| // RUN: mlir-opt --split-input-file --tosa-to-linalg %s -verify-diagnostics -o -| FileCheck %s |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<() -> ()> |
| |
| // CHECK-LABEL: @test_abs |
| func @test_abs(%arg0: tensor<f32>) -> tensor<f32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [] : tensor<f32> |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = []} ins(%arg0 : tensor<f32>) outs([[INIT]] : tensor<f32>) { |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32): |
| // CHECK: [[ELEMENT:%.+]] = math.abs %arg1 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<f32> |
| |
| %0 = "tosa.abs"(%arg0) : (tensor<f32>) -> tensor<f32> |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<f32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @test_abs |
| func @test_abs(%arg0: tensor<2xf32>) -> tensor<2xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] : tensor<2xf32> |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xf32>) outs([[INIT]] : tensor<2xf32>) { |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32): |
| // CHECK: [[ELEMENT:%.+]] = math.abs %arg1 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2xf32> |
| %0 = "tosa.abs"(%arg0) : (tensor<2xf32>) -> tensor<2xf32> |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| |
| // CHECK-LABEL: @test_abs |
| func @test_abs(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2, 3] : tensor<2x3xf32> |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<2x3xf32>) outs([[INIT]] : tensor<2x3xf32>) { |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32): |
| // CHECK: [[ELEMENT:%.+]] = math.abs %arg1 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2x3xf32> |
| %0 = "tosa.abs"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32> |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<2x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_abs |
| func @test_abs(%arg0: tensor<?xf32>) -> tensor<?xf32> { |
| // CHECK: %[[C0:.+]] = arith.constant 0 |
| // CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C0]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DIM]]] |
| // CHECK: linalg.generic |
| // CHECK: math.abs |
| %0 = "tosa.abs"(%arg0) : (tensor<?xf32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| |
| // CHECK-LABEL: @test_abs_dyn |
| func @test_abs_dyn(%arg0: tensor<2x?xf32>) -> tensor<2x?xf32> { |
| // CHECK: %[[C1:.+]] = arith.constant 1 |
| // CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C1]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [2, %[[DIM]]] |
| // CHECK: linalg.generic |
| // CHECK: math.abs |
| %0 = "tosa.abs"(%arg0) : (tensor<2x?xf32>) -> tensor<2x?xf32> |
| return %0 : tensor<2x?xf32> |
| } |
| // ----- |
| |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> ()> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @test_broadcast |
| func @test_broadcast(%arg0: tensor<1xf32>, %arg1: tensor<2xf32>) -> tensor<2xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] : tensor<2xf32> |
| // CHECK: [[RESHAPE:%.+]] = tensor.collapse_shape %arg0 |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins([[RESHAPE]], %arg1 : tensor<f32>, tensor<2xf32>) outs([[INIT]] : tensor<2xf32>) { |
| // CHECK: ^bb0(%arg2: f32, %arg3: f32, %arg4: f32): |
| // CHECK: [[ELEMENT:%.+]] = arith.addf %arg2, %arg3 : f32 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2xf32> |
| %0 = "tosa.add"(%arg0, %arg1) : (tensor<1xf32>, tensor<2xf32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0) -> ()> |
| |
| // CHECK-LABEL: @test_broadcast_swapped_args |
| func @test_broadcast_swapped_args(%arg0: tensor<2xf32>, %arg1: tensor<1xf32>) -> tensor<2xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] : tensor<2xf32> |
| // CHECK: [[RESHAPE:%.+]] = tensor.collapse_shape %arg1 |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0, [[RESHAPE]] : tensor<2xf32>, tensor<f32>) outs([[INIT]] : tensor<2xf32>) { |
| // CHECK: ^bb0(%arg2: f32, %arg3: f32, %arg4: f32): |
| // CHECK: [[ELEMENT:%.+]] = arith.addf %arg2, %arg3 : f32 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2xf32> |
| %0 = "tosa.add"(%arg0, %arg1) : (tensor<2xf32>, tensor<1xf32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> |
| |
| // CHECK-LABEL: @test_multibroadcast |
| func @test_multibroadcast(%arg0: tensor<1x3xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x3xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2, 3] : tensor<2x3xf32> |
| // CHECK: [[RESHAPE1:%.+]] = tensor.collapse_shape %arg0 {{\[}}[0, 1]] |
| // CHECK: [[RESHAPE2:%.+]] = tensor.collapse_shape %arg1 {{\[}}[0, 1]] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins([[RESHAPE1]], [[RESHAPE2]] : tensor<3xf32>, tensor<2xf32>) outs([[INIT]] : tensor<2x3xf32>) { |
| // CHECK: ^bb0(%arg2: f32, %arg3: f32, %arg4: f32): |
| // CHECK: [[ELEMENT:%.+]] = arith.addf %arg2, %arg3 : f32 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2x3xf32> |
| %0 = "tosa.add"(%arg0, %arg1) : (tensor<1x3xf32>, tensor<2x1xf32>) -> tensor<2x3xf32> |
| return %0 : tensor<2x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_f32 |
| 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.abs |
| %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 |
| %4 = "tosa.mul"(%0, %1) {shift = 0 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.negf |
| %5 = "tosa.negate"(%0) : (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.cmpf |
| // CHECK: select |
| %14 = "tosa.maximum"(%0, %1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpf |
| // CHECK: select |
| %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.cmpf |
| // CHECK: select |
| %18 = "tosa.clamp"(%0) {min_int = 1 : i64, max_int = 5 : i64, min_fp = 1.0 : f32, max_fp = 5.0 : f32} : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpf |
| // CHECK: select |
| %19 = "tosa.reluN"(%0) {max_int = 5 : i64, max_fp = 5.0 : f32} : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.negf |
| // CHECK: exp |
| // CHECK: arith.addf |
| // CHECK: arith.divf |
| %20 = "tosa.sigmoid"(%0) : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant 0.000000e+00 |
| // CHECK: arith.constant 5.000000e-01 |
| // CHECK: arith.constant -2.14748365E+9 |
| // CHECK: arith.constant 2.14748365E+9 |
| // CHECK: arith.addf |
| // CHECK: arith.subf |
| // CHECK: arith.cmpf olt |
| // CHECK: select |
| // CHECK: arith.cmpf olt |
| // CHECK: select |
| // CHECK: arith.cmpf olt |
| // CHECK: select |
| // CHECK: arith.fptosi |
| %21 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant 0 |
| // CHECK: arith.cmpf |
| %22 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.truncf |
