| func.func @conv2d_nopadding() { |
| %inputs = util.unfoldable_constant dense<[[ |
| [[ 1.0, 2.0], [ 3.0, 4.0], [ 5.0, 6.0], [ 7.0, 8.0]], |
| [[11.0, 12.0], [13.0, 14.0], [15.0, 16.0], [17.0, 18.0]], |
| [[21.0, 22.0], [23.0, 24.0], [25.0, 26.0], [27.0, 28.0]], |
| [[31.0, 32.0], [33.0, 34.0], [35.0, 36.0], [37.0, 38.0]]]]> : tensor<1x4x4x2xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0]], [[ 3.0], [ 4.0]]], |
| [[[ 5.0], [ 6.0]], [[ 7.0], [ 8.0]]], |
| [[[ 9.0], [10.0]], [[11.0], [12.0]]]]> : tensor<3x2x2x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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 = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<1x4x4x2xf32>, tensor<3x2x2x1xf32>) -> tensor<1x2x3x1xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[1310.0],[1466.0],[1622.0]], |
| [[2090.0],[2246.0],[2402.0]] |
| ]]> : tensor<1x2x3x1xf32>) : tensor<1x2x3x1xf32> |
| return |
| } |
| |
| func.func @conv2d_nopadding_batch_feature() { |
| %inputs = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 3.0], [ 5.0], [ 7.0]], |
| [[11.0], [13.0], [15.0], [17.0]], |
| [[21.0], [23.0], [25.0], [27.0]], |
| [[31.0], [33.0], [35.0], [37.0]]], |
| [[[ 2.0], [ 4.0], [ 6.0], [ 8.0]], |
| [[12.0], [14.0], [16.0], [18.0]], |
| [[22.0], [24.0], [26.0], [28.0]], |
| [[32.0], [34.0], [36.0], [38.0]]] |
| ]> : tensor<2x4x4x1xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0]], [[ 3.0], [ 4.0]]], |
| [[[ 5.0], [ 6.0]], [[ 7.0], [ 8.0]]], |
| [[[ 9.0], [10.0]], [[11.0], [12.0]]]]> : tensor<3x2x2x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| batch_group_count = 1 : i64, |
| dimension_numbers = #stablehlo.conv<raw |
| input_batch_dimension = 3, |
| input_feature_dimension = 0, |
| 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 = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<2x4x4x1xf32>, tensor<3x2x2x1xf32>) -> tensor<1x2x3x1xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[1310.0],[1466.0],[1622.0]], |
| [[2090.0],[2246.0],[2402.0]] |
| ]]> : tensor<1x2x3x1xf32>) : tensor<1x2x3x1xf32> |
| return |
| } |
| |
| func.func @conv2d_reorder_input_spatial() { |
| %inputs = util.unfoldable_constant dense<[[ |
| [[ 1.0, 2.0], [11.0, 12.0], [21.0, 22.0], [31.0, 32.0]], |
| [[ 3.0, 4.0], [13.0, 14.0], [23.0, 24.0], [33.0, 34.0]], |
| [[ 5.0, 6.0], [15.0, 16.0], [25.0, 26.0], [35.0, 36.0]], |
| [[ 7.0, 8.0], [17.0, 18.0], [27.0, 28.0], [37.0, 38.0]]]]> : tensor<1x4x4x2xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0]], [[ 3.0], [ 4.0]]], |
| [[[ 5.0], [ 6.0]], [[ 7.0], [ 8.0]]], |
| [[[ 9.0], [10.0]], [[11.0], [12.0]]]]> : tensor<3x2x2x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| batch_group_count = 1 : i64, |
| dimension_numbers = #stablehlo.conv<raw |
| input_batch_dimension = 0, |
| input_feature_dimension = 3, |
| input_spatial_dimensions = [2, 1], |
| 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 = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<1x4x4x2xf32>, tensor<3x2x2x1xf32>) -> tensor<1x2x3x1xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[1310.0],[1466.0],[1622.0]], |
