| func.func @normal_conv_1() { |
| %0 = util.unfoldable_constant dense< |
| [[[[0.5 , 0.5212766 ], |
| [0.54255319, 0.56382979], |
| [0.58510638, 0.60638298], |
| [0.62765957, 0.64893617], |
| [0.67021277, 0.69148936], |
| [0.71276596, 0.73404255]], |
| |
| [[0.75531915, 0.77659574], |
| [0.79787234, 0.81914894], |
| [0.84042553, 0.86170213], |
| [0.88297872, 0.90425532], |
| [0.92553191, 0.94680851], |
| [0.96808511, 0.9893617 ]], |
| |
| [[1.0106383 , 1.03191489], |
| [1.05319149, 1.07446809], |
| [1.09574468, 1.11702128], |
| [1.13829787, 1.15957447], |
| [1.18085106, 1.20212766], |
| [1.22340426, 1.24468085]], |
| |
| [[1.26595745, 1.28723404], |
| [1.30851064, 1.32978723], |
| [1.35106383, 1.37234043], |
| [1.39361702, 1.41489362], |
| [1.43617021, 1.45744681], |
| [1.4787234 , 1.5 ]]]]> : tensor<1x4x6x2xf32> |
| %1 = util.unfoldable_constant dense< |
| [[[[0.5 , 0.52857143, 0.55714286], |
| [0.58571429, 0.61428571, 0.64285714]], |
| |
| [[0.67142857, 0.7 , 0.72857143], |
| [0.75714286, 0.78571429, 0.81428571]], |
| |
| [[0.84285714, 0.87142857, 0.9 ], |
| [0.92857143, 0.95714286, 0.98571429]]], |
| |
| |
| [[[1.01428571, 1.04285714, 1.07142857], |
| [1.1 , 1.12857143, 1.15714286]], |
| |
| [[1.18571429, 1.21428571, 1.24285714], |
| [1.27142857, 1.3 , 1.32857143]], |
| |
| [[1.35714286, 1.38571429, 1.41428571], |
| [1.44285714, 1.47142857, 1.5 ]]]]> |
| : tensor<2x3x2x3xf32> |
| %2 = "stablehlo.convolution"(%0, %1) { |
| batch_group_count = 1 : i64, |
| dimension_numbers = #stablehlo.conv<raw |
| input_batch_dimension = 0, |
| input_feature_dimension = 3, |
| input_spatial_dimensions = [1, 2], |
| kernel_input_feature_dimension = 2, |
| kernel_output_feature_dimension = 3, |
| kernel_spatial_dimensions = [0, 1], |
| output_batch_dimension = 0, |
| output_feature_dimension = 3, |
| output_spatial_dimensions = [1, 2] |
| >, |
| feature_group_count = 1 : i64, |
| rhs_dilation = dense<1> : tensor<2xi64>, |
| window_strides = dense<1> : tensor<2xi64>} |
| : (tensor<1x4x6x2xf32>, tensor<2x3x2x3xf32>) -> (tensor<1x3x4x3xf32>) |
| check.expect_almost_eq_const(%2, dense< |
| [[[[ 8.39452888, 8.62796353, 8.86139818], |
| [ 8.89057751, 9.13860182, 9.38662614], |
| [ 9.38662614, 9.64924012, 9.9118541 ], |
| [ 9.88267477, 10.15987842, 10.43708207]], |
| |
| [[11.37082067, 11.69179331, 12.01276596], |
