| // RUN: mlir-opt --split-input-file --tosa-to-linalg-on-tensors %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]], #[[$MAP0]]], iterator_types = []} ins(%arg0 : tensor<f32>) outs([[INIT]] : tensor<f32>) { |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32): |
| // CHECK: [[ELEMENT:%.+]] = absf %arg1 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<f32> |
| |
| %0 = "tosa.abs"(%arg0) : (tensor<f32>) -> tensor<f32> |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<f32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$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:%.+]] = absf %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]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<2x3xf32>) outs([[INIT]] : tensor<2x3xf32>) { |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32): |
| // CHECK: [[ELEMENT:%.+]] = absf %arg1 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2x3xf32> |
| %0 = "tosa.abs"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32> |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<2x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> ()> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @test_broadcast |
| func @test_broadcast(%arg0: tensor<1xf32>, %arg1: tensor<2xf32>) -> tensor<2xf32> { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] : tensor<2xf32> |
| // CHECK: [[RESHAPE:%.+]] = linalg.tensor_collapse_shape %arg0 |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$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:%.+]] = 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:%.+]] = linalg.tensor_collapse_shape %arg1 |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0, [[RESHAPE]] : tensor<2xf32>, tensor<f32>) outs([[INIT]] : tensor<2xf32>) { |
| // CHECK: ^bb0(%arg2: f32, %arg3: f32, %arg4: f32): |
| // CHECK: [[ELEMENT:%.+]] = addf %arg2, %arg3 : f32 |
| // CHECK: linalg.yield [[ELEMENT]] : f32 |
| // CHECK: } -> tensor<2xf32> |
| %0 = "tosa.add"(%arg0, %arg1) : (tensor<2xf32>, tensor<1xf32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-DAG: #[[$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:%.+]] = linalg.tensor_collapse_shape %arg0 {{\[}}[0, 1]] |
| // CHECK: [[RESHAPE2:%.+]] = linalg.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:%.+]] = 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> |
| } |
| |
| // ----- |
| |
| func @test_abs(%arg0: tensor<?xf32>) -> tensor<?xf32> { |
| // expected-error @+1 {{failed to legalize operation 'tosa.abs'}} |
| %0 = "tosa.abs"(%arg0) : (tensor<?xf32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| |
| // ----- |
| |
| // 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: absf |
| %1 = "tosa.abs"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: addf |
| %2 = "tosa.add"(%0, %0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: subf |
| %3 = "tosa.sub"(%0, %1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: mulf |
| %4 = "tosa.mul"(%0, %1) {shift = 0 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: 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: cmpf |
| %10 = "tosa.greater"(%0, %1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpf |
| %11 = "tosa.greater_equal"(%0, %1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: 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: cmpf |
| // CHECK: select |
| %14 = "tosa.maximum"(%0, %1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: 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: 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: cmpf |
| // CHECK: select |
| %19 = "tosa.reluN"(%0) {max_int = 5 : i64, max_fp = 5.0 : f32} : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: negf |
| // CHECK: exp |
| // CHECK: addf |
| // CHECK: divf |
| %20 = "tosa.sigmoid"(%0) : (tensor<1xf32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 0.000000e+00 |
| // CHECK: constant 5.000000e-01 |
| // CHECK: constant -2.14748365E+9 |
| // CHECK: constant 2.14748365E+9 |
| // CHECK: addf |
| // CHECK: subf |
| // CHECK: cmpf olt |
| // CHECK: select |
| // CHECK: cmpf olt |
| // CHECK: select |
| // CHECK: cmpf olt |
| // CHECK: select |
| // CHECK: fptosi |
| %21 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 0 |
| // CHECK: cmpf |
| %22 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: fptrunc |
| %23 = "tosa.cast"(%0) : (tensor<1xf32>) -> tensor<1xf16> |
| |
| // CHECK: linalg.generic |
| // CHECK: 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: fpext |
| %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: sext |
| // CHECK: sext |
| // CHECK: muli |
| %0 = "tosa.mul"(%arg0, %arg0) {shift = 0 : i32} : (tensor<1xi16>, tensor<1xi16>) -> tensor<1xi32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_simple_i32 |
| func @test_simple_i32(%arg0: tensor<1xi32>) -> () { |
| // CHECK: linalg.generic |
| // CHECK: addi |
| %0 = "tosa.add"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: subi |
| %1 = "tosa.sub"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: muli |
| %2 = "tosa.mul"(%arg0, %arg0) {shift = 0 : i32} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 2 |
| // CHECK: apply_scale |
| %3 = "tosa.mul"(%arg0, %arg0) {shift = 2 : i32} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: divi |
| %4 = "tosa.div"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: [[ZERO:%.+]] = constant 0 |
| // CHECK: 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: xor |
| %8 = "tosa.bitwise_xor"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: shift_left |
| %9 = "tosa.logical_left_shift"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: shift_right_unsigned |
| %10 = "tosa.logical_right_shift"(%arg0, %arg0) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: shift_right_signed |
| %11 = "tosa.arithmetic_right_shift"(%arg0, %arg0) {round = 0 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 1 |
| // CHECK: constant 0 |
| // CHECK: constant true |
| // CHECK: cmpi |
| // CHECK: subi |
| // CHECK: shift_right_signed |
| // CHECK: trunci |
| // CHECK: and |
| // CHECK: and |
| // CHECK: zexti |
| // CHECK: addi |
| %12 = "tosa.arithmetic_right_shift"(%arg0, %arg0) {round = 1 : i1} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| %13 = "tosa.greater"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| %14 = "tosa.greater_equal"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: select |
| %15 = "tosa.select"(%13, %0, %1) : (tensor<1xi1>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| // CHECK: select |
| %16 = "tosa.maximum"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| // CHECK: select |