| [[2090.0],[2246.0],[2402.0]] |
| ]]> : tensor<1x2x3x1xf32>) : tensor<1x2x3x1xf32> |
| return |
| } |
| |
| func.func @conv2d_reorder_kernel() { |
| %inputs = util.unfoldable_constant dense<[[ |
| [[ 1.0, 2.0], [ 3.0, 4.0], [ 5.0, 6.0], [ 7.0, 8.0]], |
| [[11.0, 12.0], [13.0, 14.0], [15.0, 16.0], [17.0, 18.0]], |
| [[21.0, 22.0], [23.0, 24.0], [25.0, 26.0], [27.0, 28.0]], |
| [[31.0, 32.0], [33.0, 34.0], [35.0, 36.0], [37.0, 38.0]]]]> : tensor<1x4x4x2xf32> |
| %weights = util.unfoldable_constant dense< |
| [[[[ 1.0, 3.0], [ 2.0, 4.0]], |
| [[ 5.0, 7.0], [ 6.0, 8.0]], |
| [[ 9.0, 11.0], [10.0, 12.0]]]]> : tensor<1x3x2x2xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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 = 0, |
| kernel_spatial_dimensions = [1, 3], |
| output_batch_dimension = 0, |
| output_feature_dimension = 3, |
| output_spatial_dimensions = [1, 2] |
| >, |
| feature_group_count = 1 : i64, |
| rhs_dilation = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<1x4x4x2xf32>, tensor<1x3x2x2xf32>) -> tensor<1x2x3x1xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[1310.0],[1466.0],[1622.0]], |
| [[2090.0],[2246.0],[2402.0]] |
| ]]> : tensor<1x2x3x1xf32>) : tensor<1x2x3x1xf32> |
| return |
| } |
| |
| func.func @conv2d_reorder_output() { |
| %inputs = util.unfoldable_constant dense<[[ |
| [[ 1.0, 2.0], [ 3.0, 4.0], [ 5.0, 6.0], [ 7.0, 8.0]], |
| [[11.0, 12.0], [13.0, 14.0], [15.0, 16.0], [17.0, 18.0]], |
| [[21.0, 22.0], [23.0, 24.0], [25.0, 26.0], [27.0, 28.0]], |
| [[31.0, 32.0], [33.0, 34.0], [35.0, 36.0], [37.0, 38.0]]]]> : tensor<1x4x4x2xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0]], [[ 3.0], [ 4.0]]], |
| [[[ 5.0], [ 6.0]], [[ 7.0], [ 8.0]]], |
| [[[ 9.0], [10.0]], [[11.0], [12.0]]]]> : tensor<3x2x2x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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 = 2, |
| output_feature_dimension = 0, |
| output_spatial_dimensions = [3, 1] |
| >, |
| feature_group_count = 1 : i64, |
| rhs_dilation = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<1x4x4x2xf32>, tensor<3x2x2x1xf32>) -> tensor<1x3x1x2xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[1310.0, 2090.0]], |
| [[1466.0, 2246.0]], |
| [[1622.0, 2402.0]] |
| ]]> : tensor<1x3x1x2xf32>) : tensor<1x3x1x2xf32> |
| return |
| } |
| |
| func.func @conv2d_1452x3221_same() { |
| %inputs = util.unfoldable_constant dense<[[ |
| [[ 1.0, 2.0], [ 3.0, 4.0], [ 5.0, 6.0], [ 7.0, 8.0], [ 9.0, 10.0]], |
| [[11.0, 12.0], [13.0, 14.0], [15.0, 16.0], [17.0, 18.0], [19.0, 20.0]], |
| [[21.0, 22.0], [23.0, 24.0], [25.0, 26.0], [27.0, 28.0], [29.0, 30.0]], |
| [[31.0, 32.0], [33.0, 34.0], [35.0, 36.0], [37.0, 38.0], [39.0, 40.0]]]]> : tensor<1x4x5x2xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0]], [[ 3.0], [ 4.0]]], |
| [[[ 5.0], [ 6.0]], [[ 7.0], [ 8.0]]], |
| [[[ 9.0], [10.0]], [[11.0], [12.0]]]]> : tensor<3x2x2x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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<[[1, 1], [0, 1]]> : tensor<2x2xi64>, |
