VAL_22]], %[[VAL_42]] : f32 // CHECK: %[[VAL_45:.*]] = arith.addf %[[VAL_23]], %[[VAL_43]] : f32 // CHECK: %[[VAL_46:.*]] = arith.subf %[[VAL_24]], %[[VAL_44]] : f32 // CHECK: linalg.yield %[[VAL_45]], %[[VAL_46]] : f32, f32 // CHECK: } -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>) // CHECK: return %[[VAL_47:.*]]#0, %[[VAL_47]]#1 : tensor<5x4x5xf32>, tensor<5x4x5xf32> // CHECK: } func.func @test_static_rfft2d(%arg0: tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>) { %output_real, %output_imag = "tosa.rfft2d"(%arg0) {} : (tensor<5x4x8xf32>) -> (tensor<5x4x5xf32>, tensor<5x4x5xf32>) return %output_real, %output_imag : tensor<5x4x5xf32>, tensor<5x4x5xf32> } // ----- // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // CHECK: #[[$ATTR_0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d3, d4)> // CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> // CHECK-LABEL: func.func @test_dynamic_rfft2d( // CHECK-SAME: %[[VAL_0:.*]]: tensor) -> (tensor, tensor) { // CHECK: %[[VAL_1:.*]] = arith.constant 0 : index // CHECK: %[[VAL_2:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor // CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor // CHECK: %[[VAL_5:.*]] = arith.constant 2 : index // CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor // CHECK: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK: %[[VAL_8:.*]] = arith.constant 2 : index // CHECK: %[[VAL_9:.*]] = arith.divui %[[VAL_6]], %[[VAL_8]] : index // CHECK: %[[VAL_10:.*]] = arith.addi %[[VAL_9]], %[[VAL_7]] : index // CHECK: %[[VAL_11:.*]] = tensor.empty(%[[VAL_2]], %[[VAL_4]], %[[VAL_10]]) : tensor // CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_13:.*]] = linalg.fill ins(%[[VAL_12]] : f32) outs(%[[VAL_11]] : tensor) -> tensor // CHECK: %[[VAL_14:.*]] = tensor.empty(%[[VAL_2]], %[[VAL_4]], %[[VAL_10]]) : tensor // CHECK: %[[VAL_15:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_16:.*]] = linalg.fill ins(%[[VAL_15]] : f32) outs(%[[VAL_14]] : tensor) -> tensor // CHECK: %[[VAL_17:.*]] = arith.constant 1 : index // CHECK: %[[VAL_18:.*]] = tensor.dim %[[VAL_0]], %[[VAL_17]] : tensor // CHECK: %[[VAL_19:.*]] = arith.constant 2 : index // CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor // CHECK: %[[VAL_21:.*]] = arith.constant 6.28318548 : f32 // CHECK: %[[VAL_22:.*]] = arith.index_castui %[[VAL_18]] : index to i32 // CHECK: %[[VAL_23:.*]] = arith.uitofp %[[VAL_22]] : i32 to f32 // CHECK: %[[VAL_24:.*]] = arith.index_castui %[[VAL_20]] : index to i32 // CHECK: %[[VAL_25:.*]] = arith.uitofp %[[VAL_24]] : i32 to f32 // CHECK: %[[VAL_26:.*]]:2 = linalg.generic {indexing_maps = [#[[$ATTR_0]], #[[$ATTR_1]], #[[$ATTR_1]]], iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]} ins(%[[VAL_0]] : tensor) outs(%[[VAL_13]], %[[VAL_16]] : tensor, tensor) { // CHECK: ^bb0(%[[VAL_27:.*]]: f32, %[[VAL_28:.*]]: f32, %[[VAL_29:.*]]: f32): // CHECK: %[[VAL_30:.*]] = linalg.index 1 : index // CHECK: %[[VAL_31:.*]] = linalg.index 2 : index // CHECK: %[[VAL_32:.*]] = linalg.index 3 : index // CHECK: %[[VAL_33:.*]] = linalg.index 4 : index // CHECK: %[[VAL_34:.