| %17 = "tosa.minimum"(%0, %1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| // 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<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: cmpi |
| // CHECK: select |
| %19 = "tosa.reluN"(%0) {max_int = 5 : i64, max_fp = 5.0 : f32} : (tensor<1xi32>) -> tensor<1xi32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant -32768 |
| // CHECK: constant 32767 |
| // CHECK: cmpi slt |
| // CHECK: select |
| // CHECK: cmpi slt |
| // CHECK: select |
| // CHECK: trunci |
| %20 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi16> |
| |
| // CHECK: linalg.generic |
| // CHECK: sexti |
| %21 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi64> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 0 |
| // CHECK: cmpi |
| %22 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: sitofp |
| %23 = "tosa.cast"(%0) : (tensor<1xi32>) -> tensor<1xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant 0 |
| // CHECK: cmpi sgt |
| // CHECK: subi |
| // CHECK: select |
| %24 = "tosa.abs"(%arg0) : (tensor<1xi32>) -> tensor<1xi32> |
| |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @test_i8 |
| func @test_i8(%arg0: tensor<1xi8>) -> () { |
| // CHECK: linalg.generic |
| // CHECK-DAG: %[[C127:.+]] = constant -127 |
| // CHECK-DAG: %[[C126:.+]] = constant 126 |
| // CHECK-DAG: %[[CMP1:.+]] = cmpi slt, %arg1, %[[C127]] |
| // CHECK-DAG: %[[SEL1:.+]] = select %[[CMP1]], %[[C127]] |
| // CHECK-DAG: %[[CMP2:.+]] = 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:.+]] = constant -128 |
| // CHECK-DAG: %[[C127:.+]] = constant 127 |
| // CHECK-DAG: %[[CMP1:.+]] = cmpi slt, %arg1, %[[C128]] |
| // CHECK-DAG: %[[SEL1:.+]] = select %[[CMP1]], %[[C128]] |
| // CHECK-DAG: %[[CMP2:.+]] = 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: xor |
| %2 = "tosa.logical_xor"(%arg0, %arg1) : (tensor<1xi1>, tensor<1xi1>) -> tensor<1xi1> |
| |
| // CHECK: linalg.generic |
| // CHECK: constant true |
| // CHECK: xor |
| %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:%.+]] = constant 0 |
| // CHECK: [[EXT:%.+]] = sexti %arg1 : i8 to i16 |
| // CHECK: [[SUB:%.+]] = subi [[ZERO]], [[EXT]] |
| // CHECK: [[MIN:%.+]] = constant -128 |
| // CHECK: [[MAX:%.+]] = constant 127 |
| // CHECK: [[PRED1:%.+]] = cmpi slt, [[SUB]], [[MIN]] |
| // CHECK: [[LBOUND:%.+]] = select [[PRED1]], [[MIN]], [[SUB]] |
| // CHECK: [[PRED2:%.+]] = cmpi slt, [[MAX]], [[SUB]] |
| // CHECK: [[UBOUND:%.+]] = select [[PRED2]], [[MAX]], [[LBOUND]] |
| // CHECK: [[TRUNC:%.+]] = 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:%.+]] = sexti %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:%.+]] = sexti %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:%.+]] = linalg.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_uprank |
| func @test_reshape_uprank(%arg0: tensor<6xf32>) -> tensor<2x3xf32> { |
| // CHECK: [[RESHAPE:%.+]] = linalg.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_samerank |
| func @test_reshape_samerank(%arg0: tensor<3x2xf32>) -> tensor<2x3xf32> { |
| // CHECK-SAME: (%[[ARG0:.*]]: tensor<3x2xf32>) |
| // CHECK-NEXT: %[[RESHAPE1:.*]] = linalg.tensor_collapse_shape %[[ARG0]] {{\[}}[0, 1]] |
| // CHECK-NEXT: %[[RESHAPE2:.*]] = linalg.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_downrank_6D |
| func @test_reshape_downrank_6D(%arg0: tensor<1x2x3x5x7x11xf32>) -> tensor<6x5x77xf32> { |
| // CHECK: linalg.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_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 = 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-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> |
| |
| // CHECK-LABEL: @reduce_float |
| // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xf32> |
| func @reduce_float(%arg0: tensor<5x4xf32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [4] |
| // CHECK: [[CST0:%.+]] = constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins([[ARG0]] : tensor<5x4xf32>) outs([[FILL]] : tensor<4xf32>) |
| // CHECK: ^bb0(%arg1: f32, %arg2: f32) |
| // CHECK: [[RES:%.+]] = addf %arg1, %arg2 : f32 |
| // CHECK: linalg.yield [[RES]] : f32 |
| // CHECK: linalg.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:%.+]] = 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:%.+]] = addf %arg1, %arg2 : f32 |
| // CHECK: linalg.yield [[RES]] : f32 |
| // CHECK: linalg.tensor_expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<5xf32> into tensor<5x1xf32> |
| %1 = "tosa.reduce_sum"(%arg0) {axis = 1 : i64} : (tensor<5x4xf32>) -> tensor<5x1xf32> |
| |
| // CHECK: constant 1.0 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: mulf |
| %2 = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| |
| // CHECK: constant 3.40282347E+38 : f32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: cmpf olt |
| // CHECK: select |
| %3 = "tosa.reduce_min"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> |
| |
| // CHECK: constant -3.40282347E+38 : f32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: 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: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> |
| |
| // CHECK-LABEL: @reduce_int |
| // CHECK-SAME: [[ARG0:%.+]]: tensor<5x4xi32> |
| func @reduce_int(%arg0: tensor<5x4xi32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [4] |
| // CHECK: [[CST0:%.+]] = constant 0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]]) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]} ins([[ARG0]] : tensor<5x4xi32>) outs([[FILL]] : tensor<4xi32>) |
| // CHECK: ^bb0(%arg1: i32, %arg2: i32) |
| // CHECK: [[RES:%.+]] = addi %arg1, %arg2 : i32 |
| // CHECK: linalg.yield [[RES]] : i32 |
| // CHECK: linalg.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:%.+]] = 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:%.+]] = addi %arg1, %arg2 : i32 |
| // CHECK: linalg.yield [[RES]] : i32 |
| // CHECK: linalg.tensor_expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<5xi32> into tensor<5x1xi32> |
| %1 = "tosa.reduce_sum"(%arg0) {axis = 1 : i64} : (tensor<5x4xi32>) -> tensor<5x1xi32> |
| |
| // CHECK: constant 1 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: muli |
| %2 = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| |
| // CHECK: constant 2147483647 : i32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: cmpi slt |
| // CHECK: select |
| %3 = "tosa.reduce_min"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<1x4xi32> |
| |
| // CHECK: constant -2147483648 : i32 |
| // CHECK: linalg.fill |
| // CHECK: linalg.generic |
| // CHECK: 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:%.+]] = 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:%.+]] = and %arg1, %arg2 : i1 |
