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1// RUN: mlir-opt --split-input-file --tosa-layerwise-constant-fold %s | FileCheck %s2 3// CHECK-LABEL: @reciprocal_fold_single_valued4func.func @reciprocal_fold_single_valued() -> tensor<f32> {5  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}2.5{{0*}}e-01{{.*}}tensor<f32>6  // CHECK-NOT: tosa.reciprocal7  // CHECK: return [[RES]]8  %0 = "tosa.const"() {values = dense<4.0> : tensor<f32>} : () -> tensor<f32>9  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>10  return %1 : tensor<f32>11}12 13// CHECK-LABEL: @reciprocal_fold_splat14func.func @reciprocal_fold_splat() -> tensor<12x7xf32> {15  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}2.5{{0*}}e-01{{.*}}tensor<12x7xf32>16  // CHECK-NOT: tosa.reciprocal17  // CHECK: return [[RES]]18  %0 = "tosa.const"() {values = dense<4.0> : tensor<12x7xf32>} : () -> tensor<12x7xf32>19  %1 = "tosa.reciprocal"(%0) : (tensor<12x7xf32>) -> tensor<12x7xf32>20  return %1 : tensor<12x7xf32>21}22 23// CHECK-LABEL: @reciprocal_div_zero24func.func @reciprocal_div_zero() -> tensor<f32> {25  // 0x7F800000 is the value for +infinity26  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7F80000027  // CHECK-NOT: tosa.reciprocal28  // CHECK: return [[RES]]29  %0 = "tosa.const"() {values = dense<0.0> : tensor<f32>} : () -> tensor<f32>30  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>31  return %1 : tensor<f32>32}33 34// CHECK-LABEL: @reciprocal_div_neg_zero35func.func @reciprocal_div_neg_zero() -> tensor<f32> {36  // 0xFF800000 is the value for -infinity37  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0xFF80000038  // CHECK-NOT: tosa.reciprocal39  // CHECK: return [[RES]]40  %0 = "tosa.const"() {values = dense<-0.0> : tensor<f32>} : () -> tensor<f32>41  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>42  return %1 : tensor<f32>43}44 45// CHECK-LABEL: @reciprocal_div_nan46func.func @reciprocal_div_nan() -> tensor<f32> {47  // 0x7FC00000 is the value for NAN48  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7FC0000049  // CHECK-NOT: tosa.reciprocal50  // CHECK: return [[RES]]51  %0 = "tosa.const"() {values = dense<0x7FC00000> : tensor<f32>} : () -> tensor<f32>52  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>53  return %1 : tensor<f32>54}55 56// CHECK-LABEL: @reciprocal_div_infinity57func.func @reciprocal_div_infinity() -> tensor<f32> {58  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}<0.{{0*}}e+00>59  // CHECK-NOT: tosa.reciprocal60  // CHECK: return [[RES]]61  %0 = "tosa.const"() {values = dense<0x7F800000> : tensor<f32>} : () -> tensor<f32>62  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>63  return %1 : tensor<f32>64}65 66// CHECK-LABEL: @reciprocal_div_neg_infinity67func.func @reciprocal_div_neg_infinity() -> tensor<f32> {68  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}<-0.{{0*}}e+00>69  // CHECK-NOT: tosa.reciprocal70  // CHECK: return [[RES]]71  %0 = "tosa.const"() {values = dense<0xFF800000> : tensor<f32>} : () -> tensor<f32>72  %1 = "tosa.reciprocal"(%0) : (tensor<f32>) -> tensor<f32>73  return %1 : tensor<f32>74}75 76// CHECK-LABEL: @reciprocal_div_underflow77func.func @reciprocal_div_underflow() -> tensor<2xf16> {78  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}-0.{{0*}}e+00, 0.{{0*}}e+0079  // CHECK-NOT: tosa.reciprocal80  // CHECK: return [[RES]]81  %0 = "tosa.const"() {values = dense<[-6.0e+15, 6.0e+15]> : tensor<2xf16>} : () -> tensor<2xf16>82  %1 = "tosa.reciprocal"(%0) : (tensor<2xf16>) -> tensor<2xf16>83  return %1 : tensor<2xf16>84}85 86// CHECK-LABEL: @reciprocal_div_overflow87func.func @reciprocal_div_overflow() -> tensor<2xf16> {88  // CHECK: [[RES:]] ={{.*}}tosa.const{{.*}}0x7C00, 0xFC0089  // CHECK-NOT: tosa.reciprocal90  // CHECK: return [[RES]]91  %0 = "tosa.const"() {values = dense<[0.0000001, -0.0000001]> : tensor<2xf16>} : () -> tensor<2xf16>92  %1 = "tosa.reciprocal"(%0) : (tensor<2xf16>) -> tensor<2xf16>93  return %1 : tensor<2xf16>94}95 96// CHECK-LABEL: @reciprocal_no_fold97// The folding optimization works only intra-procedurally, so we won't be able98// to fold anything here99func.func @reciprocal_no_fold(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {100  // CHECK: tosa.reciprocal101  // CHECK-NEXT: return102  %0 = "tosa.reciprocal"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>103  return %0 : tensor<?x?xf32>104}105 106// CHECK-LABEL: @reciprocal_fold107func.func @reciprocal_fold() -> tensor<4x6xf32> {108  // CHECK: [[RES:]] ={{.*}}tosa.const109  // CHECK-SAME{LITERAL}: [[5.68828249, 11.4416485, 1.6880486, 0.680272102, -0.875350117, 0.342313349],110  // CHECK-SAME{LITERAL}:  [-4.81231928, 0.698080301, 0.65432179, -82.6446304, -4.33651352, -0.747551739],111  // CHECK-SAME{LITERAL}:  [-12.4378109, 13.140605, 1.89501607, 0.885582745, 4.08830738, 1.4396776],112  // CHECK-SAME{LITERAL}:  [2.02880907, -1.53280187, 0.552730501, 7.15819644, 0.64495325, -0.973709881]]113  // CHECK-NOT: tosa.reciprocal114  // CHECK: return [[RES]]115  %0 = "tosa.const"() { values = dense<[116                        [ 0.1758,  0.0874,  0.5924,  1.4700, -1.1424,  2.9213],117                        [-0.2078,  1.4325,  1.5283, -0.0121, -0.2306, -1.3377],118                        [-0.0804,  0.0761,  0.5277,  1.1292,  0.2446,  0.6946],119                        [ 0.4929, -0.6524,  1.8092,  0.1397,  1.5505, -1.0270]]>120                        : tensor<4x6xf32>121                      } : () -> tensor<4x6xf32>122  %1 = "tosa.reciprocal"(%0) : (tensor<4x6xf32>) -> tensor<4x6xf32>123  return %1 : tensor<4x6xf32>124}125 126// CHECK-LABEL: @reciprocal_of_const_sparse127// Sparse tensors are currently not supported128func.func @reciprocal_of_const_sparse() -> tensor<32xbf16> {129  // CHECK: tosa.const130  // CHECK: tosa.reciprocal131    %0 = "tosa.const"() { values = sparse<132          [[0], [3], [11], [17], [20], [23], [25], [30], [31]],133          [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]>134          : tensor<32xbf16> } : () -> tensor<32xbf16>135    %1 = "tosa.reciprocal"(%0) : (tensor<32xbf16>) -> tensor<32xbf16>136    return %1 : tensor<32xbf16>137}138