| rhs_dilation = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : |
| (tensor<1x4x5x2xf32>, tensor<3x2x2x1xf32>) -> tensor<1x4x5x1xf32> |
| check.expect_almost_eq_const(%res, dense<[[ |
| [[ 600.0], [ 736.0], [ 872.0], [1008.0], [ 476.0]], |
| [[1310.0], [1466.0], [1622.0], [1778.0], [ 805.0]], |
| [[2090.0], [2246.0], [2402.0], [2558.0], [1135.0]], |
| [[1080.0], [1152.0], [1224.0], [1296.0], [ 524.0]]]]> : tensor<1x4x5x1xf32>) : tensor<1x4x5x1xf32> |
| return |
| } |
| |
| func.func @conv2d_2451x2311_same() { |
| %inputs = util.unfoldable_constant dense<[ |
| [[[ 1.0], [ 2.0], [ 3.0], [ 4.0], [ 5.0]], |
| [[ 6.0], [ 7.0], [ 8.0], [ 9.0], [10.0]], |
| [[11.0], [12.0], [13.0], [14.0], [15.0]], |
| [[16.0], [17.0], [18.0], [19.0], [20.0]]], |
| [[[21.0], [22.0], [23.0], [24.0], [25.0]], |
| [[26.0], [27.0], [28.0], [29.0], [30.0]], |
| [[31.0], [32.0], [33.0], [34.0], [35.0]], |
| [[36.0], [37.0], [38.0], [39.0], [40.0]]]]> : tensor <2x4x5x1xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[1.0]], [[2.0]], [[3.0]]], |
| [[[4.0]], [[5.0]], [[6.0]]]]> : tensor <2x3x1x1xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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, 1], [1, 1]]> : tensor<2x2xi64>, |
| rhs_dilation = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : |
| (tensor<2x4x5x1xf32>, tensor<2x3x1x1xf32>) -> tensor<2x4x5x1xf32> |
| check.expect_almost_eq_const(%res, dense<[ |
| [[[ 80.0], [121.0], [142.0], [163.0], [100.0]], |
| [[160.0], [226.0], [247.0], [268.0], [160.0]], |
| [[240.0], [331.0], [352.0], [373.0], [220.0]], |
| [[ 83.0], [104.0], [110.0], [116.0], [ 59.0]]], |
| [[[400.0], [541.0], [562.0], [583.0], [340.0]], |
| [[480.0], [646.0], [667.0], [688.0], [400.0]], |
| [[560.0], [751.0], [772.0], [793.0], [460.0]], |
| [[183.0], [224.0], [230.0], [236.0], [119.0]]]]> : tensor<2x4x5x1xf32>) : tensor<2x4x5x1xf32> |
| return |
| } |
| |
| func.func @conv2d_no_padding2() { |
| %inputs = util.unfoldable_constant dense<[ |
| [[[ 1.0, 2.0, 3.0], |
| [ 4.0, 5.0, 6.0], |
| [ 7.0, 8.0, 9.0], |
| [ 10.0, 11.0, 12.0], |
| [ 13.0, 14.0, 15.0]], |
| [[ 16.0, 17.0, 18.0], |
| [ 19.0, 20.0, 21.0], |
| [ 22.0, 23.0, 24.0], |
| [ 25.0, 26.0, 27.0], |
| [ 28.0, 29.0, 30.0]], |
| [[ 31.0, 32.0, 33.0], |
| [ 34.0, 35.0, 36.0], |
| [ 37.0, 38.0, 39.0], |
| [ 40.0, 41.0, 42.0], |
| [ 43.0, 44.0, 45.0]], |
| [[ 46.0, 47.0, 48.0], |
| [ 49.0, 50.0, 51.0], |
| [ 52.0, 53.0, 54.0], |
| [ 55.0, 56.0, 57.0], |
| [ 58.0, 59.0, 60.0]]], |
| [[[ 61.0, 62.0, 63.0], |
| [ 64.0, 65.0, 66.0], |
| [ 67.0, 68.0, 69.0], |
| [ 70.0, 71.0, 72.0], |
| [ 73.0, 74.0, 75.0]], |
| [[ 76.0, 77.0, 78.0], |
| [ 79.0, 80.0, 81.0], |
| [ 82.0, 83.0, 84.0], |
| [ 85.0, 86.0, 87.0], |
| [ 88.0, 89.0, 90.0]], |
| [[ 91.0, 92.0, 93.0], |
| [ 94.0, 95.0, 96.0], |
| [ 97.0, 98.0, 99.0], |
| [100.0, 101.0, 102.0], |
| [103.0, 104.0, 105.0]], |
| [[106.0, 107.0, 108.0], |
| [109.0, 110.0, 111.0], |
| [112.0, 113.0, 114.0], |