| // CHECK: linalg.yield [[RES]] : i1 |
| // CHECK: linalg.tensor_expand_shape [[GENERIC]] {{\[}}[0, 1]] : tensor<4xi1> into tensor<1x4xi1> |
| %0 = "tosa.reduce_all"(%arg0) {axis = 0 : i64} : (tensor<5x4xi1>) -> tensor<1x4xi1> |
| |
| // CHECK: 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:%.+]] = constant 0 |
| // CHECK: [[STRIDE:%.+]] = constant 1 |
| // CHECK: [[OFFSET:%.+]] = constant 0 : index |
| // CHECK: [[IDX0:%.+]] = constant 0 : index |
| // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[IDX0]] |
| // CHECK: [[IDX1:%.+]] = constant 1 : index |
| // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[IDX1]] |
| // CHECK: [[ARG1_AXIS:%.+]] = tensor.dim %arg1, [[AXIS]] |
| // CHECK: [[RESULT_AXIS:%.+]] = addi [[ARG0_DIM0]], [[ARG1_AXIS]] |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [11, 1] |
| // CHECK: [[CST:%.+]] = constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]]) |
| // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[AXIS]] |
| // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]]{{\[}}[[OFFSET]], [[OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]] |
| // CHECK: [[NEW_OFFSET:%.+]] = addi [[OFFSET]], [[ARG0_DIM0]] |
| // CHECK: [[ARG1_DIM0:%.+]] = tensor.dim %arg1, [[AXIS]] |
| // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg1 into [[INSERT0]]{{\[}}[[NEW_OFFSET]], [[OFFSET]]] {{\[}}[[ARG1_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]] |
| %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i64} : (tensor<5x1xf32>, tensor<6x1xf32>) -> (tensor<11x1xf32>) |
| |
| // CHECK: [[AXIS:%.+]] = constant 1 |
| // CHECK: [[STRIDE:%.+]] = constant 1 |
| // CHECK: [[OFFSET:%.+]] = constant 0 : index |
| // CHECK: [[IDX0:%.+]] = constant 0 : index |
| // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[IDX0]] |
| // CHECK: [[IDX1:%.+]] = constant 1 : index |
| // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[IDX1]] |
| // CHECK: [[ARG1_AXIS:%.+]] = tensor.dim %arg0, [[AXIS]] |
| // CHECK: [[RESULT_AXIS:%.+]] = addi [[ARG0_DIM1]], [[ARG1_AXIS]] |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 2] |
| // CHECK: [[CST:%.+]] = constant 0.0 |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]]) |
| // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[AXIS]] |
| // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]]{{\[}}[[OFFSET]], [[OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]] |
| // CHECK: [[NEW_OFFSET:%.+]] = addi [[OFFSET]], [[ARG0_DIM1]] |
| // CHECK: [[ARG1_DIM1:%.+]] = tensor.dim %arg0, [[AXIS]] |
| // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg0 into [[INSERT0]]{{\[}}[[OFFSET]], [[NEW_OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG1_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]] |
| %1 = "tosa.concat"(%arg0, %arg0) { axis = 1 : i64} : (tensor<5x1xf32>, tensor<5x1xf32>) -> (tensor<5x2xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_i8 |
| func @rescale_i8(%arg0 : tensor<2xi8>) -> () { |
| // CHECK: [[C0:%.+]] = constant 19689 |
| // CHECK: [[C1:%.+]] = constant 15 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): |
| // CHECK: [[C17:%.+]] = constant 17 |
| // CHECK: [[C22:%.+]] = constant 22 |
| // CHECK-DAG: [[IN32:%.+]] = sexti [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[MAXLT:%.+]] = cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = trunci [[BOUNDED]] |
| // CHECK-DAG: linalg.yield [[TRUNC]] |
| %0 = "tosa.rescale"(%arg0) {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xi8>) -> (tensor<2xi8>) |
| |
| // CHECK: [[C0:%.+]] = constant 19689 |
| // CHECK: [[C1:%.+]] = constant 15 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xi8>) outs([[INIT]] : tensor<2xui8>) |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: ui8): |
| // CHECK: [[C17:%.+]] = constant 17 |
| // CHECK: [[C22:%.+]] = constant 22 |
| // CHECK-DAG: [[IN32:%.+]] = sexti [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = constant 0 |
| // CHECK-DAG: [[CMAX:%.+]] = constant 255 |
| // CHECK-DAG: [[MINLT:%.+]] = cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[MAXLT:%.+]] = cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = trunci [[BOUNDED]] |
| // CHECK-DAG: [[CAST:%.+]] = builtin.unrealized_conversion_cast [[TRUNC]] : i8 to ui8 |
| // CHECK: linalg.yield [[CAST]] |
| %1 = "tosa.rescale"(%arg0) {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = [19689 : i32], shift = [15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xi8>) -> (tensor<2xui8>) |
| |
| // CHECK: return |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_ui8 |
| func @rescale_ui8(%arg0 : tensor<2xui8>) -> () { |
| // CHECK: [[C0:%.+]] = constant 19689 |
| // CHECK: [[C1:%.+]] = 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:%.+]] = constant 17 |
| // CHECK: [[C22:%.+]] = constant 22 |
| // CHECK-DAG: [[CAST:%.+]] = builtin.unrealized_conversion_cast [[IN]] : ui8 to i8 |
| // CHECK-DAG: [[IN32:%.+]] = zexti [[CAST]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = subi [[IN32]], [[C17]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[C0]], [[C1]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = addi [[SCALED]], [[C22]] |
| // CHECK-DAG: [[CMIN:%.+]] = constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[MAXLT:%.+]] = cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = 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: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: @rescale_per_channel |
| func @rescale_per_channel(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) { |
| // CHECK: [[MULTIPLIERS:%.+]] = constant dense<[42, 43]> |
| // CHECK: [[SHIFTS:%.+]] = constant dense<[14, 15]> |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0, [[MULTIPLIERS]], [[SHIFTS]] : tensor<2xi8>, tensor<2xi32>, tensor<2xi8>) outs([[INIT]] : tensor<2xi8>) |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[MULTIPLIER:%.+]]: i32, [[SHIFT:%.+]]: i8, [[UNUSED:%.+]]: i8): |
| // CHECK: [[C243:%.+]] = constant 243 |
| // CHECK: [[C252:%.+]] = constant 252 |
| |
| // CHECK-DAG: [[IN32:%.+]] = sexti [[IN]] |
| // CHECK-DAG: [[IN_ZEROED:%.+]] = subi [[IN32]], [[C243]] |
| // CHECK-DAG: [[SCALED:%.+]] = "tosa.apply_scale"([[IN_ZEROED]], [[MULTIPLIER]], [[SHIFT]]) {double_round = false} |
| // CHECK-DAG: [[SCALED_ZEROED:%.+]] = addi [[SCALED]], [[C252]] |
| // CHECK-DAG: [[CMIN:%.+]] = constant -128 |
| // CHECK-DAG: [[CMAX:%.+]] = constant 127 |
| // CHECK-DAG: [[MINLT:%.+]] = cmpi slt, [[SCALED_ZEROED]], [[CMIN]] |