| [11.8668693 , 12.20243161, 12.53799392], |
| [12.36291793, 12.71306991, 13.06322188], |
| [12.85896657, 13.22370821, 13.58844985]], |
| |
| [[14.34711246, 14.7556231 , 15.16413374], |
| [14.84316109, 15.2662614 , 15.6893617 ], |
| [15.33920973, 15.7768997 , 16.21458967], |
| [15.83525836, 16.28753799, 16.73981763]]]]> |
| : tensor<1x3x4x3xf32>) : tensor<1x3x4x3xf32> |
| return |
| } |
| func.func @normal_conv_2() { |
| %input = util.unfoldable_constant dense< |
| [[[[6.0, 7.5, 0.0, 1.5], |
| [1.5, 3.5, 4.5, 2.0], |
| [3.0, 6.0, 0.5, 3.0]], |
| [[3.5, 7.0, 2.5, 6.5], |
| [4.0, 4.5, 8.0, 2.5], |
| [7.5, 7.5, 0.0, 1.5]], |
| [[7.0, 3.5, 0.0, 0.5], |
| [4.5, 0.0, 5.0, 1.5], |
| [5.5, 1.0, 0.0, 0.0]]]]> |
| : tensor<1x3x3x4xf32> |
| %filter = util.unfoldable_constant dense< |
| [[[[2.0, 2.5, 2.5, 3.0, 4.0, 2.0, 0.5, 2.0, 4.5, 5.0, 5.0, 4.0, 0.5, 0.5, 3.5, 4.5, |
| 4.5, 1.5, 3.0, 3.5, 1.0, 0.0, 1.5, 2.5, 4.5, 5.0, 2.0, 2.0, 3.0, 2.0, 2.0, 1.5], |
| [2.0, 2.0, 4.0, 2.0, 1.5, 5.0, 3.5, 2.5, 2.5, 0.0, 0.5, 2.5, 4.5, 1.5, 0.0, 2.5, |
| 0.0, 0.5, 1.0, 2.0, 1.0, 0.0, 1.5, 1.0, 5.0, 0.0, 3.5, 2.5, 4.5, 0.0, 5.0, 1.0], |
| [5.0, 3.5, 1.0, 4.5, 1.0, 1.5, 1.5, 1.0, 1.5, 2.0, 0.5, 1.0, 4.5, 5.0, 0.5, 2.0, |
| 5.0, 3.0, 4.0, 1.0, 1.5, 0.0, 0.0, 3.0, 0.0, 3.0, 1.5, 5.0, 1.5, 4.0, 4.0, 4.0], |
| [1.0, 1.5, 1.0, 0.0, 4.0, 4.0, 1.5, 4.0, 5.0, 1.0, 4.0, 2.0, 1.5, 0.0, 2.0, 1.5, |
| 3.0, 4.5, 4.0, 0.0, 4.0, 2.5, 4.5, 0.0, 4.5, 3.0, 2.5, 1.5, 0.5, 4.0, 0.0, 2.0]], |
| [[4.5, 3.0, 2.5, 3.5, 4.0, 4.0, 4.5, 1.0, 4.0, 3.0, 3.0, 4.5, 0.5, 3.0, 4.0, 4.0, |
| 1.5, 1.0, 1.5, 5.0, 3.0, 1.5, 3.0, 2.5, 3.5, 0.0, 4.0, 2.0, 5.0, 3.0, 2.5, 4.0], |
| [1.0, 1.5, 4.5, 3.5, 2.5, 1.5, 2.0, 2.5, 1.5, 1.5, 3.5, 4.5, 4.5, 4.5, 3.5, 1.5, |
| 5.0, 1.0, 1.5, 4.5, 5.0, 3.5, 3.5, 2.5, 0.5, 1.0, 1.0, 4.0, 0.5, 2.5, 4.0, 2.0], |
| [0.0, 1.0, 2.5, 2.5, 0.0, 4.0, 0.5, 0.5, 0.0, 1.5, 4.0, 4.0, 2.0, 2.0, 0.0, 4.5, |
| 1.5, 3.5, 1.5, 1.0, 0.5, 0.5, 1.0, 0.5, 2.0, 1.0, 2.5, 2.5, 2.5, 1.0, 2.5, 3.5], |
| [3.5, 3.0, 0.5, 3.0, 3.5, 1.0, 1.5, 0.5, 4.5, 2.5, 4.5, 4.5, 1.0, 0.0, 4.5, 0.5, |
| 4.5, 5.0, 0.0, 3.0, 0.0, 5.0, 2.0, 4.0, 2.0, 1.5, 1.5, 4.0, 4.0, 3.5, 0.0, 1.5]]], |
| [[[4.0, 3.5, 3.5, 5.0, 0.5, 4.0, 2.0, 3.5, 0.0, 2.0, 4.5, 0.0, 5.0, 3.0, 2.0, 1.0, |
| 2.0, 3.0, 1.5, 5.0, 1.5, 3.5, 4.0, 2.5, 0.0, 4.0, 2.5, 2.0, 3.5, 5.0, 5.0, 2.0], |
