| func.func @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]]]]> |
| : 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 = "mhlo.convolution"(%input, %filter) {batch_group_count = 1 : i64, |
| dimension_numbers = #mhlo.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 = "mhlo.convolution"(%input, %filter) {batch_group_count = 1 : i64, |
| dimension_numbers = #mhlo.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 |
| } |
| |