| // CHECK-DAG: [[MAXLT:%.+]] = cmpi slt, [[CMAX]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[LOWER:%.+]] = select [[MINLT]], [[CMIN]], [[SCALED_ZEROED]] |
| // CHECK-DAG: [[BOUNDED:%.+]] = select [[MAXLT]], [[CMAX]], [[LOWER]] |
| // CHECK-DAG: [[TRUNC:%.+]] = trunci [[BOUNDED]] |
| // CHECK-DAG: linalg.yield [[TRUNC]] |
| %0 = "tosa.rescale"(%arg0) {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = [42 : i32, 43 : i32], shift = [14 : i32, 15 : i32], scale32 = false, double_round = false, per_channel = false} : (tensor<2xi8>) -> (tensor<2xi8>) |
| |
| // CHECK: return [[GENERIC]] |
| return %0 : tensor<2xi8> |
| } |
| |
| // ----- |
| |
| // 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 + 4, d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0, -d1 + 3)> |
| |
| // CHECK-LABEL: @reverse |
| func @reverse(%arg0: tensor<5x4xi32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 4] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<5x4xi32>) outs([[INIT]] : tensor<5x4xi32>) { |
| // CHECK: ^bb0(%arg1: i32, %arg2: i32): |
| // CHECK: linalg.yield %arg1 : i32 |
| %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 4] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<5x4xi32>) outs([[INIT]] : tensor<5x4xi32>) { |
| // CHECK: ^bb0(%arg1: i32, %arg2: i32): |
| // CHECK: linalg.yield %arg1 : i32 |
| %1 = "tosa.reverse"(%arg0) {axis = 1 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> |
| 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: linalg.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: linalg.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: linalg.tensor_collapse_shape [[GENERIC]] {{\[}}[0, 1], [2, 3]] |
| %2 = "tosa.tile"(%arg0) {multiples = [5, 7]} : (tensor<2x3xi8>) -> (tensor<10x21xi8>) |
| |
| return |
| } |
| |
| // ----- |
| |
| |
| // CHECK-LABEL: @matmul |
| func @matmul(%arg0: tensor<1x5x3xf32>, %arg1: tensor<1x3x6xf32>, %arg2: tensor<1x6xf32>) -> (tensor<1x5x6xf32>) { |
| // CHECK: [[C0:%.+]] = constant 0 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 6] |
| // CHECK: [[FILLED:%.+]] = linalg.fill([[C0]], [[INIT]]) : f32, tensor<1x5x6xf32> -> tensor<1x5x6xf32> |
| // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x3xf32>, tensor<1x3x6xf32>) outs([[FILLED]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32> |
| %0 = "tosa.matmul"(%arg0, %arg1) : (tensor<1x5x3xf32>, tensor<1x3x6xf32>) -> (tensor<1x5x6xf32>) |
| return %0 : tensor<1x5x6xf32> |
| } |
| |
| // ----- |
| |
| |
| // CHECK-LABEL: @matmul_quantized |
| func @matmul_quantized(%arg0: tensor<1x5x3xi8>, %arg1: tensor<1x3x6xi8>) -> (tensor<1x5x6xi32>) { |
| // CHECK: [[C0:%.+]] = constant 0 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 6] |
| // CHECK: [[FILLED:%.+]] = linalg.fill([[C0]], [[INIT]]) : i32, tensor<1x5x6xi32> -> tensor<1x5x6xi32> |
| // CHECK: [[ONE:%.+]] = constant 1 |
| // CHECK: [[TWO:%.+]] = constant 2 |
| // CHECK: linalg.quantized_batch_matmul ins(%arg0, %arg1, [[ONE]], [[TWO]] : tensor<1x5x3xi8>, tensor<1x3x6xi8>, i32, i32) outs([[FILLED]] : tensor<1x5x6xi32>) -> tensor<1x5x6xi32> |
| %0 = "tosa.matmul"(%arg0, %arg1) {quantization_info = {a_zp = 1 : i32, b_zp = 2 : i32}} : (tensor<1x5x3xi8>, tensor<1x3x6xi8>) -> (tensor<1x5x6xi32>) |
| return %0 : tensor<1x5x6xi32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d1, d0)> |
| |
| // CHECK-LABEL: @fully_connected |
| func @fully_connected(%arg0: tensor<5x3xf32>, %arg1: tensor<6x3xf32>, %arg2: tensor<6xf32>) -> (tensor<5x6xf32>) { |
| // CHECK: [[INITB:%.+]] = linalg.init_tensor [5, 6] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg2 : tensor<6xf32>) outs([[INITB]] : tensor<5x6xf32>) { |
| // CHECK: ^bb0([[IN:%.+]]: f32, [[UNUSED:%.+]]: f32): |
| // CHECK: linalg.yield [[IN]] : f32 |
| // CHECK: [[INITT:%.+]] = linalg.init_tensor [3, 6] |
| // CHECK: [[TRANSPOSE:%.+]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg1 : tensor<6x3xf32>) outs([[INITT]] |
| // CHECK: ^bb0([[IN:%.+]]: f32, [[UNUSED:%.+]]: f32): |
| // CHECK: linalg.yield [[IN]] : f32 |
| // CHECK: linalg.matmul ins(%arg0, [[TRANSPOSE]] : tensor<5x3xf32>, tensor<3x6xf32>) outs([[GENERIC]] : tensor<5x6xf32>) -> tensor<5x6xf32> |
| %0 = "tosa.fully_connected"(%arg0, %arg1, %arg2) : (tensor<5x3xf32>, tensor<6x3xf32>, tensor<6xf32>) -> (tensor<5x6xf32>) |
| return %0 : tensor<5x6xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d1)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d1, d0)> |
| |
| // CHECK-LABEL: @quantized_fully_connected |
| func @quantized_fully_connected(%arg0: tensor<5x3xi8>, %arg1: tensor<6x3xi8>, %arg2: tensor<6xi32>) -> (tensor<5x6xi32>) { |
| // CHECK: [[INITB:%.+]] = linalg.init_tensor [5, 6] |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg2 : tensor<6xi32>) outs([[INITB]] : tensor<5x6xi32>) { |
| // CHECK: ^bb0([[IN:%.+]]: i32, [[UNUSED:%.+]]: i32): |
| // CHECK: linalg.yield [[IN]] : i32 |
| // CHECK: [[INITT:%.+]] = linalg.init_tensor [3, 6] |
| // CHECK: [[TRANSPOSE:%.+]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg1 : tensor<6x3xi8>) outs([[INITT]] |
| // CHECK: ^bb0([[IN:%.+]]: i8, [[UNUSED:%.+]]: i8): |
| // CHECK: linalg.yield [[IN]] : i8 |
| // CHECK: [[ONE:%.+]] = constant 1 |
| // CHECK: [[TWO:%.+]] = constant 2 |
| // CHECK: linalg.quantized_matmul ins(%arg0, [[TRANSPOSE]], [[ONE]], [[TWO]] : tensor<5x3xi8>, tensor<3x6xi8>, i32, i32) outs([[GENERIC]] : tensor<5x6xi32>) -> tensor<5x6xi32> |
| %0 = "tosa.fully_connected"(%arg0, %arg1, %arg2) {quantization_info = {input_zp = 1:i32, weight_zp = 2:i32}} : (tensor<5x3xi8>, tensor<6x3xi8>, tensor<6xi32>) -> (tensor<5x6xi32>) |
| return %0 : tensor<5x6xi32> |
| } |
| |
| // ----- |
| |
| func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) { |
| %0 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // TODO: Output contains multiple "constant 1 : index". |
| // CHECK: [[INDEX1:%.+]] = constant 1 : index |
| // CHECK: [[INDEX2:%.+]] = constant 2 : index |
| // CHECK: [[INDEX3:%.+]] = constant 3 : index |
| // CHECK: [[INDEX4:%.+]] = constant 4 : index |
| // CHECK: [[CST:%.+]] = 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 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // CHECK: [[CST:%.+]] = 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 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> |
| // CHECK: [[CST:%.+]] = 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> |
| } |
| |
| // ----- |
| |
| // 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: #[[$MAP4:.*]] = affine_map<(d0) -> ()> |
| |
| func @argmax(%arg0 : tensor<3x2xi32>, %arg1 : tensor<6xf32>) -> () { |
| // CHECK: [[IDX_INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[IDX_MIN:%.+]] = constant 0 : i32 |