| %23 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xf16> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.divf |
| %24 = "tosa.reciprocal"(%0) : (tensor<1xf32>) -> tensor<1xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_f16 |
| func @test_simple_f16(%arg0: tensor<1xf16>) -> () { |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.extf |
| %0 = "tosa.cast"(%arg0) : (tensor<1xf16>) -> tensor<1xf32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_i16 |
| func @test_simple_i16(%arg0: tensor<1xi16>) -> () { |
| // CHECK: linalg.generic |
| // CHECK: arith.extsi |
| // CHECK: arith.extsi |
| // CHECK: arith.muli |
| %0 = "tosa.mul"(%arg0, %arg0) {shift = 0 : i32} : (tensor<1xi16>, tensor<1xi16>) -> tensor<1xi32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_ui8 |
| 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 @test_simple_i32(%arg0: tensor<1xi32>) -> () { |
| // 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 |
| %2 = "tosa.mul"(%arg0, %arg0) {shift = 0 : i32} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant 2 |
| // CHECK: apply_scale |
| %3 = "tosa.mul"(%arg0, %arg0) {shift = 2 : i32} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.divsi |
| %4 = "tosa.div"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: [[ZERO:%.+]] = arith.constant 0 |
| // CHECK: arith.subi [[ZERO]], %arg1 |
| %5 = "tosa.negate"(%arg0) : (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: scf.while |
| // CHECK: arith.cmpi ne |
| // CHECK: scf.condition |
| // CHECK: arith.shrui |
| // CHECK: arith.subi |
| // CHECK: scf.yield |
| %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.cmpi |
| // CHECK: select |
| %17 = "tosa.maximum"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpi |
| // CHECK: select |
| %18 = "tosa.minimum"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpi |
| // CHECK: select |
| %19 = "tosa.clamp"(%0) {min_int = 1 : i64, max_int = 5 : i64, min_fp = 1.0 : f32, max_fp = 5.0 : f32} : (tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpi |
| // CHECK: select |
| %20 = "tosa.reluN"(%0) {max_int = 5 : i64, max_fp = 5.0 : f32} : (tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant -32768 |
| // CHECK: arith.constant 32767 |
| // CHECK: arith.cmpi slt |
| // CHECK: select |
| // CHECK: arith.cmpi slt |
| // CHECK: select |
| // CHECK: arith.trunci |
| %21 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi16> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.extsi |
| %22 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi64> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant 0 |
| // CHECK: arith.cmpi |
| %23 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.sitofp |
| %24 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: arith.constant 0 |
| // CHECK: arith.cmpi sgt |
| // CHECK: arith.subi |
| // CHECK: select |
| %25 = "tosa.abs"(%arg0) : (tensor<1xi32>) -> tensor<1xi32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_ui8 |
| 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 @test_i8(%arg0: tensor<1xi8>) -> () { |
| // CHECK: linalg.generic |
| // CHECK-DAG: %[[C127:.+]] = arith.constant -127 |
| // CHECK-DAG: %[[C126:.+]] = arith.constant 126 |
| // CHECK-DAG: %[[CMP1:.+]] = arith.cmpi slt, %arg1, %[[C127]] |
| // CHECK-DAG: %[[SEL1:.+]] = select %[[CMP1]], %[[C127]] |
| // CHECK-DAG: %[[CMP2:.+]] = arith.cmpi slt, %[[C126]], %arg1 |
| // CHECK: %[[SEL2:.+]] = select %[[CMP2]], %[[C126]], %[[SEL1]] |
| %0 = "tosa.clamp"(%arg0) {min_int = -127 : i64, max_int = 126 : i64, min_fp = 0.0 : f32, max_fp = 0.0 : f32} : (tensor<1xi8>) -> tensor<1xi8> |
| |
| // CHECK: linalg.generic |
| // CHECK-DAG: %[[C128:.+]] = arith.constant -128 |
| // CHECK-DAG: %[[C127:.+]] = arith.constant 127 |
| // CHECK-DAG: %[[CMP1:.+]] = arith.cmpi slt, %arg1, %[[C128]] |
| // CHECK-DAG: %[[SEL1:.+]] = select %[[CMP1]], %[[C128]] |
| // CHECK-DAG: %[[CMP2:.+]] = arith.cmpi slt, %[[C127]], %arg1 |
| // CHECK: %[[SEL2:.+]] = select %[[CMP2]], %[[C127]], %[[SEL1]] |
| %1 = "tosa.clamp"(%arg0) {min_int = -130 : i64, max_int = 130 : i64, min_fp = 0.0 : f32, max_fp = 0.0 : f32} : (tensor<1xi8>) -> tensor<1xi8> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_bool |
| 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 @test_negate_quantized(%arg0: tensor<1xi8>) -> () { |
| // CHECK: linalg.generic |
| // CHECK: [[ZERO:%.+]] = arith.constant 0 |
| // CHECK: [[EXT:%.+]] = arith.extsi %arg1 : i8 to i16 |
| // CHECK: [[SUB:%.+]] = arith.subi [[ZERO]], [[EXT]] |
| // CHECK: [[MIN:%.+]] = arith.constant -128 |
| // CHECK: [[MAX:%.+]] = arith.constant 127 |
| // CHECK: [[PRED1:%.+]] = arith.cmpi slt, [[SUB]], [[MIN]] |
| // CHECK: [[LBOUND:%.+]] = select [[PRED1]], [[MIN]], [[SUB]] |
| // CHECK: [[PRED2:%.+]] = arith.cmpi slt, [[MAX]], [[SUB]] |
| // CHECK: [[UBOUND:%.+]] = select [[PRED2]], [[MAX]], [[LBOUND]] |
| // CHECK: [[TRUNC:%.+]] = arith.trunci [[UBOUND]] |
| // CHECK: linalg.yield [[TRUNC]] |
| %0 = "tosa.negate"(%arg0) {quantization_info = { input_zp = 0 : i32, output_zp = 0 : i32}} : (tensor<1xi8>) -> tensor<1xi8> |
| |
| // CHECK: linalg.generic |
| // CHECK: [[EXT:%.+]] = arith.extsi %arg1 : i8 to i16 |
| %1 = "tosa.negate"(%arg0) {quantization_info = { input_zp = 32639 : i32, output_zp = 0 : i32}} : (tensor<1xi8>) -> tensor<1xi8> |
| |
| // CHECK: linalg.generic |
| // CHECK: [[EXT:%.+]] = arith.extsi %arg1 : i8 to i32 |
| %2 = "tosa.negate"(%arg0) {quantization_info = { input_zp = 32640 : i32, output_zp = 0 : i32}} : (tensor<1xi8>) -> tensor<1xi8> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_downrank |
| func @test_reshape_downrank(%arg0: tensor<2x3xf32>) -> tensor<6xf32> { |
| // CHECK: [[RESHAPE:%.+]] = tensor.collapse_shape %arg0 {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [6]} : (tensor<2x3xf32>) -> tensor<6xf32> |
| // CHECK: return [[RESHAPE]] |
| return %0 : tensor<6xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_downrank_dyn |
| func @test_reshape_downrank_dyn(%arg0: tensor<2x?xf32>) -> tensor<?xf32> { |
| // CHECK: [[RESHAPE:%.+]] = tensor.collapse_shape %arg0 {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<2x?xf32>) -> tensor<?xf32> |
| // CHECK: return [[RESHAPE]] |
| return %0 : tensor<?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_uprank |
| func @test_reshape_uprank(%arg0: tensor<6xf32>) -> tensor<2x3xf32> { |
| // CHECK: [[RESHAPE:%.+]] = tensor.expand_shape %arg0 {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [2, 3]} : (tensor<6xf32>) -> tensor<2x3xf32> |
| // CHECK: return [[RESHAPE]] |
| return %0 : tensor<2x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_uprank_dyn |
| func @test_reshape_uprank_dyn(%arg0: tensor<?xf32>) -> tensor<2x?xf32> { |