*]] = index.mul %[[VAL_32]], %[[VAL_30]] // CHECK: %[[VAL_35:.*]] = index.mul %[[VAL_33]], %[[VAL_31]] // CHECK: %[[VAL_36:.*]] = index.remu %[[VAL_34]], %[[VAL_18]] // CHECK: %[[VAL_37:.*]] = index.remu %[[VAL_35]], %[[VAL_20]] // CHECK: %[[VAL_38:.*]] = arith.index_castui %[[VAL_36]] : index to i32 // CHECK: %[[VAL_39:.*]] = arith.uitofp %[[VAL_38]] : i32 to f32 // CHECK: %[[VAL_40:.*]] = arith.index_castui %[[VAL_37]] : index to i32 // CHECK: %[[VAL_41:.*]] = arith.uitofp %[[VAL_40]] : i32 to f32 // CHECK: %[[VAL_42:.*]] = arith.divf %[[VAL_39]], %[[VAL_23]] : f32 // CHECK: %[[VAL_43:.*]] = arith.divf %[[VAL_41]], %[[VAL_25]] : f32 // CHECK: %[[VAL_44:.*]] = arith.addf %[[VAL_42]], %[[VAL_43]] : f32 // CHECK: %[[VAL_45:.*]] = arith.mulf %[[VAL_21]], %[[VAL_44]] : f32 // CHECK: %[[VAL_46:.*]] = math.cos %[[VAL_45]] : f32 // CHECK: %[[VAL_47:.*]] = math.sin %[[VAL_45]] : f32 // CHECK: %[[VAL_48:.*]] = arith.mulf %[[VAL_27]], %[[VAL_46]] : f32 // CHECK: %[[VAL_49:.*]] = arith.mulf %[[VAL_27]], %[[VAL_47]] : f32 // CHECK: %[[VAL_50:.*]] = arith.addf %[[VAL_28]], %[[VAL_48]] : f32 // CHECK: %[[VAL_51:.*]] = arith.subf %[[VAL_29]], %[[VAL_49]] : f32 // CHECK: linalg.yield %[[VAL_50]], %[[VAL_51]] : f32, f32 // CHECK: } -> (tensor, tensor) // CHECK: return %[[VAL_52:.*]]#0, %[[VAL_52]]#1 : tensor, tensor // CHECK: } func.func @test_dynamic_rfft2d(%arg0: tensor) -> (tensor, tensor) { %output_real, %output_imag = "tosa.rfft2d"(%arg0) {} : (tensor) -> (tensor, tensor) return %output_real, %output_imag : tensor, tensor } // ----- // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // CHECK: #[[$ATTR_0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d3, d4)> // CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> // CHECK-LABEL: func.func @test_static_fft2d( // CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8x8xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>) { // CHECK: %[[VAL_2:.*]] = tensor.empty() : tensor<8x8x8xf32> // CHECK: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_4:.*]] = linalg.fill ins(%[[VAL_3]] : f32) outs(%[[VAL_2]] : tensor<8x8x8xf32>) -> tensor<8x8x8xf32> // CHECK: %[[VAL_5:.*]] = tensor.empty() : tensor<8x8x8xf32> // CHECK: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_7:.*]] = linalg.fill ins(%[[VAL_6]] : f32) outs(%[[VAL_5]] : tensor<8x8x8xf32>) -> tensor<8x8x8xf32> // CHECK: %[[VAL_8:.*]] = arith.constant 1 : index // CHECK: %[[VAL_9:.*]] = arith.constant 8 : index // CHECK: %[[VAL_10:.*]] = arith.constant 2 : index // CHECK: %[[VAL_11:.*]] = arith.constant 8 : index // CHECK: %[[VAL_12:.*]] = arith.constant 6.28318548 : f32 // CHECK: %[[VAL_13:.*]] = arith.index_castui %[[VAL_9]] : index to i32 // CHECK: %[[VAL_14:.*]] = arith.uitofp %[[VAL_13]] : i32 to f32 // CHECK: %[[VAL_15:.*]] = arith.index_castui %[[VAL_11]] : index to i32 // CHECK: %[[VAL_16:.*]] = arith.uitofp %[[VAL_15]] : i32 to f32 // CHECK: %[[VAL_17:.*]]:2 = linalg.generic {indexing_maps = [#[[$ATTR_0]], #[[$ATTR_0]], #[[$ATTR_1]], #[[$ATTR_1]]], iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]} ins(%[[VAL_0]], %[[VAL_1]] : tensor<8x8x8xf32>, tensor<8x8x8xf32>) outs(%[[VAL_4]], %[[VAL_7]] : tensor<8x8x8xf32>, tensor<8x8x8xf32>) { // CHECK: ^bb0(%[[VAL_18:.