| // CHECK: [[IDX_FILL:%.+]] = linalg.fill([[IDX_MIN]], [[IDX_INIT]]) |
| // CHECK: [[VAL_INIT:%.+]] = linalg.init_tensor [2] |
| // CHECK: [[VAL_MIN:%.+]] = constant -2147483648 |
| // CHECK: [[VAL_FILL:%.+]] = linalg.fill([[VAL_MIN]], [[VAL_INIT]]) |
| // CHECK: linalg.generic {indexing_maps = [#[[$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:%.+]] = index_cast [[IDX]] |
| // CHECK: [[CMP:%.+]] = 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:%.+]] = constant 0 : i32 |
| // CHECK: [[IDX_FILL:%.+]] = linalg.fill([[IDX_MIN]], [[IDX_INIT]]) |
| // CHECK: [[VAL_INIT:%.+]] = linalg.init_tensor [3] |
| // CHECK: [[VAL_MIN:%.+]] = 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:%.+]] = index_cast [[IDX]] |
| // CHECK: [[CMP:%.+]] = 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: constant -3.40282347E+38 : f32 |
| // CHECK: linalg.index |
| // CHECK: index_cast |
| // CHECK: 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:.+]] = 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:.+]] = 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<513xi8>) -> () { |
| // 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:.+]] = index_cast %[[ARG_IN]] |
| // CHECK: %[[EXTRACT:.+]] = tensor.extract %arg1[%[[CAST]]] |
| // CHECK: linalg.yield %[[EXTRACT]] |
| %0 = "tosa.table"(%arg0, %arg1) : (tensor<6xi8>, tensor<513xi8>) -> (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:.+]] = sexti %arg2 |
| // CHECK: %[[C32768:.+]] = constant 32768 |
| // CHECK: %[[C7:.+]] = constant 7 |
| // CHECK: %[[C1:.+]] = constant 1 |
| // CHECK: %[[C127:.+]] = constant 127 |
| // CHECK: %[[INADD:.+]] = addi %[[EXT_IN]], %[[C32768]] |
| // CHECK: %[[IDX:.+]] = shift_right_unsigned %[[INADD]], %[[C7]] |
| // CHECK: %[[FRACTION:.+]] = and %[[INADD]], %[[C127]] |
| // CHECK: %[[IDXPLUS1:.+]] = addi %[[IDX]], %[[C1]] |
| // CHECK: %[[IDX_CAST:.+]] = index_cast %[[IDX]] |
| // CHECK: %[[IDXPLUS1_CAST:.+]] = index_cast %[[IDXPLUS1]] |
| // CHECK: %[[BASE:.+]] = tensor.extract %arg1[%[[IDX_CAST]]] |
| // CHECK: %[[NEXT:.+]] = tensor.extract %arg1[%[[IDXPLUS1_CAST]]] |
| // CHECK: %[[BASE_EXT:.+]] = sexti %[[BASE]] |
| // CHECK: %[[NEXT_EXT:.+]] = sexti %[[NEXT]] |
| // CHECK: %[[BASE_MUL:.+]] = shift_left %[[BASE_EXT]], %[[C7]] |
| // CHECK: %[[DIFF:.+]] = subi %[[NEXT_EXT]], %[[BASE_EXT]] |
| // CHECK: %[[DIFF_MUL:.+]] = muli %[[DIFF]], %[[FRACTION]] |
| // CHECK: %[[RESULT:.+]] = addi %[[BASE_MUL]], %[[DIFF_MUL]] |
| // CHECK: linalg.yield %[[RESULT]] |
| %0 = "tosa.table"(%arg0, %arg1) : (tensor<6xi16>, tensor<513xi16>) -> (tensor<6xi32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @max_pool |
| func @max_pool(%arg0: tensor<1x6x34x62xf32>) -> () { |
| // CHECK-DAG: [[CONST:%.+]] = constant -3.40282347E+38 |
| // CHECK-DAG: [[INIT:%.+]] = linalg.init_tensor [1, 4, 32, 62] |
| // CHECK-DAG: [[FILL:%.+]] = linalg.fill([[CONST]], [[INIT]]) |
| // CHECK-DAG: [[KERNEL:%.+]] = linalg.init_tensor [3, 3] |
| // CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%arg0, [[KERNEL]] : tensor<1x6x34x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x32x62xf32>) |
| %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xf32>) -> (tensor<1x4x32x62xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: @max_pool_padded |
| func @max_pool_padded(%arg0: tensor<1x6x34x62xf32>) -> () { |
| // CHECK-DAG: [[CONST:%.+]] = constant -3.40282347E+38 : f32 |
| // CHECK-DAG: [[PAD:%.+]] = linalg.pad_tensor %arg0 low[0, 0, 0, 0] high[0, 0, 1, 0] |
| // CHECK-DAG: linalg.yield [[CONST]] |
| // CHECK-DAG: [[INITVAL:%.+]] = constant -3.40282347E+38 : f32 |
| // CHECK-DAG: [[INIT:%.+]] = linalg.init_tensor [1, 4, 33, 62] |
| // CHECK-DAG: [[FILL:%.+]] = linalg.fill([[INITVAL]], [[INIT]]) |
| // CHECK-DAG: [[KERNEL:%.+]] = linalg.init_tensor [3, 3] |
| // CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins([[PAD]], [[KERNEL]] : tensor<1x6x35x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x33x62xf32>) |
| %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 1], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xf32>) -> (tensor<1x4x33x62xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: @max_pool_i8 |
| func @max_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> () { |
| // CHECK: constant -128 |
| // CHECK: linalg.pooling_nhwc_max |
| %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi8>) -> (tensor<1x4x32x62xi8>) |
| return |
| } |
| |
| // CHECK-LABEL: @max_pool_i16 |
| func @max_pool_i16(%arg0: tensor<1x6x34x62xi16>) -> () { |
| // CHECK: constant -32768 |
| // CHECK: linalg.pooling_nhwc_max |
| %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi16>) -> (tensor<1x4x32x62xi16>) |
| return |
| } |
| |
| // CHECK-LABEL: @max_pool_i32 |
| func @max_pool_i32(%arg0: tensor<1x6x34x62xi32>) -> () { |
| // CHECK: constant -2147483648 |
| // CHECK: linalg.pooling_nhwc_max |
| %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi32>) -> (tensor<1x4x32x62xi32>) |
| return |
| } |
| // ----- |
| |
| // CHECK-LABEL: @avg_pool |
| func @avg_pool(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>) { |
| // Initial piece computes the sum of the pooling region, with appropriate padding. |
| // CHECK: [[CONST:%.+]] = constant 0 |
| // CHECK: [[PAD:%.+]] = linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0] |
| // CHECK: [[CONST:%.+]] = constant 0 |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 33, 62] |
| // CHECK: [[FILL:%.+]] = linalg.fill([[CONST]], [[INIT]]) |
| // CHECK: [[KERNEL:%.+]] = linalg.init_tensor [4, 4] |
| // CHECK: [[POOL:%.+]] = linalg.pooling_nhwc_sum {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins([[PAD]], [[KERNEL]] : tensor<1x8x36x62xf32>, tensor<4x4xf32>) outs([[FILL]] : tensor<1x5x33x62xf32>) |
| // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} outs([[POOL]] : tensor<1x5x33x62xf32>) |
| // CHECK: [[ZERO:%.0]] = constant 0 |
| // CHECK: [[ONE:%.+]] = constant 1 |
| // CHECK: [[HEIGHT:%.+]] = constant 4 |
| // CHECK: [[WIDTH:%.+]] = constant 32 |
| // CHECK: [[IDX1:%.+]] = linalg.index 1 |
| // CHECK: [[IDX2:%.+]] = linalg.index 2 |
| |
| // The large block below computes what portion of the kernel is within non-padded input. |
| // CHECK: [[NY:%.+]] = subi [[HEIGHT]], [[IDX1]] |
| // CHECK: [[NX:%.+]] = subi [[WIDTH]], [[IDX2]] |
| // CHECK: [[KH:%.+]] = constant 4 |
| // CHECK: [[PAD0:%.+]] = constant 1 |
| // CHECK: [[SUBP0:%.+]] = subi [[IDX1]], [[PAD0]] |
| // CHECK: [[P0CMP:%.+]] = cmpi slt, [[SUBP0]], [[ZERO]] |
| // CHECK: [[SELP0:%.+]] = select [[P0CMP]], [[SUBP0]], [[ZERO]] |