| // CHECK: [[RESHAPE:%.+]] = tensor.expand_shape %arg0 {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<?xf32>) -> tensor<2x?xf32> |
| // CHECK: return [[RESHAPE]] |
| return %0 : tensor<2x?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_samerank |
| func @test_reshape_samerank(%arg0: tensor<3x2xf32>) -> tensor<2x3xf32> { |
| // CHECK-SAME: (%[[ARG0:.*]]: tensor<3x2xf32>) |
| // CHECK-NEXT: %[[RESHAPE1:.*]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1]] |
| // CHECK-NEXT: %[[RESHAPE2:.*]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [2, 3]} : (tensor<3x2xf32>) -> tensor<2x3xf32> |
| // CHECK-NEXT: return %[[RESHAPE2]] |
| return %0 : tensor<2x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_samerank_dyn |
| func @test_reshape_samerank_dyn(%arg0: tensor<?x2xf32>) -> tensor<2x?xf32> { |
| // CHECK-SAME: (%[[ARG0:.*]]: tensor<?x2xf32>) |
| // CHECK-NEXT: %[[RESHAPE1:.*]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1]] |
| // CHECK-NEXT: %[[RESHAPE2:.*]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<?x2xf32>) -> tensor<2x?xf32> |
| // CHECK-NEXT: return %[[RESHAPE2]] |
| return %0 : tensor<2x?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_downrank_6D |
| func @test_reshape_downrank_6D(%arg0: tensor<1x2x3x5x7x11xf32>) -> tensor<6x5x77xf32> { |
| // CHECK: tensor.collapse_shape %arg0 {{\[}}[0, 1, 2], [3], [4, 5]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [6, 5, 77]} : (tensor<1x2x3x5x7x11xf32>) -> tensor<6x5x77xf32> |
| return %0 : tensor<6x5x77xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_reshape_downrank_6D_dyn |
| func @test_reshape_downrank_6D_dyn(%arg0: tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32> { |
| // CHECK: tensor.collapse_shape %arg0 {{\[}}[0, 1, 2, 3, 4, 5]] |
| // CHECK: tensor.expand_shape %0 {{\[}}[0, 1, 2]] |
| %0 = "tosa.reshape"(%arg0) {new_shape = [-1, 5, 77]} : (tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32> |
| return %0 : tensor<?x5x77xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_identity |
| 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: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d2, d0, d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> |
| |
| // CHECK-LABEL: @test_transpose |
| // CHECK-SAME: ([[ARG0:%.+]]: tensor<1x2x3xi32>) |
| func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () { |
| %0 = arith.constant dense<[1, 2, 0]> : tensor<3xi32> |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2, 3, 1] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel"]} ins([[ARG0]] : tensor<1x2x3xi32>) outs([[OUT:%.+]] : tensor<2x3x1xi32>) |
| // CHECK: ^bb0([[ARG1:%.+]]: i32, [[ARG2:%.+]]: i32) |
| // CHECK: linalg.yield [[ARG1]] |
| // CHECK: } |
| %1 = "tosa.transpose"(%arg0, %0) : (tensor<1x2x3xi32>, tensor<3xi32>) -> (tensor<2x3x1xi32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d0, d3, d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| |
| // CHECK-LABEL: @test_transpose_dyn |
| // CHECK-SAME: (%[[ARG0:.+]]: tensor<1x?x3x4xi32>) |
| func @test_transpose_dyn(%arg0: tensor<1x?x3x4xi32>) -> () { |
| %0 = arith.constant dense<[1, 3, 0, 2]> : tensor<4xi32> |
| // CHECK: %[[C1:.+]] = arith.constant 1 |
| // CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C1]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DIM]], 4, 1, 3] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<1x?x3x4xi32>) outs([[OUT:%.+]] : tensor<?x4x1x3xi32>) |
| // CHECK: ^bb0([[ARG1:%.+]]: i32, [[ARG2:%.+]]: i32) |
| // CHECK: linalg.yield [[ARG1]] |
| // CHECK: } |
| %1 = "tosa.transpose"(%arg0, %0) : (tensor<1x?x3x4xi32>, tensor<4xi32>) -> (tensor<?x4x1x3xi32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d1, d0)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| |
| // CHECK-LABEL: @test_transpose_dyn |
| // CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?xf32>) |
| func @test_transpose_dyn_multiple(%arg0: tensor<?x?xf32>) -> () { |
| %0 = arith.constant dense<[1, 0]> : tensor<2xi32> |
| // CHECK: %[[C0:.+]] = arith.constant 0 |
| // CHECK: %[[DIM0:.+]] = tensor.dim %arg0, %[[C0]] |
| // CHECK: %[[C1:.+]] = arith.constant 1 |
| // CHECK: %[[DIM1:.+]] = tensor.dim %arg0, %[[C1]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DIM1]], %[[DIM0]]] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x?xf32>) outs([[OUT:%.+]] : tensor<?x?xf32>) |
| // CHECK: ^bb0([[ARG1:%.+]]: f32, [[ARG2:%.+]]: f32) |
| // CHECK: linalg.yield [[ARG1]] |
| // CHECK: } |
| %1 = "tosa.transpose"(%arg0, %0) : (tensor<?x?xf32>, tensor<2xi32>) -> (tensor<?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> |
| |
| // CHECK-LABEL: @reduce_float |
| // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xf32> |
| func @reduce_float(%arg0: tensor<5x4xf32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [4] |
| // CHECK: [[CST0:%.+]] = arith.constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins([[ARG0]] : tensor<5x4xf32>) outs([[FILL]] : tensor<4xf32>) |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32) |
| // CHECK: [[RES:%.+]] = arith.addf %arg1, %arg2 : f32 |
| // CHECK: linalg.yield [[RES]] : f32 |
| // CHECK: tensor.expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<4xf32> into tensor<1x4xf32> |
| %0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5] |
| // CHECK: [[CST0:%.+]] = arith.constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP2]]], iterator_types = ["parallel", "reduction"]} ins([[ARG0]] : tensor<5x4xf32>) outs([[FILL]] : tensor<5xf32>) |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32) |
| // CHECK: [[RES:%.+]] = arith.addf %arg1, %arg2 : f32 |
| // CHECK: linalg.yield [[RES]] : f32 |
| // CHECK: tensor.expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<5xf32> into tensor<5x1xf32> |
| %1 = "tosa.reduce_sum"(%arg0) {axis = 1 : i64} : (tensor<5x4xf32>) -> tensor<5x1xf32> |
| |
| // CHECK: arith.constant 1.0 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.mulf |
| %2 = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| |
| // CHECK: arith.constant 3.40282347E+38 : f32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpf olt |
| // CHECK: select |
| %3 = "tosa.reduce_min"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| |
| // CHECK: arith.constant -3.40282347E+38 : f32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpf ogt |
| // CHECK: select |
| %4 = "tosa.reduce_max"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> |
| |
| // CHECK-LABEL: @reduce_int |
| // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xi32> |
| func @reduce_int(%arg0: tensor<5x4xi32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [4] |