*]]: f32, %[[VAL_19:.*]]: f32, %[[VAL_20:.*]]: f32, %[[VAL_21:.*]]: f32): // CHECK: %[[VAL_22:.*]] = linalg.index 1 : index // CHECK: %[[VAL_23:.*]] = linalg.index 2 : index // CHECK: %[[VAL_24:.*]] = linalg.index 3 : index // CHECK: %[[VAL_25:.*]] = linalg.index 4 : index // CHECK: %[[VAL_26:.*]] = index.mul %[[VAL_24]], %[[VAL_22]] // CHECK: %[[VAL_27:.*]] = index.mul %[[VAL_25]], %[[VAL_23]] // CHECK: %[[VAL_28:.*]] = index.remu %[[VAL_26]], %[[VAL_9]] // CHECK: %[[VAL_29:.*]] = index.remu %[[VAL_27]], %[[VAL_11]] // CHECK: %[[VAL_30:.*]] = arith.index_castui %[[VAL_28]] : index to i32 // CHECK: %[[VAL_31:.*]] = arith.uitofp %[[VAL_30]] : i32 to f32 // CHECK: %[[VAL_32:.*]] = arith.index_castui %[[VAL_29]] : index to i32 // CHECK: %[[VAL_33:.*]] = arith.uitofp %[[VAL_32]] : i32 to f32 // CHECK: %[[VAL_34:.*]] = arith.divf %[[VAL_31]], %[[VAL_14]] : f32 // CHECK: %[[VAL_35:.*]] = arith.divf %[[VAL_33]], %[[VAL_16]] : f32 // CHECK: %[[VAL_36:.*]] = arith.addf %[[VAL_34]], %[[VAL_35]] : f32 // CHECK: %[[VAL_37:.*]] = arith.mulf %[[VAL_12]], %[[VAL_36]] : f32 // CHECK: %[[VAL_38:.*]] = math.cos %[[VAL_37]] : f32 // CHECK: %[[VAL_39:.*]] = math.sin %[[VAL_37]] : f32 // CHECK: %[[VAL_40:.*]] = arith.mulf %[[VAL_18]], %[[VAL_38]] : f32 // CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_19]], %[[VAL_39]] : f32 // CHECK: %[[VAL_42:.*]] = arith.addf %[[VAL_40]], %[[VAL_41]] : f32 // CHECK: %[[VAL_43:.*]] = arith.mulf %[[VAL_19]], %[[VAL_38]] : f32 // CHECK: %[[VAL_44:.*]] = arith.mulf %[[VAL_18]], %[[VAL_39]] : f32 // CHECK: %[[VAL_45:.*]] = arith.subf %[[VAL_43]], %[[VAL_44]] : f32 // CHECK: %[[VAL_46:.*]] = arith.addf %[[VAL_20]], %[[VAL_42]] : f32 // CHECK: %[[VAL_47:.*]] = arith.addf %[[VAL_21]], %[[VAL_45]] : f32 // CHECK: linalg.yield %[[VAL_46]], %[[VAL_47]] : f32, f32 // CHECK: } -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>) // CHECK: return %[[VAL_48:.*]]#0, %[[VAL_48]]#1 : tensor<8x8x8xf32>, tensor<8x8x8xf32> // CHECK: } func.func @test_static_fft2d(%arg0: tensor<8x8x8xf32>, %arg1: tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>) { %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) {inverse=false} : (tensor<8x8x8xf32>, tensor<8x8x8xf32>) -> (tensor<8x8x8xf32>, tensor<8x8x8xf32>) return %output_real, %output_imag : tensor<8x8x8xf32>, tensor<8x8x8xf32> } // ----- // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // CHECK: #[[$ATTR_0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d3, d4)> // CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2)> // CHECK-LABEL: func.func @test_dynamic_fft2d( // CHECK-SAME: %[[VAL_0:.*]]: tensor, // CHECK-SAME: %[[VAL_1:.*]]: tensor) -> (tensor, tensor) { // CHECK: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor // CHECK: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor // CHECK: %[[VAL_6:.*]] = arith.constant 2 : index // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor // CHECK: %[[VAL_8:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_5]], %[[VAL_7]]) : tensor // CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_10:.