| // CHECK: [[ADDP0:%.+]] = addi [[KH]], [[SELP0]] |
| // CHECK: [[PAD1:%.+]] = constant 1 |
| // CHECK: [[SUBP1:%.+]] = subi [[NY]], [[PAD1]] |
| // CHECK: [[P1CMP:%.+]] = cmpi slt, [[SUBP1]], [[ZERO]] |
| // CHECK: [[SELP1:%.+]] = select [[P1CMP]], [[SUBP1]], [[ZERO]] |
| // CHECK: [[ADDP1:%.+]] = addi [[ADDP0]], [[SELP1]] |
| // CHECK: [[YCMP:%.+]] = cmpi slt, [[ADDP1]], [[ONE]] |
| // CHECK: [[YSEL:%.+]] = select [[YCMP]], [[ONE]], [[ADDP1]] |
| // CHECK: [[KW:%.+]] = constant 4 : index |
| // CHECK: [[PAD2:%.+]] = constant 1 : index |
| // CHECK: [[SUBP2:%.+]] = subi [[IDX2]], [[PAD2]] |
| // CHECK: [[P2CMP:%.+]] = cmpi slt, [[SUBP2]], [[ZERO]] |
| // CHECK: [[SELP2:%.+]] = select [[P2CMP]], [[SUBP2]], [[ZERO]] |
| // CHECK: [[ADDP2:%.+]] = addi [[KW]], [[SELP2]] |
| // CHECK: [[PAD3:%.+]] = constant 1 : index |
| // CHECK: [[SUBP3:%.+]] = subi [[NX]], [[PAD3]] |
| // CHECK: [[P3CMP:%.+]] = cmpi slt, [[SUBP3]], [[ZERO]] |
| // CHECK: [[SELP3:%.+]] = select [[P3CMP]], [[SUBP3]], [[ZERO]] |
| // CHECK: [[ADDP3:%.+]] = addi [[ADDP2]], [[SELP3]] |
| // CHECK: [[XCMP:%.+]] = cmpi slt, [[ADDP3]], [[ONE]] |
| // CHECK: [[XSEL:%.+]] = select [[XCMP]], [[ONE]], [[ADDP3]] |
| |
| // Given the valid coverage of the pooling region, normalize the summation. |
| // CHECK: [[C:%.+]] = muli [[YSEL]], [[XSEL]] |
| // CHECK: [[CI:%.+]] = index_cast [[C]] |
| // CHECK: [[CF:%.+]] = sitofp [[CI]] |
| // CHECK: [[RESULT:%.+]] = divf %arg1, [[CF]] |
| // CHECK: linalg.yield [[RESULT]] |
| %0 = "tosa.avg_pool2d"(%arg0) {pad = [1, 1, 1, 1], kernel = [4, 4], stride = [1, 1]} : (tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>) |
| return %0 : tensor<1x5x33x62xf32> |
| } |
| |
| // ----- |
| |
| // CHECK: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)> |
| // CHECK: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| // CHECK: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d3)> |
| |
| // CHECK-LABEL @conv2d_f32 |
| func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () { |
| // CHECK: %[[W_IN:.+]] = linalg.init_tensor [3, 3, 27, 28] |
| // CHECK: %[[W:.+]] = linalg.generic {indexing_maps = [#[[MAP0]], #[[MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg1 : tensor<28x3x3x27xf32>) outs(%[[W_IN]] : tensor<3x3x27x28xf32>) |
| // CHECK: linalg.yield %arg3 : f32 |
| // CHECK: %[[B_IN:.+]] = linalg.init_tensor [1, 45, 40, 28] |
| // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[MAP2]], #[[MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[B_IN]] : tensor<1x45x40x28xf32>) |
| // CHECK: linalg.yield %arg3 : f32 |
| // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %1 : tensor<1x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%[[B]] : tensor<1x45x40x28xf32>) |
| %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [2, 1]} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> (tensor<1x45x40x28xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @conv2d_padded_f32 |
| func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x28xf32>, %bias: tensor<28xf32>) -> () { |
| // CHECK: %[[C0:.+]] = constant 0 |
| // CHECK: linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0] |
| // CHECK: linalg.yield %[[C0]] |
| // CHECK: linalg.conv_2d_nhwc_hwcf |
| %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [1, 1, 1, 1], stride = [1, 1], dilation = [2, 1]} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>) -> (tensor<1x45x40x28xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @conv2d_quant |
| func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1xi8>, %arg2 : tensor<1024xi32>) -> () { |
| // CHECK: %[[C22:.+]] = constant -22 |
| // CHECK: linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0] |
| // CHECK: linalg.yield %[[C22]] |
| // CHECK: linalg.conv_2d_nhwc_hwcf_q |
| %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [1, 1, 1, 1], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = [1, 1]} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| |
| // CHECK-LABEL: @depthwise_conv |
| func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>, %arg2 : tensor<33xf32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 5, 33] |
| // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<33xf32>) outs([[INIT]] : tensor<1x5x5x33xf32>) { |
| // CHECK: ^bb0(%arg3: f32, %arg4: f32): // no predecessors |
| // CHECK: linalg.yield %arg3 : f32 |
| // CHECK: } -> tensor<1x5x5x33xf32> |
| // CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]] |
| // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>) |
| // CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]] |
| %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1] } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> (tensor<1x5x5x33xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| |
| // CHECK-LABEL: @depthwise_conv_strides |
| func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3x1x3x11xf32>, %arg2 : tensor<33xf32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 5, 5, 33] |
| // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<33xf32>) outs([[INIT]] : tensor<1x5x5x33xf32>) { |
| // CHECK: ^bb0(%arg3: f32, %arg4: f32): // no predecessors |
| // CHECK: linalg.yield %arg3 : f32 |
| // CHECK: } -> tensor<1x5x5x33xf32> |
| // CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]] |
| // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>) |
| // CHECK: linalg.tensor_collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]] |
| %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1] } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> (tensor<1x5x5x33xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| |
| // CHECK-LABEL: @depthwise_conv_quant |
| func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3x4x128xi8>, %arg2 : tensor<512xi32>) -> () { |
| // CHECK: %[[PADV:.+]] = constant -128 |
| // CHECK: %[[PAD:.+]] = linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0] |
| // CHECK: linalg.yield %[[PADV]] |
| |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 12, 12, 512] |
| // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<512xi32>) outs([[INIT]] : tensor<1x12x12x512xi32>) { |
| // CHECK: ^bb0(%arg3: i32, %arg4: i32): // no predecessors |
| // CHECK: linalg.yield %arg3 : i32 |
| // CHECK: } -> tensor<1x12x12x512xi32> |
| // CHECK: %[[DBIAS:.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]] |
| // CHECK: %[[C128:.+]] = constant -128 |
| // CHECK: %[[C42:.+]] = constant 42 |
| // CHECK: %[[DEPTH:.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%[[PAD]], %arg1, %[[C128]], %[[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs(%[[DBIAS]] : tensor<1x12x12x4x128xi32>) |