| // CHECK: [[CST0:%.+]] = arith.constant 0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins([[ARG0]] : tensor<5x4xi32>) outs([[FILL]] : tensor<4xi32>) |
| // CHECK: ^bb0(%arg1: i32, %arg2: i32) |
| // CHECK: [[RES:%.+]] = arith.addi %arg1, %arg2 : i32 |
| // CHECK: linalg.yield [[RES]] : i32 |
| // CHECK: tensor.expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<4xi32> into tensor<1x4xi32> |
| %0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5] |
| // CHECK: [[CST0:%.+]] = arith.constant 0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP2]]], iterator_types = ["parallel", "reduction"]} ins([[ARG0]] : tensor<5x4xi32>) outs([[FILL]] : tensor<5xi32>) |
| // CHECK: ^bb0(%arg1: i32, %arg2: i32) |
| // CHECK: [[RES:%.+]] = arith.addi %arg1, %arg2 : i32 |
| // CHECK: linalg.yield [[RES]] : i32 |
| // CHECK: tensor.expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<5xi32> into tensor<5x1xi32> |
| %1 = "tosa.reduce_sum"(%arg0) {axis = 1 : i64} : (tensor<5x4xi32>) -> tensor<5x1xi32> |
| |
| // CHECK: arith.constant 1 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.muli |
| %2 = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| |
| // CHECK: arith.constant 2147483647 : i32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpi slt |
| // CHECK: select |
| %3 = "tosa.reduce_min"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| |
| // CHECK: arith.constant -2147483648 : i32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: arith.cmpi sgt |
| // CHECK: select |
| %4 = "tosa.reduce_max"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> |
| |
| // CHECK-LABEL: @reduce_bool |
| // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xi1> |
| func @reduce_bool(%arg0: tensor<5x4xi1>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [4] |
| // CHECK: [[CST0:%.+]] = arith.constant true |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins([[ARG0]] : tensor<5x4xi1>) outs([[FILL]] : tensor<4xi1>) |
| // CHECK: ^bb0(%arg1: i1, %arg2: i1) |
| // CHECK: [[RES:%.+]] = arith.andi %arg1, %arg2 : i1 |
| // CHECK: linalg.yield [[RES]] : i1 |
| // CHECK: tensor.expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<4xi1> into tensor<1x4xi1> |
| %0 = "tosa.reduce_all"(%arg0) {axis = 0 : i64} : (tensor<5x4xi1>) -> tensor<1x4xi1> |
| |
| // CHECK: arith.constant false |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: or |
| %1 = "tosa.reduce_any"(%arg0) {axis = 0 : i64} : (tensor<5x4xi1>) -> tensor<1x4xi1> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @concat |
| func @concat(%arg0: tensor<5x1xf32>, %arg1: tensor<6x1xf32>) -> () { |
| // CHECK: [[AXIS:%.+]] = arith.constant 0 |
| // CHECK: [[STRIDE:%.+]] = arith.constant 1 |
| // CHECK: [[OFFSET:%.+]] = arith.constant 0 : index |
| // CHECK: [[IDX0:%.+]] = arith.constant 0 : index |
| // CHECK: [[IDX1:%.+]] = arith.constant 1 : index |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [11, 1] |
| // CHECK: [[CST:%.+]] = arith.constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]]) |
| // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]][0, 0] [5, 1] [1, 1] |
| // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg1 into [[INSERT0]][5, 0] [6, 1] [1, 1] |
| %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i64} : (tensor<5x1xf32>, tensor<6x1xf32>) -> (tensor<11x1xf32>) |
| |
| // CHECK: [[AXIS:%.+]] = arith.constant 1 |
| // CHECK: [[STRIDE:%.+]] = arith.constant 1 |
| // CHECK: [[OFFSET:%.+]] = arith.constant 0 : index |
| // CHECK: [[IDX0:%.+]] = arith.constant 0 : index |
| // CHECK: [[IDX1:%.+]] = arith.constant 1 : index |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 2] |
| // CHECK: [[CST:%.+]] = arith.constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]]) |
| // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]][0, 0] [5, 1] [1, 1] |
| // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg0 into [[INSERT0]][0, 1] [5, 1] [1, 1] |
| %1 = "tosa.concat"(%arg0, %arg0) { axis = 1 : i64} : (tensor<5x1xf32>, tensor<5x1xf32>) -> (tensor<5x2xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_i8 |
| func @rescale_i8(%arg0 : tensor<2xi8>) -> () { |
| // CHECK: [[C0:%.+]] = arith.constant 19689 |
| // CHECK: [[C1:%.+]] = arith.constant 15 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): |
| // CHECK: [[C17:%.+]] = arith.constant 17 |
| // CHECK: [[C22:%.+]] = arith.constant 22 |
| // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = arith.cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[MAXLT:%.+]] = arith.cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] |
| // CHECK-DAG: linalg.yield [[TRUNC]] |
| %0 = "tosa.rescale"(%arg0) {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xi8>) -> (tensor<2xi8>) |
| |
| // CHECK: [[C0:%.+]] = arith.constant 19689 |
| // CHECK: [[C1:%.+]] = arith.constant 15 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xi8>) outs([[INIT]] : tensor<2xui8>) |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: ui8): |
| // CHECK: [[C17:%.+]] = arith.constant 17 |
| // CHECK: [[C22:%.+]] = arith.constant 22 |
| // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = arith.constant 0 |
| // CHECK-DAG: [[CMAX:%.+]] = arith.constant 255 |
| // CHECK-DAG: [[MINLT:%.+]] = arith.cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[MAXLT:%.+]] = arith.cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] |
| // CHECK-DAG: [[CAST:%.+]] = builtin.unrealized_conversion_cast [[TRUNC]] : i8 to ui8 |
| // CHECK: linalg.yield [[CAST]] |
| %1 = "tosa.rescale"(%arg0) {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xi8>) -> (tensor<2xui8>) |
| |
| // CHECK: return |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_ui8 |
| func @rescale_ui8(%arg0 : tensor<2xui8>) -> () { |
| // CHECK: [[C0:%.+]] = arith.constant 19689 |
| // CHECK: [[C1:%.+]] = arith.constant 15 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xui8>) outs([[INIT]] : tensor<2xi8>) |
| // CHECK: ^bb0([[IN:%.+]]: ui8, [[UNUSED:%.+]]: i8): |
| // CHECK: [[C17:%.+]] = arith.constant 17 |
| // CHECK: [[C22:%.+]] = arith.constant 22 |
| // CHECK-DAG: [[CAST:%.+]] = builtin.unrealized_conversion_cast [[IN]] : ui8 to i8 |
| // CHECK-DAG: [[IN32:%.+]] = arith.extui [[CAST]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = arith.cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[MAXLT:%.+]] = arith.cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] |
| // CHECK: linalg.yield [[TRUNC]] |
| %0 = "tosa.rescale"(%arg0) {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xui8>) -> (tensor<2xi8>) |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_per_channel |