*]] = linalg.fill ins(%[[VAL_9]] : f32) outs(%[[VAL_8]] : tensor) -> tensor // CHECK: %[[VAL_11:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_5]], %[[VAL_7]]) : tensor // CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_13:.*]] = linalg.fill ins(%[[VAL_12]] : f32) outs(%[[VAL_11]] : tensor) -> tensor // CHECK: %[[VAL_14:.*]] = arith.constant 1 : index // CHECK: %[[VAL_15:.*]] = tensor.dim %[[VAL_0]], %[[VAL_14]] : tensor // CHECK: %[[VAL_16:.*]] = arith.constant 2 : index // CHECK: %[[VAL_17:.*]] = tensor.dim %[[VAL_0]], %[[VAL_16]] : tensor // CHECK: %[[VAL_18:.*]] = arith.constant 6.28318548 : f32 // CHECK: %[[VAL_19:.*]] = arith.index_castui %[[VAL_15]] : index to i32 // CHECK: %[[VAL_20:.*]] = arith.uitofp %[[VAL_19]] : i32 to f32 // CHECK: %[[VAL_21:.*]] = arith.index_castui %[[VAL_17]] : index to i32 // CHECK: %[[VAL_22:.*]] = arith.uitofp %[[VAL_21]] : i32 to f32 // CHECK: %[[VAL_23:.*]]:2 = linalg.generic {indexing_maps = [#[[$ATTR_0]], #[[$ATTR_0]], #[[$ATTR_1]], #[[$ATTR_1]]], iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction"]} ins(%[[VAL_0]], %[[VAL_1]] : tensor, tensor) outs(%[[VAL_10]], %[[VAL_13]] : tensor, tensor) { // CHECK: ^bb0(%[[VAL_24:.*]]: f32, %[[VAL_25:.*]]: f32, %[[VAL_26:.*]]: f32, %[[VAL_27:.*]]: f32): // CHECK: %[[VAL_28:.*]] = linalg.index 1 : index // CHECK: %[[VAL_29:.*]] = linalg.index 2 : index // CHECK: %[[VAL_30:.*]] = linalg.index 3 : index // CHECK: %[[VAL_31:.*]] = linalg.index 4 : index // CHECK: %[[VAL_32:.*]] = index.mul %[[VAL_30]], %[[VAL_28]] // CHECK: %[[VAL_33:.*]] = index.mul %[[VAL_31]], %[[VAL_29]] // CHECK: %[[VAL_34:.*]] = index.remu %[[VAL_32]], %[[VAL_15]] // CHECK: %[[VAL_35:.*]] = index.remu %[[VAL_33]], %[[VAL_17]] // CHECK: %[[VAL_36:.*]] = arith.index_castui %[[VAL_34]] : index to i32 // CHECK: %[[VAL_37:.*]] = arith.uitofp %[[VAL_36]] : i32 to f32 // CHECK: %[[VAL_38:.*]] = arith.index_castui %[[VAL_35]] : index to i32 // CHECK: %[[VAL_39:.*]] = arith.uitofp %[[VAL_38]] : i32 to f32 // CHECK: %[[VAL_40:.*]] = arith.divf %[[VAL_37]], %[[VAL_20]] : f32 // CHECK: %[[VAL_41:.*]] = arith.divf %[[VAL_39]], %[[VAL_22]] : f32 // CHECK: %[[VAL_42:.*]] = arith.addf %[[VAL_40]], %[[VAL_41]] : f32 // CHECK: %[[VAL_43:.*]] = arith.mulf %[[VAL_18]], %[[VAL_42]] : f32 // CHECK: %[[VAL_44:.*]] = arith.constant -1.000000e+00 : f32 // CHECK: %[[VAL_45:.*]] = arith.mulf %[[VAL_43]], %[[VAL_44]] : f32 // CHECK: %[[VAL_46:.*]] = math.cos %[[VAL_45]] : f32 // CHECK: %[[VAL_47:.*]] = math.sin %[[VAL_45]] : f32 // CHECK: %[[VAL_48:.*]] = arith.mulf %[[VAL_24]], %[[VAL_46]] : f32 // CHECK: %[[VAL_49:.*]] = arith.mulf %[[VAL_25]], %[[VAL_47]] : f32 // CHECK: %[[VAL_50:.*]] = arith.addf %[[VAL_48]], %[[VAL_49]] : f32 // CHECK: %[[VAL_51:.*]] = arith.mulf %[[VAL_25]], %[[VAL_46]] : f32 // CHECK: %[[VAL_52:.*]] = arith.mulf %[[VAL_24]], %[[VAL_47]] : f32 // CHECK: %[[VAL_53:.*]] = arith.subf %[[VAL_51]], %[[VAL_52]] : f32 // CHECK: %[[VAL_54:.*]] = arith.addf %[[VAL_26]], %[[VAL_50]] : f32 // CHECK: %[[VAL_55:.