| // CHECK: linalg.tensor_collapse_shape %[[DEPTH]] {{\[}}[0], [1], [2], [3, 4]] |
| %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [1, 1, 1, 1], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [1, 1] } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x12x12x512xi32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)> |
| // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> |
| |
| // CHECK-LABEL: @depthwise_conv_quant_dilations |
| func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 : tensor<3x3x4x128xi8>, %arg2 : tensor<512xi32>) -> () { |
| // CHECK: [[INIT:%.+]] = linalg.init_tensor [1, 10, 10, 512] |
| // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<512xi32>) outs([[INIT]] : tensor<1x10x10x512xi32>) { |
| // CHECK: ^bb0(%arg3: i32, %arg4: i32): // no predecessors |
| // CHECK: linalg.yield %arg3 : i32 |
| // CHECK: } -> tensor<1x10x10x512xi32> |
| // CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]] |
| // CHECK: %[[C128:.+]] = constant -128 |
| // CHECK: %[[C42:.+]] = constant 42 |
| // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[DBIAS]] : tensor<1x10x10x4x128xi32>) |
| // CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]] |
| %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [2, 2] } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>) -> tensor<1x10x10x512xi32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @transpose_conv |
| func @transpose_conv(%arg0 : tensor<1x12x12x2xf32>, %arg1 : tensor<4x3x3x2xf32>, %arg2 : tensor<4xf32>) -> () { |
| // CHECK: linalg.pad_tensor %arg0 low[0, 2, 2, 0] high[0, 2, 2, 0] |
| // CHECK: linalg.conv_2d_nhwc_hwcf |
| %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [1, 14, 14, 4], stride = [1, 1]} : (tensor<1x12x12x2xf32>, tensor<4x3x3x2xf32>, tensor<4xf32>) -> tensor<1x14x14x4xf32> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @transpose_conv_dilated |
| func @transpose_conv_dilated(%arg0 : tensor<1x12x12x2xf32>, %arg1 : tensor<4x3x3x2xf32>, %arg2 : tensor<4xf32>) -> () { |
| // CHECK: [[PAD:%.+]] = linalg.pad_tensor %arg0 low[0, 4, 4, 0] high[0, 4, 4, 0] |
| // CHECK: linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], {{%.+}} : tensor<1x20x20x2xf32>, tensor<3x3x2x4xf32>) |
| %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [2, 2], out_pad = [0, 0], out_shape = [1, 16, 16, 4], stride = [1, 1]} : (tensor<1x12x12x2xf32>, tensor<4x3x3x2xf32>, tensor<4xf32>) -> tensor<1x16x16x4xf32> |
| 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:.+]] = constant 0 |
| // CHECK-DAG: %[[YMAX:.+]] = constant 1 |
| // CHECK-DAG: %[[XMAX:.+]] = constant 1 |
| // CHECK-DAG: %[[Y:.+]] = index_cast %[[IDX1]] |
| // CHECK-DAG: %[[X:.+]] = index_cast %[[IDX2]] |
| // CHECK-DAG: %[[STRIDEY:.+]] = constant 5.000000e-01 |
| // CHECK-DAG: %[[STRIDEX:.+]] = constant 5.000000e-01 |
| // CHECK-DAG: %[[OFFSETY:.+]] = constant 1.000000e-01 |
| // CHECK-DAG: %[[OFFSETX:.+]] = constant 2.000000e-01 |
| // CHECK-DAG: %[[VAL4:.+]] = uitofp %[[Y]] |
| // CHECK-DAG: %[[VAL5:.+]] = uitofp %[[X]] |
| // CHECK-DAG: %[[VAL6:.+]] = mulf %[[VAL4]], %[[STRIDEY]] |
| // CHECK-DAG: %[[VAL7:.+]] = mulf %[[VAL5]], %[[STRIDEX]] |
| // CHECK-DAG: %[[VAL8:.+]] = addf %[[VAL6]], %[[OFFSETY]] |
| // CHECK-DAG: %[[VAL9:.+]] = addf %[[VAL7]], %[[OFFSETX]] |
| |
| // Find the remainder and integer component of the target index. |
| |
| // CHECK-DAG: %[[VAL10:.+]] = floorf %[[VAL8]] |
| // CHECK-DAG: %[[VAL11:.+]] = floorf %[[VAL9]] |
| // CHECK-DAG: %[[VAL12:.+]] = subf %[[VAL8]], %[[VAL10]] |
| // CHECK-DAG: %[[VAL13:.+]] = subf %[[VAL9]], %[[VAL11]] |
| // CHECK-DAG: %[[VAL14:.+]] = fptosi %[[VAL10]] |
| // CHECK-DAG: %[[VAL15:.+]] = fptosi %[[VAL11]] |
| |
| // Round to the nearest index. |
| |
| // CHECK-DAG: %[[ROUND:.+]] = constant 5.000000e-01 |
| // CHECK-DAG: %[[VAL16:.+]] = cmpf oge, %[[VAL12]], %[[ROUND]] |
| // CHECK-DAG: %[[VAL17:.+]] = cmpf oge, %[[VAL13]], %[[ROUND]] |
| // CHECK-DAG: %[[ZERO:.+]] = constant 0 |
| // CHECK-DAG: %[[ONE:.+]] = constant 1 |
| // CHECK-DAG: %[[VAL18:.+]] = select %[[VAL16]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL19:.+]] = select %[[VAL17]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL20:.+]] = addi %[[VAL14]], %[[VAL18]] |
| // CHECK-DAG: %[[VAL21:.+]] = addi %[[VAL15]], %[[VAL19]] |
| |
| // This section applies bound checking to be within the input image. |
| |
| // CHECK-DAG: %[[VAL22:.+]] = cmpi slt, %[[VAL20]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL23:.+]] = select %[[VAL22]], %[[XYMIN]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL24:.+]] = cmpi slt, %[[YMAX]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL25:.+]] = select %[[VAL24]], %[[YMAX]], %[[VAL23]] |
| // CHECK-DAG: %[[VAL26:.+]] = cmpi slt, %[[VAL21]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL27:.+]] = select %[[VAL26]], %[[XYMIN]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL28:.+]] = cmpi slt, %[[XMAX]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL29:.+]] = select %[[VAL28]], %[[XMAX]], %[[VAL27]] |
| |
| // Extract the nearest value using the computed indices. |
| |
| // CHECK-DAG: %[[IDY:.+]] = index_cast %[[VAL25]] |
| // CHECK-DAG: %[[IDX:.+]] = 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:.+]] = constant 0 |
| // CHECK: %[[YMAX:.+]] = constant 1 |
| // CHECK: %[[XMAX:.+]] = constant 1 |
| |
| // CHECK: %[[VAL10:.+]] = floorf %[[VAL8:.+]] |
| // CHECK: %[[VAL11:.+]] = floorf %[[VAL9:.+]] |
| |
| // CHECK: %[[DY:.+]] = subf %[[VAL8:.+]], %[[VAL10]] |
| // CHECK: %[[DX:.+]] = subf %[[VAL9:.+]], %[[VAL11]] |
| |
| // CHECK: %[[Y0:.+]] = fptosi %[[VAL10]] |
| // CHECK: %[[X0:.+]] = fptosi %[[VAL11]] |
| |
| // Compute the left, right, and top indices for the bilinear interpolation. |
| |
| // CHECK: %[[ONE:.+]] = constant 1 |
| // CHECK: %[[Y1:.+]] = addi %[[Y0]], %[[ONE]] |
| // CHECK: %[[X1:.+]] = addi %[[X0]], %[[ONE]] |
| |
| // Bound check each dimension. |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[Y0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y0]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[YMAX]], %[[Y0]] |
| // CHECK: %[[YLO:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[Y1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y1]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[YMAX]], %[[Y1]] |
| // CHECK: %[[YHI:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[X0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X0]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[XMAX]], %[[X0]] |