| func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) { |
| // CHECK: [[MULTIPLIERS:%.+]] = arith.constant dense<[42, 43, 0]> |
| // CHECK: [[SHIFTS:%.+]] = arith.constant dense<[14, 15, 0]> |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [3] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$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 243 |
| // CHECK: [[C252:%.+]] = arith.constant 252 |
| |
| // CHECK-DAG: [[IN32:%.+]] = arith.extsi [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = arith.subi [[IN32]], [[C243]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[MULTIPLIER]], [[SHIFT]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = arith.addi [[SCALED]], [[C252]] |
| // CHECK-DAG: [[CMIN:%.+]] = arith.constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = arith.constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = arith.cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[MAXLT:%.+]] = arith.cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]] |
| // CHECK-DAG: linalg.yield [[TRUNC]] |
| %0 = "tosa.rescale"(%arg0) {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = [42 : i32, 43 : i32, 44 : i32], shift = [14 : i32, 15 : i32, 64 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<3xi8>) -> (tensor<3xi8>) |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<3xi8> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @rescaleDoubleRound |
| func @rescaleDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { |
| // CHECK: linalg.generic |
| // CHECK: "tosa.apply_scale" |
| // CHECK-SAME: {double_round = true} |
| %0 = "tosa.rescale"(%arg0) {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = [19689 : i32], shift = [33 : i32], scale32 = true, double_round = true, per_channel = false} : (tensor<2xi8>) -> (tensor<2xi8>) |
| return %0 : tensor<2xi8> |
| } |
| |
| // CHECK-LABEL: @rescaleUnnecessaryDoubleRound |
| func @rescaleUnnecessaryDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { |
| // CHECK: linalg.generic |
| // CHECK: "tosa.apply_scale" |
| // CHECK-SAME: {double_round = false} |
| %0 = "tosa.rescale"(%arg0) {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = true, double_round = true, per_channel = false} : (tensor<2xi8>) -> (tensor<2xi8>) |
| return %0 : tensor<2xi8> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| |
| // CHECK-LABEL: @reverse |
| func @reverse(%arg0: tensor<5x4xi32>) -> () { |
| // CHECK: %[[C0:.+]] = arith.constant 0 |
| // CHECK: %[[RDIM:.+]] = tensor.dim %arg0, %[[C0]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [5, 4] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]]], iterator_types = ["parallel", "parallel"]} outs(%[[INIT]] : tensor<5x4xi32>) |
| // CHECK-DAG: %[[I0:.+]] = linalg.index 0 |
| // CHECK-DAG: %[[I1:.+]] = linalg.index 1 |
| // CHECK-DAG: %[[SUB1:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[RDIM_MINUS_C1:.+]] = arith.subi %[[RDIM]], %[[SUB1]] |
| // CHECK-DAG: %[[READ_DIM:.+]] = arith.subi %[[RDIM_MINUS_C1]], %[[I0]] |
| // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[READ_DIM]], %[[I1]]] : tensor<5x4xi32> |
| // CHECK: linalg.yield %[[EXTRACT]] |
| %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> |
| |
| // CHECK: %[[C1:.+]] = arith.constant 1 |
| // CHECK: %[[RDIM:.+]] = tensor.dim %arg0, %[[C1]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [5, 4] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$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 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @reverse_dyn |
| func @reverse_dyn(%arg0: tensor<?xi32>) -> () { |
| // CHECK: %[[C0_1:.+]] = arith.constant 0 |
| // CHECK: %[[D0_1:.+]] = tensor.dim %arg0, %[[C0_1]] |
| // CHECK: %[[C0_2:.+]] = arith.constant 0 |
| // CHECK: %[[D0_2:.+]] = tensor.dim %arg0, %[[C0_2]] |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[D0_1]]] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]]], iterator_types = ["parallel"]} outs(%[[INIT]] : tensor<?xi32>) |
| // 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<?xi32> |
| // CHECK: linalg.yield %[[EXTRACT]] |
| %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<?xi32>) -> tensor<?xi32> |
| 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 |
| func @tile(%arg0 : tensor<2x3xi8>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2, 2, 1, 3] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<2x3xi8>) outs([[INIT]] : tensor<2x2x1x3xi8>) |
| // CHECK: linalg.yield %arg1 : i8 |
| // CHECK: tensor.collapse_shape [[GENERIC]] {{\[}}[0, 1, 2], [3]] |
| %0 = "tosa.tile"(%arg0) {multiples = [2, 1]} : (tensor<2x3xi8>) -> (tensor<4x3xi8>) |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 2, 2, 3] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<2x3xi8>) outs([[INIT]] : tensor<1x2x2x3xi8>) |
| // CHECK: linalg.yield %arg1 : i8 |
| // CHECK: tensor.collapse_shape [[GENERIC]] {{\[}}[0, 1], [2, 3]] |
| %1 = "tosa.tile"(%arg0) {multiples = [1, 2]} : (tensor<2x3xi8>) -> (tensor<2x6xi8>) |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 2, 7, 3] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<2x3xi8>) outs([[INIT]] : tensor<5x2x7x3xi8>) |
| // CHECK: linalg.yield %arg1 : i8 |
| // CHECK: tensor.collapse_shape [[GENERIC]] {{\[}}[0, 1], [2, 3]] |
| %2 = "tosa.tile"(%arg0) {multiples = [5, 7]} : (tensor<2x3xi8>) -> (tensor<10x21xi8>) |
| |
| return |
| } |
| |
| // ----- |
| |
| func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) { |
| %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // TODO: Output contains multiple "arith.constant 1 : index". |
| // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index |
| // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index |
| // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index |
| // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index |
| // CHECK-DAG: [[CST:%.+]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: linalg.pad_tensor %arg0 low{{\[}}%{{.*}}, [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]] { |
| // CHECK: ^bb0(%arg1: index, %arg2: index): // no predecessors |
| // CHECK: linalg.yield [[CST]] |
| // CHECK: } : tensor<1x2xf32> to tensor<4x9xf32> |
| %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xf32>, tensor<2x2xi32>) -> (tensor<4x9xf32>) |
| return %1 : tensor<4x9xf32> |
| } |
| |
| func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) { |
| %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // CHECK: [[CST:%.+]] = arith.constant 0 : i32 |
| // CHECK: linalg.pad_tensor |
| // CHECK: linalg.yield [[CST]] |
| %1 = "tosa.pad"(%arg0, %0) : (tensor<1x2xi32>, tensor<2x2xi32>) -> (tensor<4x9xi32>) |