*]] = arith.addf %[[VAL_27]], %[[VAL_53]] : f32 // CHECK: linalg.yield %[[VAL_54]], %[[VAL_55]] : f32, f32 // CHECK: } -> (tensor, tensor) // CHECK: return %[[VAL_56:.*]]#0, %[[VAL_56]]#1 : tensor, tensor // CHECK: } func.func @test_dynamic_fft2d(%arg0: tensor, %arg1: tensor) -> (tensor, tensor) { %output_real, %output_imag = "tosa.fft2d"(%arg0, %arg1) {inverse = true} : (tensor, tensor) -> (tensor, tensor) return %output_real, %output_imag : tensor, tensor } // ----- // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0)> // CHECK-LABEL: func.func @test_cast_fp32_i64( // CHECK-SAME: %[[ARG0:.*]]: tensor<1xf32>) -> tensor<1xi64> { // CHECK: %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<1xi64> // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<1xi64>) { // CHECK: ^bb0(%[[IN:.*]]: f32, %[[OUT:.*]]: i64): // CHECK: %[[ROUND_EVEN:.*]] = math.roundeven %[[IN]] : f32 // CHECK: %[[FP_INT_MIN:.*]] = arith.constant -9.22337203E+18 : f32 // CHECK: %[[FP_INT_MAX_PLUS_ONE:.*]] = arith.constant 9.22337203E+18 : f32 // CHECK: %[[INT_MAX:.*]] = arith.constant 9223372036854775807 : i64 // CHECK: %[[MAX:.*]] = arith.maximumf %[[ROUND_EVEN]], %[[FP_INT_MIN]] : f32 // CHECK: %[[FPTOSI:.*]] = arith.fptosi %[[MAX]] : f32 to i64 // CHECK: %[[CMPF:.*]] = arith.cmpf uge, %[[ROUND_EVEN]], %[[FP_INT_MAX_PLUS_ONE]] : f32 // CHECK: %[[SELECT:.*]] = arith.select %[[CMPF]], %[[INT_MAX]], %[[FPTOSI]] : i64 // CHECK: linalg.yield %[[SELECT]] : i64 // CHECK: } -> tensor<1xi64> // CHECK: return %[[RESULT]] : tensor<1xi64> func.func @test_cast_fp32_i64(%arg0: tensor<1xf32>) -> (tensor<1xi64>) { %0 = tosa.cast %arg0 : (tensor<1xf32>) -> tensor<1xi64> return %0: tensor<1xi64> } // ----- // CHECK-LABEL: @reduce_min_nan_propagate func.func @reduce_min_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.reduce // CHECK: arith.minimumf // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield // CHECK-NOT: arith.constant 0x7FC00000 // CHECK-NOT: tensor.empty() // CHECK-NOT: linalg.fill // CHECK-NOT: tensor.empty() // CHECK-NOT: select // CHECK: return %3 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = "PROPAGATE"} : (tensor<5x4xf32>) -> tensor<1x4xf32> return } // ----- // CHECK-LABEL: @reduce_max_nan_propagate func.func @reduce_max_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.reduce // CHECK: arith.maximumf // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield // CHECK-NOT: arith.constant 0x7FC00000 // CHECK-NOT: tensor.empty() // CHECK-NOT: linalg.fill // CHECK-NOT: tensor.empty() // CHECK-NOT: select // CHECK: return %4 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = "PROPAGATE"} : (tensor<5x4xf32>) -> tensor<1x4xf32> return } // ----- // CHECK-LABEL: @reduce_min_nan_ignore_int func.func @reduce_min_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.reduce // CHECK: arith.minsi // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield // CHECK-NOT: arith.constant 0x7FC00000 // CHECK-NOT: tensor.empty() // CHECK-NOT: linalg.fill // CHECK-NOT: tensor.empty() // CHECK-NOT: select // CHECK: return %5 