| // CHECK: %[[XLO:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[X1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X1]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[XMAX]], %[[X1]] |
| // CHECK: %[[XHI:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // Extract each corner of the bilinear interpolation. |
| |
| // CHECK: %[[YLOI:.+]] = index_cast %[[YLO]] |
| // CHECK: %[[YHII:.+]] = index_cast %[[YHI]] |
| // CHECK: %[[XLOI:.+]] = index_cast %[[XLO]] |
| // CHECK: %[[XHII:.+]] = 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:.+]] = constant 1.000000e+00 |
| // CHECK: %[[NDX:.+]] = subf %[[ONE]], %[[DX]] |
| // CHECK: %[[WLOLO:.+]] = mulf %[[LOLO]], %[[NDX]] |
| // CHECK: %[[WLOHI:.+]] = mulf %[[LOHI]], %[[DX]] |
| // CHECK: %[[LO:.+]] = addf %[[WLOLO]], %[[WLOHI]] |
| // CHECK: %[[WHILO:.+]] = mulf %[[HILO]], %[[NDX]] |
| // CHECK: %[[WHIHI:.+]] = mulf %[[HIHI]], %[[DX]] |
| // CHECK: %[[HI:.+]] = addf %[[WHILO]], %[[WHIHI]] |
| // CHECK: %[[NDY:.+]] = subf %[[ONE]], %[[DY]] |
| // CHECK: %[[WLO:.+]] = mulf %[[LO]], %[[NDY]] |
| // CHECK: %[[WHI:.+]] = mulf %[[HI]], %[[DY]] |
| // CHECK: %[[RESULT:.+]] = 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:.+]] = constant 0 |
| // CHECK-DAG: %[[YMAX:.+]] = constant 1 |
| // CHECK-DAG: %[[XMAX:.+]] = constant 1 |
| // CHECK-DAG: %[[Y:.+]] = index_cast %[[IDX1]] |
| // CHECK-DAG: %[[X:.+]] = index_cast %[[IDX2]] |
| // CHECK-DAG: %[[STRIDEY:.+]] = constant 128 |
| // CHECK-DAG: %[[STRIDEX:.+]] = constant 128 |
| // CHECK-DAG: %[[OFFSETY:.+]] = constant 1 |
| // CHECK-DAG: %[[OFFSETX:.+]] = constant 2 |
| // CHECK-DAG: %[[EIGHT:.+]] = constant 8 |
| // CHECK-DAG: %[[VAL4:.+]] = muli %[[Y]], %[[STRIDEY]] |
| // CHECK-DAG: %[[VAL5:.+]] = muli %[[X]], %[[STRIDEX]] |
| // CHECK-DAG: %[[VAL6:.+]] = addi %[[VAL4]], %[[OFFSETY]] |
| // CHECK-DAG: %[[VAL7:.+]] = addi %[[VAL5]], %[[OFFSETX]] |
| |
| // Find the remainder and integer component of the target index. |
| |
| |
| // CHECK-DAG: %[[VAL8:.+]] = shift_right_signed %[[VAL6]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL9:.+]] = shift_right_signed %[[VAL7]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL10:.+]] = shift_left %[[VAL8]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL11:.+]] = shift_left %[[VAL9]], %[[EIGHT]] |
| // CHECK-DAG: %[[VAL12:.+]] = subi %[[VAL6]], %[[VAL10]] |
| // CHECK-DAG: %[[VAL13:.+]] = subi %[[VAL7]], %[[VAL11]] |
| |
| // Round to the nearest index. |
| |
| // CHECK-DAG: %[[ROUND:.+]] = constant 128 |
| // CHECK-DAG: %[[VAL16:.+]] = cmpi sge, %[[VAL12]], %[[ROUND]] |
| // CHECK-DAG: %[[VAL17:.+]] = cmpi sge, %[[VAL13]], %[[ROUND]] |
| // CHECK-DAG: %[[ZERO:.+]] = constant 0 |
| // CHECK-DAG: %[[ONE:.+]] = constant 1 |
| // CHECK-DAG: %[[VAL18:.+]] = select %[[VAL16]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL19:.+]] = select %[[VAL17]], %[[ONE]], %[[ZERO]] |
| // CHECK-DAG: %[[VAL20:.+]] = addi %[[VAL8]], %[[VAL18]] |
| // CHECK-DAG: %[[VAL21:.+]] = addi %[[VAL9]], %[[VAL19]] |
| |
| // This section applies bound checking to be within the input image. |
| |
| // CHECK-DAG: %[[VAL22:.+]] = cmpi slt, %[[VAL20]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL23:.+]] = select %[[VAL22]], %[[XYMIN]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL24:.+]] = cmpi slt, %[[YMAX]], %[[VAL20]] |
| // CHECK-DAG: %[[VAL25:.+]] = select %[[VAL24]], %[[YMAX]], %[[VAL23]] |
| // CHECK-DAG: %[[VAL26:.+]] = cmpi slt, %[[VAL21]], %[[XYMIN]] |
| // CHECK-DAG: %[[VAL27:.+]] = select %[[VAL26]], %[[XYMIN]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL28:.+]] = cmpi slt, %[[XMAX]], %[[VAL21]] |
| // CHECK-DAG: %[[VAL29:.+]] = select %[[VAL28]], %[[XMAX]], %[[VAL27]] |
| |
| // Extract the nearest value using the computed indices. |
| |
| // CHECK-DAG: %[[IDY:.+]] = index_cast %[[VAL25]] |
| // CHECK-DAG: %[[IDX:.+]] = 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:.+]] = constant 0 |
| // CHECK: %[[YMAX:.+]] = constant 1 |
| // CHECK: %[[XMAX:.+]] = constant 1 |
| |
| // CHECK: %[[Y0:.+]] = shift_right_signed |
| // CHECK: %[[X0:.+]] = shift_right_signed |
| // CHECK: %[[ROUNDY:.+]] = shift_left %[[Y0]] |
| // CHECK: %[[ROUNDX:.+]] = shift_left %[[X0]] |
| // CHECK: %[[DY:.+]] = subi %10, %[[ROUNDY]] |
| // CHECK: %[[DX:.+]] = subi %11, %[[ROUNDX]] |
| |
| // Compute the left, right, and top indices for the bilinear interpolation. |
| |
| // CHECK: %[[ONE:.+]] = constant 1 |
| // CHECK: %[[Y1:.+]] = addi %[[Y0]], %[[ONE]] |
| // CHECK: %[[X1:.+]] = addi %[[X0]], %[[ONE]] |
| |
| // Bound check each dimension. |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[Y0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y0]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[YMAX]], %[[Y0]] |
| // CHECK: %[[YLO:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[Y1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[Y1]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[YMAX]], %[[Y1]] |
| // CHECK: %[[YHI:.+]] = select %[[PRED]], %[[YMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[X0]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X0]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[XMAX]], %[[X0]] |
| // CHECK: %[[XLO:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[X1]], %[[XYMIN]] |
| // CHECK: %[[BOUND:.+]] = select %[[PRED]], %[[XYMIN]], %[[X1]] |
| // CHECK: %[[PRED:.+]] = cmpi slt, %[[XMAX]], %[[X1]] |
| // CHECK: %[[XHI:.+]] = select %[[PRED]], %[[XMAX]], %[[BOUND]] |
| |
| // Extract each corner of the bilinear interpolation. |
| |
| // CHECK: %[[YLOI:.+]] = index_cast %[[YLO]] |
| // CHECK: %[[YHII:.+]] = index_cast %[[YHI]] |
| // CHECK: %[[XLOI:.+]] = index_cast %[[XLO]] |
| // CHECK: %[[XHII:.+]] = 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:.+]] = sexti %[[LOLO]] |
| // CHECK: %[[XLOHI:.+]] = sexti %[[LOHI]] |
| // CHECK: %[[XHILO:.+]] = sexti %[[HILO]] |
| // CHECK: %[[XHIHI:.+]] = sexti %[[HIHI]] |
| |
| // Compute the bilinear interpolation. |
| |
| // CHECK: %[[SCALE:.+]] = constant 256 |
| // CHECK: %[[NDX:.+]] = subi %[[SCALE]], %[[DX]] |
| // CHECK: %[[WLOLO:.+]] = muli %[[XLOLO]], %[[NDX]] |
| // CHECK: %[[WLOHI:.+]] = muli %[[XLOHI]], %[[DX]] |
| // CHECK: %[[LO:.+]] = addi %[[WLOLO]], %[[WLOHI]] |
| // CHECK: %[[WHILO:.+]] = muli %[[XHILO]], %[[NDX]] |
| // CHECK: %[[WHIHI:.+]] = muli %[[XHIHI]], %[[DX]] |
| // CHECK: %[[HI:.+]] = addi %[[WHILO]], %[[WHIHI]] |
| // CHECK: %[[NDY:.+]] = subi %[[SCALE]], %[[DY]] |
| // CHECK: %[[WLO:.+]] = muli %[[LO]], %[[NDY]] |
| // CHECK: %[[WHI:.+]] = muli %[[HI]], %[[DY]] |
| // CHECK: %[[RESULT:.+]] = 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 |
| } |