| return %1 : tensor<4x9xi32> |
| } |
| |
| func @pad_quant(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) { |
| %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // CHECK: [[CST:%.+]] = arith.constant 42 : i32 |
| // CHECK: linalg.pad_tensor |
| // CHECK: linalg.yield [[CST]] |
| %1 = "tosa.pad"(%arg0, %0) { quantization_info = { input_zp = 42 : i32}} : (tensor<1x2xi32>, tensor<2x2xi32>) -> (tensor<4x9xi32>) |
| return %1 : tensor<4x9xi32> |
| } |
| |
| // ----- |
| |
| func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) { |
| %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // TODO: Output contains multiple "arith.constant 1 : index". |
| // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index |
| // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index |
| // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index |
| // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index |
| // CHECK-DAG: [[CST:%.+]] = arith.constant 4.200000e+01 : f32 |
| // CHECK: linalg.pad_tensor %arg0 low{{\[}}%{{.*}}, [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]] { |
| // CHECK: ^bb0(%arg1: index, %arg2: index): // no predecessors |
| // CHECK: linalg.yield [[CST]] |
| // CHECK: } : tensor<1x2xf32> to tensor<4x9xf32> |
| %1 = arith.constant dense<42.0> : tensor<f32> |
| %2 = "tosa.pad"(%arg0, %0, %1) : (tensor<1x2xf32>, tensor<2x2xi32>, tensor<f32>) -> (tensor<4x9xf32>) |
| return %2 : tensor<4x9xf32> |
| } |
| |
| // ----- |
| |
| // 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: #[[$MAP4:.*]] = affine_map<(d0) -> ()> |
| |
| func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () { |
| // CHECK: [[IDX_INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[IDX_MIN:%.+]] = arith.constant 0 : i32 |
| // CHECK: [[IDX_FILL:%.+]] = linalg.fill([[IDX_MIN]], [[IDX_INIT]]) |
| // CHECK: [[VAL_INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[VAL_MIN:%.+]] = arith.constant -2147483648 |
| // CHECK: [[VAL_FILL:%.+]] = linalg.fill([[VAL_MIN]], [[VAL_INIT]]) |
| // CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins(%arg0 : tensor<3x2xi32>) outs([[IDX_FILL]], [[VAL_FILL]] : tensor<2xi32>, tensor<2xi32>) |
| // CHECK: [[IDX:%.+]] = linalg.index 0 |
| // CHECK: [[CAST:%.+]] = arith.index_cast [[IDX]] |
| // CHECK: [[CMP:%.+]] = arith.cmpi sgt, %arg2, %arg4 |
| // CHECK: [[SELECT_VAL:%.+]] = select [[CMP]], %arg2, %arg4 |
| // CHECK: [[SELECT_IDX:%.+]] = select [[CMP]], [[CAST]], %arg3 |
| // CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]] |
| %0 = "tosa.argmax"(%arg0) { axis = 0 : i64} : (tensor<3x2xi32>) -> (tensor<2xi32>) |
| |
| // CHECK: [[IDX_INIT:%.+]] = linalg.init_tensor [3] |
| // CHECK: [[IDX_MIN:%.+]] = arith.constant 0 : i32 |
| // CHECK: [[IDX_FILL:%.+]] = linalg.fill([[IDX_MIN]], [[IDX_INIT]]) |
| // CHECK: [[VAL_INIT:%.+]] = linalg.init_tensor [3] |
| // CHECK: [[VAL_MIN:%.+]] = arith.constant -2147483648 |
| // CHECK: [[VAL_FILL:%.+]] = linalg.fill([[VAL_MIN]], [[VAL_INIT]]) |
| // CHECK: linalg.generic {indexing_maps = [#map0, #map2, #map2], iterator_types = ["parallel", "reduction"]} ins(%arg0 : tensor<3x2xi32>) 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, %arg2, %arg4 |
| // CHECK: [[SELECT_VAL:%.+]] = select [[CMP]], %arg2, %arg4 |
| // CHECK: [[SELECT_IDX:%.+]] = select [[CMP]], [[CAST]], %arg3 |
| // CHECK: linalg.yield [[SELECT_IDX]], [[SELECT_VAL]] |
| %1 = "tosa.argmax"(%arg0) { axis = 1 : i64} : (tensor<3x2xi32>) -> (tensor<3xi32>) |
| |
| // CHECK: arith.constant -3.40282347E+38 : f32 |
| // CHECK: linalg.index |
| // CHECK: arith.index_cast |
| // CHECK: arith.cmpf ogt |
| // CHECK: select |
| // CHECK: select |
| // CHECK: linalg.yield |
| %2 = "tosa.argmax"(%arg1) { axis = 0 : i64} : (tensor<6xf32>) -> (tensor<i32>) |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @gather_float |
| func @gather_float(%arg0: tensor<2x3x2xf32>, %arg1: tensor<2x3xi32>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [2, 3, 2] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map0, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%arg1 : tensor<2x3xi32>) outs(%[[INIT]] : tensor<2x3x2xf32>) |
| // CHECK: ^bb0(%[[ARG0:.+]]: i32, %[[ARG1:.+]]: f32) |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[CAST:.+]] = arith.index_cast %[[ARG0]] |
| // 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_int |
| func @gather_int(%arg0: tensor<2x3x2xi32>, %arg1: tensor<2x3xi32>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [2, 3, 2] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#map0, #map1], iterator_types = ["parallel", "parallel", "parallel"]} ins(%arg1 : tensor<2x3xi32>) outs(%[[INIT]] : tensor<2x3x2xi32>) |
| // CHECK: ^bb0(%[[ARG0:.+]]: i32, %[[ARG1:.+]]: i32) |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[CAST:.+]] = arith.index_cast %[[ARG0]] |
| // 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 |
| func @table8(%arg0: tensor<6xi8>, %arg1: tensor<512xi8>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [6] |
| // 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 |
| func @table16(%arg0: tensor<6xi16>, %arg1: tensor<513xi16>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [6] |
| // 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: @resize_nearest |
| func @resize_nearest(%input: tensor<1x2x2x1xf32>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 4, 4, 1] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[IDX1:.+]] = linalg.index 1 |
| // CHECK: %[[IDX2:.+]] = linalg.index 2 |
| // CHECK: %[[IDX3:.+]] = linalg.index 3 |
| // CHECK-DAG: %[[XYMIN:.+]] = arith.constant 0 |
| // CHECK-DAG: %[[YMAX:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[XMAX:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[Y:.+]] = arith.index_cast %[[IDX1]] |
| // CHECK-DAG: %[[X:.+]] = arith.index_cast %[[IDX2]] |
| // CHECK-DAG: %[[STRIDEY:.+]] = arith.constant 5.000000e-01 |
| // CHECK-DAG: %[[STRIDEX:.+]] = arith.constant 5.000000e-01 |
| // CHECK-DAG: %[[OFFSETY:.+]] = arith.constant 1.000000e-01 |
| // CHECK-DAG: %[[OFFSETX:.+]] = arith.constant 2.000000e-01 |
| // CHECK-DAG: %[[VAL4:.+]] = arith.uitofp %[[Y]] |
| // CHECK-DAG: %[[VAL5:.+]] = arith.uitofp %[[X]] |
| // CHECK-DAG: %[[VAL6:.+]] = arith.mulf %[[VAL4]], %[[STRIDEY]] |
| // CHECK-DAG: %[[VAL7:.+]] = arith.mulf %[[VAL5]], %[[STRIDEX]] |
| // CHECK-DAG: %[[VAL8:.+]] = arith.addf %[[VAL6]], %[[OFFSETY]] |
| // CHECK-DAG: %[[VAL9:.+]] = arith.addf %[[VAL7]], %[[OFFSETX]] |
| |
| // Find the remainder and integer component of the target index. |
| |
| // CHECK-DAG: %[[VAL10:.+]] = math.floor %[[VAL8]] |
| // CHECK-DAG: %[[VAL11:.+]] = math.floor %[[VAL9]] |