| [0.5, 1.5, 1.5, 4.5, 1.0, 2.5, 1.0, 1.5, 2.5, 5.0, 3.5, 1.0, 3.5, 0.5, 3.0, 5.0, |
| 2.5, 0.0, 0.0, 5.0, 1.5, 5.0, 0.5, 5.0, 4.5, 4.5, 3.0, 3.0, 3.5, 4.0, 4.0, 3.5], |
| [0.0, 4.0, 3.0, 4.0, 4.5, 4.0, 1.5, 3.0, 0.5, 3.5, 2.0, 4.5, 1.0, 0.0, 4.0, 1.0, |
| 3.5, 4.0, 2.0, 2.0, 0.5, 3.5, 3.0, 4.5, 2.0, 0.5, 2.5, 4.5, 3.5, 0.5, 1.5, 2.5], |
| [3.5, 1.5, 3.0, 3.0, 3.5, 4.5, 0.5, 4.5, 3.0, 0.0, 1.5, 4.0, 2.0, 0.5, 2.0, 2.5, |
| 0.0, 1.5, 5.0, 0.5, 2.0, 2.0, 2.0, 0.0, 0.0, 5.0, 4.0, 2.0, 3.0, 4.5, 1.5, 1.5]], |
| [[1.0, 0.5, 5.0, 1.0, 0.5, 1.5, 2.0, 5.0, 0.5, 0.5, 0.0, 3.5, 4.0, 5.0, 2.0, 1.5, |
| 2.5, 3.0, 1.5, 1.0, 4.5, 4.0, 0.5, 2.0, 5.0, 0.0, 4.0, 1.5, 4.5, 2.5, 2.5, 0.5], |
| [3.5, 4.0, 3.0, 2.0, 3.5, 1.5, 2.5, 1.5, 3.0, 2.0, 3.5, 1.5, 0.0, 2.5, 4.5, 1.5, |
| 3.5, 2.5, 2.5, 4.0, 0.0, 4.0, 1.5, 3.0, 4.5, 5.0, 1.5, 1.0, 3.5, 0.0, 1.5, 5.0], |
| [0.0, 1.5, 3.0, 0.5, 4.5, 1.0, 4.5, 2.0, 4.5, 0.5, 1.5, 1.0, 2.0, 4.5, 3.5, 2.0, |
| 4.5, 2.0, 0.5, 1.0, 3.5, 1.0, 1.5, 4.5, 5.0, 3.5, 5.0, 3.0, 3.0, 1.0, 5.0, 1.5], |
| [3.0, 0.0, 5.0, 4.0, 0.0, 5.0, 3.5, 3.0, 2.5, 4.5, 3.0, 2.5, 1.0, 3.5, 0.5, 4.5, |
| 1.0, 1.0, 2.5, 3.0, 2.0, 1.0, 1.0, 0.5, 0.0, 4.5, 0.0, 1.0, 4.0, 1.5, 5.0, 0.0]]]]> |
| : tensor<2x2x4x32xf32> |
| |
| %0 = "stablehlo.convolution"(%input, %filter) {batch_group_count = 1 : i64, |
| dimension_numbers = #stablehlo.conv<raw |
| input_batch_dimension = 0, |
| input_feature_dimension = 3, |
| input_spatial_dimensions = [1, 2], |
| kernel_input_feature_dimension = 2, |
| kernel_output_feature_dimension = 3, |
| kernel_spatial_dimensions = [0, 1], |
| output_batch_dimension = 0, |
| output_feature_dimension = 3, |
| output_spatial_dimensions = [1, 2] |
| >, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x3x3x4xf32>, tensor<2x2x4x32xf32>) -> tensor<1x2x2x32xf32> |
| |
| check.expect_almost_eq_const(%0, dense< |
| [[[[113.25, 127.0, 198.0, 173.25, 159.5, 190.75, 135.5, 160.0, |
| 169.5, 130.0, 173.75, 174.5, 158.5, 136.75, 159.75, 177.75, |
| 164.5, 122.25, 116.0, 168.0, 124.75, 144.0, 113.5, 159.0, |
| 208.0, 186.5, 190.5, 158.5, 213.75, 140.5, 206.75, 135.25], |
| [129.75, 147.25, 181.25, 181.75, 142.5, 161.75, 117.75, 153.25, |
| 119.5, 128.75, 149.25, 171.0, 152.5, 142.5, 166.0, 122.25, |
| 177.75, 142.75, 116.5, 170.0, 117.5, 176.75, 116.75, 162.25, |
| 161.25, 135.0, 145.5, 163.25, 190.5, 138.25, 162.5, 146.75]], |