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xi8>) -> tensor<1x4xi8> return } // ----- // CHECK-LABEL: @reduce_max_nan_ignore_int func.func @reduce_max_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.reduce // CHECK: arith.maxsi // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield // CHECK-NOT: arith.constant 0x7FC00000 // CHECK-NOT: tensor.empty() // CHECK-NOT: linalg.fill // CHECK-NOT: tensor.empty() // CHECK-NOT: select // CHECK: return %6 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xi8>) -> tensor<1x4xi8> return } // ----- // CHECK-LABEL: @reduce_min_nan_ignore func.func @reduce_min_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.reduce // CHECK: arith.minimumf // CHECK: arith.cmpf uno // CHECK: arith.select // CHECK: linalg.yield // CHECK: arith.constant 0x7FC00000 // CHECK: tensor.empty() // CHECK: linalg.fill // CHECK: tensor.empty() // CHECK: select %5 = tosa.reduce_min %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xf32>) -> tensor<1x4xf32> return } // ----- // CHECK-LABEL: @reduce_max_nan_ignore func.func @reduce_max_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.reduce // CHECK: arith.maximumf // CHECK: arith.cmpf uno // CHECK: arith.select // CHECK: linalg.yield // CHECK: arith.constant 0x7FC00000 // CHECK: tensor.empty() // CHECK: linalg.fill // CHECK: tensor.empty() // CHECK: select %6 = tosa.reduce_max %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xf32>) -> tensor<1x4xf32> return } // ----- // CHECK-LABEL: @minimum_nan_propagate func.func @minimum_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.minimumf // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %7 = tosa.minimum %arg0, %arg1 {nan_mode = "PROPAGATE"} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @maximum_nan_propagate func.func @maximum_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.maximumf // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %8 = tosa.maximum %arg0, %arg1 {nan_mode = "PROPAGATE"} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @minimum_nan_ignore_int func.func @minimum_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.generic // CHECK: arith.minsi // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %9 = tosa.minimum %arg0, %arg1 {nan_mode = "IGNORE"} : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8> return } // ----- // CHECK-LABEL: @maximum_nan_ignore_int func.func @maximum_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.generic // CHECK: arith.maxsi // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %10 = tosa.maximum %arg0, %arg1 {nan_mode = "IGNORE"} : (tensor<5x4xi8>, tensor<5x4xi8>) -> tensor<5x4xi8> return } // ----- // CHECK-LABEL: @minimum_nan_ignore func.func @minimum_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.minimumf // CHECK: arith.cmpf uno // CHECK: arith.cmpf uno // CHECK: arith.select // CHECK: arith.select // CHECK: linalg.yield %9 = tosa.minimum %arg0, %arg1 {nan_mode = "IGNORE"} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @maximum_nan_ignore func.func @maximum_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.maximumf // CHECK: arith.cmpf uno // CHECK: arith.cmpf uno // CHECK: arith.select // CHECK: arith.select // CHECK: linalg.yield %10 = tosa.maximum %arg0, %arg1 {nan_mode = "IGNORE"} : (tensor<5x4xf32>, tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @argmax_nan_propagate func.func @argmax_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.cmpf ugt // CHECK: arith.cmpf ord // CHECK: andi // CHECK: arith.select // CHECK: arith.select // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %11 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = "PROPAGATE"} : (tensor<5x4xf32>) -> tensor<4xi32> return } // ----- // CHECK-LABEL: @argmax_nan_ignore_int func.func @argmax_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.generic // CHECK: arith.cmpi sgt // CHECK: arith.select // CHECK: arith.select // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK-NOT: arith.select // CHECK: linalg.yield %12 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xi8>) -> tensor<4xi32> return } // ----- // CHECK-LABEL: @argmax_nan_ignore func.func @argmax_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.cmpf ogt // CHECK: arith.select // CHECK: arith.select // CHECK: linalg.yield %12 = tosa.argmax %arg0 {axis = 0 : i32, nan_mode = "IGNORE"} : (tensor<5x4xf32>) -> tensor<4xi32> return } // ----- // CHECK-LABEL: @clamp_nan_propagate func.func @clamp_nan_propagate(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.minimumf // CHECK: arith.maximumf // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %13 = tosa.clamp %arg0 {min_val = 1.0 : f32, max_val = 5.0 : f32, nan_mode = "PROPAGATE"} : (tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @clamp_nan_ignore_int func.func @clamp_nan_ignore_int(%arg0: tensor<5x4xi8>, %arg1: tensor<5x4xi8>) -> () { // CHECK: linalg.generic // CHECK: arith.maxsi // CHECK: arith.minsi // CHECK-NOT: arith.cmpf uno // CHECK-NOT: arith.select // CHECK: linalg.yield %14 = tosa.clamp %arg0 {min_val = 1 : i8, max_val = 5 : i8, nan_mode = "IGNORE"} : (tensor<5x4xi8>) -> tensor<5x4xi8> return } // ----- // CHECK-LABEL: @clamp_nan_ignore func.func @clamp_nan_ignore(%arg0: tensor<5x4xf32>, %arg1: tensor<5x4xf32>) -> () { // CHECK: linalg.generic // CHECK: arith.minimumf // CHECK: arith.maximumf // CHECK: arith.cmpf uno // CHECK: arith.select // CHECK: linalg.yield %14 = tosa.clamp %arg0 {min_val = 1.0 : f32, max_val = 5.0 : f32, nan_mode = "IGNORE"} : (tensor<5x4xf32>) -> tensor<5x4xf32> return } // ----- // CHECK-LABEL: @test_0d_input func.func @test_0d_input(%arg0: tensor) -> () { // CHECK: linalg.generic // CHECK: arith.muli %shift1 = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8> %0 = tosa.mul %arg0, %arg0, %shift1 : (tensor, tensor, tensor<1xi8>) -> tensor // CHECK: linalg.generic // CHECK: ^bb0(%[[ARG1:.*]]: i32, %[[ARG2:.*]]: i32): // CHECK: [[ZERO:%.+]] = arith.constant 0 // CHECK: arith.subi [[ZERO]], %[[ARG1]] %in_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32> %out_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32> %5 = tosa.negate %arg0, %in_zp, %out_zp : (tensor, tensor<1xi32>, tensor<1xi32>) -> tensor return }