| // CHECK-DAG: %[[VAL12:.+]] = arith.subf %[[VAL8]], %[[VAL10]] |
| // CHECK-DAG: %[[VAL13:.+]] = arith.subf %[[VAL9]], %[[VAL11]] |
| // CHECK-DAG: %[[VAL14:.+]] = arith.fptosi %[[VAL10]] |
| // CHECK-DAG: %[[VAL15:.+]] = arith.fptosi %[[VAL11]] |
| |
| // Round to the nearest index. |
| |
| // CHECK-DAG: %[[ROUND:.+]] = arith.constant 5.000000e-01 |
| // CHECK-DAG: %[[VAL16:.+]] = arith.cmpf oge, %[[VAL12]], %[[ROUND]] |
| // CHECK-DAG: %[[VAL17:.+]] = arith.cmpf oge, %[[VAL13]], %[[ROUND]] |
| // CHECK-DAG: %[[ZERO:.+]] = arith.constant 0 |
| // CHECK-DAG: %[[ONE:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[VAL18:.+]] = select %[[VAL16]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL19:.+]] = select %[[VAL17]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL20:.+]] = arith.addi %[[VAL14]], %[[VAL18]] |
| // CHECK-DAG: %[[VAL21:.+]] = arith.addi %[[VAL15]], %[[VAL19]] |
| |
| // This section applies bound checking to be within the input image. |
| |
| // CHECK-DAG: %[[VAL22:.+]] = arith.cmpi slt, %[[VAL20]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL23:.+]] = select %[[VAL22]], %[[XYMIN]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL24:.+]] = arith.cmpi slt, %[[YMAX]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL25:.+]] = select %[[VAL24]], %[[YMAX]], %[[VAL23]] |
| // CHECK-DAG: %[[VAL26:.+]] = arith.cmpi slt, %[[VAL21]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL27:.+]] = select %[[VAL26]], %[[XYMIN]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL28:.+]] = arith.cmpi slt, %[[XMAX]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL29:.+]] = select %[[VAL28]], %[[XMAX]], %[[VAL27]] |
| |
| // Extract the nearest value using the computed indices. |
| |
| // CHECK-DAG: %[[IDY:.+]] = arith.index_cast %[[VAL25]] |
| // CHECK-DAG: %[[IDX:.+]] = arith.index_cast %[[VAL29]] |
| // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[IDX0]], %[[IDY]], %[[IDX]], %[[IDX3]]] |
| // CHECK: linalg.yield %[[EXTRACT]] |
| %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [0, 0], offset = [0, 0], stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xf32>) -> (tensor<1x4x4x1xf32>) |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @resize_bilinear |
| func @resize_bilinear(%input: tensor<1x2x2x1xf32>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 4, 4, 1] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[IDX1:.+]] = linalg.index 1 |
| // CHECK: %[[IDX2:.+]] = linalg.index 2 |
| // CHECK: %[[IDX3:.+]] = linalg.index 3 |
| // CHECK: %[[XYMIN:.+]] = arith.constant 0 |
| // CHECK: %[[YMAX:.+]] = arith.constant 1 |
| // CHECK: %[[XMAX:.+]] = arith.constant 1 |
| |
| // CHECK: %[[VAL10:.+]] = math.floor %[[VAL8:.+]] |
| // CHECK: %[[VAL11:.+]] = math.floor %[[VAL9:.+]] |
| |
| // CHECK: %[[DY:.+]] = arith.subf %[[VAL8:.+]], %[[VAL10]] |
| // CHECK: %[[DX:.+]] = arith.subf %[[VAL9:.+]], %[[VAL11]] |
| |
| // CHECK: %[[Y0:.+]] = arith.fptosi %[[VAL10]] |
| // CHECK: %[[X0:.+]] = arith.fptosi %[[VAL11]] |
| |
| // Compute the left, right, and top indices for the bilinear interpolation. |
| |
| // CHECK: %[[ONE:.+]] = arith.constant 1 |
| // CHECK: %[[Y1:.+]] = arith.addi %[[Y0]], %[[ONE]] |
| // CHECK: %[[X1:.+]] = arith.addi %[[X0]], %[[ONE]] |
| |
| // Bound check each dimension. |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[Y0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y0]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[YMAX]], %[[Y0]] |
| // CHECK: %[[YLO:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[Y1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y1]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[YMAX]], %[[Y1]] |
| // CHECK: %[[YHI:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[X0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X0]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[XMAX]], %[[X0]] |
| // CHECK: %[[XLO:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[X1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X1]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[XMAX]], %[[X1]] |
| // CHECK: %[[XHI:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // Extract each corner of the bilinear interpolation. |
| |
| // CHECK: %[[YLOI:.+]] = arith.index_cast %[[YLO]] |
| // CHECK: %[[YHII:.+]] = arith.index_cast %[[YHI]] |
| // CHECK: %[[XLOI:.+]] = arith.index_cast %[[XLO]] |
| // CHECK: %[[XHII:.+]] = arith.index_cast %[[XHI]] |
| |
| // CHECK: %[[LOLO:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YLOI]], %[[XLOI]], %[[IDX3]]] |
| // CHECK: %[[LOHI:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YLOI]], %[[XHII]], %[[IDX3]]] |
| // CHECK: %[[HILO:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YHII]], %[[XLOI]], %[[IDX3]]] |
| // CHECK: %[[HIHI:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YHII]], %[[XHII]], %[[IDX3]]] |
| |
| // Compute the bilinear interpolation. |
| |
| // CHECK: %[[ONE:.+]] = arith.constant 1.000000e+00 |
| // CHECK: %[[NDX:.+]] = arith.subf %[[ONE]], %[[DX]] |
| // CHECK: %[[WLOLO:.+]] = arith.mulf %[[LOLO]], %[[NDX]] |
| // CHECK: %[[WLOHI:.+]] = arith.mulf %[[LOHI]], %[[DX]] |
| // CHECK: %[[LO:.+]] = arith.addf %[[WLOLO]], %[[WLOHI]] |
| // CHECK: %[[WHILO:.+]] = arith.mulf %[[HILO]], %[[NDX]] |
| // CHECK: %[[WHIHI:.+]] = arith.mulf %[[HIHI]], %[[DX]] |
| // CHECK: %[[HI:.+]] = arith.addf %[[WHILO]], %[[WHIHI]] |
| // CHECK: %[[NDY:.+]] = arith.subf %[[ONE]], %[[DY]] |
| // CHECK: %[[WLO:.+]] = arith.mulf %[[LO]], %[[NDY]] |
| // CHECK: %[[WHI:.+]] = arith.mulf %[[HI]], %[[DY]] |
| // CHECK: %[[RESULT:.+]] = arith.addf %[[WLO]], %[[WHI]] |
| // CHECK: linalg.yield %[[RESULT]] |
| %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [0, 0], offset = [0, 0], stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xf32>) -> (tensor<1x4x4x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @resize_nearest_int |
| func @resize_nearest_int(%input: tensor<1x2x2x1xi32>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 4, 4, 1] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[IDX1:.+]] = linalg.index 1 |
| // CHECK: %[[IDX2:.+]] = linalg.index 2 |
| // CHECK: %[[IDX3:.+]] = linalg.index 3 |
| // CHECK-DAG: %[[XYMIN:.+]] = arith.constant 0 |
| // CHECK-DAG: %[[YMAX:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[XMAX:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[Y:.+]] = arith.index_cast %[[IDX1]] |
| // CHECK-DAG: %[[X:.+]] = arith.index_cast %[[IDX2]] |
| // CHECK-DAG: %[[STRIDEY:.+]] = arith.constant 128 |
| // CHECK-DAG: %[[STRIDEX:.+]] = arith.constant 128 |
| // CHECK-DAG: %[[OFFSETY:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[OFFSETX:.+]] = arith.constant 2 |