| [[111.75, 115.75, 173.5, 158.25, 122.5, 187.25, 129.0, 142.5, |
| 142.25, 109.0, 175.75, 158.5, 172.75, 146.25, 122.25, 157.25, |
| 157.5, 141.25, 104.25, 151.25, 136.25, 122.0, 127.75, 125.75, |
| 180.5, 131.25, 168.75, 151.5, 180.75, 152.75, 193.5, 128.75], |
| [138.25, 133.75, 157.5, 168.5, 131.0, 149.75, 115.25, 130.75, |
| 114.5, 107.25, 127.75, 163.75, 153.5, 149.25, 133.5, 114.0, |
| 164.75, 120.75, 116.0, 149.5, 127.5, 113.5, 116.0, 129.75, |
| 126.75, 94.25, 135.0, 157.75, 158.75, 142.0, 158.75, 126.25]]]]> |
| : tensor<1x2x2x32xf32>) : tensor<1x2x2x32xf32> |
| return |
| } |
| |
| func.func @depthwise_conv() { |
| %input = util.unfoldable_constant dense< |
| [[[[6.0, 7.5, 0.0, 1.5, 1.5, 3.5, 4.5, 2.0, 3.0, 6.0, 0.5, 3.0, 3.5, 7.0, 2.5, 6.5], |
| [4.0, 4.5, 8.0, 2.5, 7.5, 7.5, 0.0, 1.5, 7.0, 3.5, 0.0, 0.5, 4.5, 0.0, 5.0, 1.5], |
| [5.5, 1.0, 0.0, 0.0, 2.0, 2.5, 3.0, 4.0, 7.5, 2.0, 4.5, 5.0, 0.5, 0.5, 3.5, 4.5], |
| [1.5, 3.0, 5.5, 7.0, 0.0, 7.0, 1.5, 6.0, 5.0, 5.5, 2.0, 3.0, 2.0, 7.5, 1.5, 6.0]]]]> |
| : tensor<1x1x4x16xf32> |
| %filter = util.unfoldable_constant dense< |
| [[[[2.0, 2.0, 4.0, 2.0, 1.5, 5.0, 3.5, 2.5, 2.5, 0.0, 0.5, 2.5, 4.5, 1.5, 0.0, 2.5]]]]> |
| : tensor<1x1x1x16xf32> |
| |
| %0 = "stablehlo.convolution"(%input, %filter) {batch_group_count = 1 : i64, |
| dimension_numbers = #stablehlo.conv<raw |
| input_batch_dimension = 0, |
| input_feature_dimension = 3, |
| input_spatial_dimensions = [1, 2], |
| kernel_input_feature_dimension = 2, |
| kernel_output_feature_dimension = 3, |
| kernel_spatial_dimensions = [0, 1], |
| output_batch_dimension = 0, |
| output_feature_dimension = 3, |
| output_spatial_dimensions = [1, 2] |
| >, feature_group_count = 16 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x1x4x16xf32>, tensor<1x1x1x16xf32>) -> tensor<1x1x4x16xf32> |
| |
| check.expect_almost_eq_const(%0, dense< |
| [[[[12.0, 15.0, 0.0, 3.0, 2.25, 17.5, 15.75, 5.0, 7.5, 0.0, 0.25, 7.5, 15.75, 10.5, 0.0, 16.25], |
| [8.0, 9.0, 32.0, 5.0, 11.25, 37.5, 0.0, 3.75, 17.5, 0.0, 0.0, 1.25, 20.25, 0.0, 0.0, 3.75], |
| [11.0, 2.0, 0.0, 0.0, 3.0, 12.5, 10.5, 10.0, 18.75, 0.0, 2.25, 12.5, 2.25, 0.75, 0.0, 11.25], |
| [3.0, 6.0, 22.0, 14.0, 0.0, 35.0, 5.25, 15.0, 12.5, 0.0, 1.0, 7.5, 9.0, 11.25, 0.0, 15.0]]]]> |
| : tensor<1x1x4x16xf32>) : tensor<1x1x4x16xf32> |
| return |
| } |
| |