| // CHECK-DAG: %[[EIGHT:.+]] = arith.constant 8 |
| // CHECK-DAG: %[[VAL4:.+]] = arith.muli %[[Y]], %[[STRIDEY]] |
| // CHECK-DAG: %[[VAL5:.+]] = arith.muli %[[X]], %[[STRIDEX]] |
| // CHECK-DAG: %[[VAL6:.+]] = arith.addi %[[VAL4]], %[[OFFSETY]] |
| // CHECK-DAG: %[[VAL7:.+]] = arith.addi %[[VAL5]], %[[OFFSETX]] |
| |
| // Find the remainder and integer component of the target index. |
| |
| |
| // CHECK-DAG: %[[VAL8:.+]] = arith.shrsi %[[VAL6]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL9:.+]] = arith.shrsi %[[VAL7]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL10:.+]] = arith.shli %[[VAL8]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL11:.+]] = arith.shli %[[VAL9]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL12:.+]] = arith.subi %[[VAL6]], %[[VAL10]] |
| // CHECK-DAG: %[[VAL13:.+]] = arith.subi %[[VAL7]], %[[VAL11]] |
| |
| // Round to the nearest index. |
| |
| // CHECK-DAG: %[[ROUND:.+]] = arith.constant 128 |
| // CHECK-DAG: %[[VAL16:.+]] = arith.cmpi sge, %[[VAL12]], %[[ROUND]] |
| // CHECK-DAG: %[[VAL17:.+]] = arith.cmpi sge, %[[VAL13]], %[[ROUND]] |
| // CHECK-DAG: %[[ZERO:.+]] = arith.constant 0 |
| // CHECK-DAG: %[[ONE:.+]] = arith.constant 1 |
| // CHECK-DAG: %[[VAL18:.+]] = select %[[VAL16]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL19:.+]] = select %[[VAL17]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL20:.+]] = arith.addi %[[VAL8]], %[[VAL18]] |
| // CHECK-DAG: %[[VAL21:.+]] = arith.addi %[[VAL9]], %[[VAL19]] |
| |
| // This section applies bound checking to be within the input image. |
| |
| // CHECK-DAG: %[[VAL22:.+]] = arith.cmpi slt, %[[VAL20]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL23:.+]] = select %[[VAL22]], %[[XYMIN]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL24:.+]] = arith.cmpi slt, %[[YMAX]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL25:.+]] = select %[[VAL24]], %[[YMAX]], %[[VAL23]] |
| // CHECK-DAG: %[[VAL26:.+]] = arith.cmpi slt, %[[VAL21]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL27:.+]] = select %[[VAL26]], %[[XYMIN]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL28:.+]] = arith.cmpi slt, %[[XMAX]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL29:.+]] = select %[[VAL28]], %[[XMAX]], %[[VAL27]] |
| |
| // Extract the nearest value using the computed indices. |
| |
| // CHECK-DAG: %[[IDY:.+]] = arith.index_cast %[[VAL25]] |
| // CHECK-DAG: %[[IDX:.+]] = arith.index_cast %[[VAL29]] |
| // CHECK: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[IDX0]], %[[IDY]], %[[IDX]], %[[IDX3]]] |
| // CHECK: linalg.yield %[[EXTRACT]] |
| %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [128, 128], offset = [1, 2], stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xi32>) -> (tensor<1x4x4x1xi32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @resize_bilinear_int |
| func @resize_bilinear_int(%input: tensor<1x2x2x1xi8>) -> () { |
| // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 4, 4, 1] |
| // CHECK: %[[GENERIC:.+]] = linalg.generic |
| |
| // CHECK: %[[IDX0:.+]] = linalg.index 0 |
| // CHECK: %[[IDX3:.+]] = linalg.index 3 |
| |
| // CHECK: %[[XYMIN:.+]] = arith.constant 0 |
| // CHECK: %[[YMAX:.+]] = arith.constant 1 |
| // CHECK: %[[XMAX:.+]] = arith.constant 1 |
| |
| // CHECK: %[[Y0:.+]] = arith.shrsi |
| // CHECK: %[[X0:.+]] = arith.shrsi |
| // CHECK: %[[ROUNDY:.+]] = arith.shli %[[Y0]] |
| // CHECK: %[[ROUNDX:.+]] = arith.shli %[[X0]] |
| // CHECK: %[[DY:.+]] = arith.subi %10, %[[ROUNDY]] |
| // CHECK: %[[DX:.+]] = arith.subi %11, %[[ROUNDX]] |
| |
| // Compute the left, right, and top indices for the bilinear interpolation. |
| |
| // CHECK: %[[ONE:.+]] = arith.constant 1 |
| // CHECK: %[[Y1:.+]] = arith.addi %[[Y0]], %[[ONE]] |
| // CHECK: %[[X1:.+]] = arith.addi %[[X0]], %[[ONE]] |
| |
| // Bound check each dimension. |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[Y0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y0]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[YMAX]], %[[Y0]] |
| // CHECK: %[[YLO:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[Y1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y1]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[YMAX]], %[[Y1]] |
| // CHECK: %[[YHI:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[X0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X0]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[XMAX]], %[[X0]] |
| // CHECK: %[[XLO:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[X1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X1]] |
| // CHECK: %[[PRED:.+]] = arith.cmpi slt, %[[XMAX]], %[[X1]] |
| // CHECK: %[[XHI:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // Extract each corner of the bilinear interpolation. |
| |
| // CHECK: %[[YLOI:.+]] = arith.index_cast %[[YLO]] |
| // CHECK: %[[YHII:.+]] = arith.index_cast %[[YHI]] |
| // CHECK: %[[XLOI:.+]] = arith.index_cast %[[XLO]] |
| // CHECK: %[[XHII:.+]] = arith.index_cast %[[XHI]] |
| |
| // CHECK: %[[LOLO:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YLOI]], %[[XLOI]], %[[IDX3]]] |
| // CHECK: %[[LOHI:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YLOI]], %[[XHII]], %[[IDX3]]] |
| // CHECK: %[[HILO:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YHII]], %[[XLOI]], %[[IDX3]]] |
| // CHECK: %[[HIHI:.+]] = tensor.extract %arg0[%[[IDX0]], %[[YHII]], %[[XHII]], %[[IDX3]]] |
| |
| // CHECK: %[[XLOLO:.+]] = arith.extsi %[[LOLO]] |
| // CHECK: %[[XLOHI:.+]] = arith.extsi %[[LOHI]] |
| // CHECK: %[[XHILO:.+]] = arith.extsi %[[HILO]] |
| // CHECK: %[[XHIHI:.+]] = arith.extsi %[[HIHI]] |
| |
| // Compute the bilinear interpolation. |
| |
| // CHECK: %[[SCALE:.+]] = arith.constant 256 |
| // CHECK: %[[NDX:.+]] = arith.subi %[[SCALE]], %[[DX]] |
| // CHECK: %[[WLOLO:.+]] = arith.muli %[[XLOLO]], %[[NDX]] |
| // CHECK: %[[WLOHI:.+]] = arith.muli %[[XLOHI]], %[[DX]] |
| // CHECK: %[[LO:.+]] = arith.addi %[[WLOLO]], %[[WLOHI]] |
| // CHECK: %[[WHILO:.+]] = arith.muli %[[XHILO]], %[[NDX]] |
| // CHECK: %[[WHIHI:.+]] = arith.muli %[[XHIHI]], %[[DX]] |
| // CHECK: %[[HI:.+]] = arith.addi %[[WHILO]], %[[WHIHI]] |
| // CHECK: %[[NDY:.+]] = arith.subi %[[SCALE]], %[[DY]] |
| // CHECK: %[[WLO:.+]] = arith.muli %[[LO]], %[[NDY]] |
| // CHECK: %[[WHI:.+]] = arith.muli %[[HI]], %[[DY]] |
| // CHECK: %[[RESULT:.+]] = arith.addi %[[WLO]], %[[WHI]] |
| // CHECK: linalg.yield %[[RESULT]] |
| %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [128, 128], offset = [1, 2], stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xi8>) -> (tensor<1x4x4x1xi32>) |
| return |
| } |