| [115.0, 116.0, 117.0], |
| [118.0, 119.0, 120.0]]]]> : tensor<2x4x5x3xf32> |
| %weights = util.unfoldable_constant dense<[ |
| [[[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], |
| [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0], |
| [ 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]], |
| [[ 19.0, 20.0, 21.0, 22.0, 23.0, 24.0], |
| [ 25.0, 26.0, 27.0, 28.0, 29.0, 30.0], |
| [ 31.0, 32.0, 33.0, 34.0, 35.0, 36.0]], |
| [[ 37.0, 38.0, 39.0, 40.0, 41.0, 42.0], |
| [ 43.0, 44.0, 45.0, 46.0, 47.0, 48.0], |
| [ 49.0, 50.0, 51.0, 52.0, 53.0, 54.0]]], |
| [[[ 55.0, 56.0, 57.0, 58.0, 59.0, 60.0], |
| [ 61.0, 62.0, 63.0, 64.0, 65.0, 66.0], |
| [ 67.0, 68.0, 69.0, 70.0, 71.0, 72.0]], |
| [[ 73.0, 74.0, 75.0, 76.0, 77.0, 78.0], |
| [ 79.0, 80.0, 81.0, 82.0, 83.0, 84.0], |
| [ 85.0, 86.0, 87.0, 88.0, 89.0, 90.0]], |
| [[ 91.0, 92.0, 93.0, 94.0, 95.0, 96.0], |
| [ 97.0, 98.0, 99.0, 100.0, 101.0, 102.0], |
| [103.0, 104.0, 105.0, 106.0, 107.0, 108.0]]]]> : tensor<2x3x3x6xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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 = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : |
| (tensor<2x4x5x3xf32>, tensor<2x3x3x6xf32>) -> tensor<2x3x3x6xf32> |
| check.expect_almost_eq_const(%res, dense<[ |
| [[[16065.0, 16290.0, 16515.0, 16740.0, 16965.0, 17190.0], |
| [18873.0, 19152.0, 19431.0, 19710.0, 19989.0, 20268.0], |
| [21681.0, 22014.0, 22347.0, 22680.0, 23013.0, 23346.0]], |
| [[30105.0, 30600.0, 31095.0, 31590.0, 32085.0, 32580.0], |
| [32913.0, 33462.0, 34011.0, 34560.0, 35109.0, 35658.0], |
| [35721.0, 36324.0, 36927.0, 37530.0, 38133.0, 38736.0]], |
| [[44145.0, 44910.0, 45675.0, 46440.0, 47205.0, 47970.0], |
| [46953.0, 47772.0, 48591.0, 49410.0, 50229.0, 51048.0], |
| [49761.0, 50634.0, 51507.0, 52380.0, 53253.0, 54126.0]]], |
| [[[72225.0, 73530.0, 74835.0, 76140.0, 77445.0, 78750.0], |
| [75033.0, 76392.0, 77751.0, 79110.0, 80469.0, 81828.0], |
| [77841.0, 79254.0, 80667.0, 82080.0, 83493.0, 84906.0]], |
| [[86265.0, 87840.0, 89415.0, 90990.0, 92565.0, 94140.0], |
| [89073.0, 90702.0, 92331.0, 93960.0, 95589.0, 97218.0], |
| [91881.0, 93564.0, 95247.0, 96930.0, 98613.0, 100296.0]], |
| [[100305.0, 102150.0, 103995.0, 105840.0, 107685.0, 109530.0], |
| [103113.0, 105012.0, 106911.0, 108810.0, 110709.0, 112608.0], |
| [105921.0, 107874.0, 109827.0, 111780.0, 113733.0, 115686.0]]]]> : tensor<2x3x3x6xf32>) : tensor<2x3x3x6xf32> |
| return |
| } |
| |
| func.func @conv2d_1452x2223_dilated_valid() { |
| %inputs = util.unfoldable_constant dense< |
| [[[[0.09762701, 0.43037874], |
| [ 0.20552675, 0.08976637], |
| [-0.1526904, 0.29178822], |
| [-0.12482557, 0.78354603], |
| [ 0.92732555, -0.23311697]], |
| [[ 0.5834501, 0.05778984], |
| [ 0.13608912, 0.85119325], |
| [-0.85792786, -0.8257414 ], |
| [-0.9595632, 0.6652397 ], |
| [ 0.5563135, 0.74002427]], |
| [[ 0.9572367, 0.59831715], |
| [-0.07704128, 0.56105834], |
| [-0.76345116, 0.27984205], |
| [-0.71329343, 0.88933784], |
| [ 0.04369664, -0.17067613]], |
| [[-0.47088876, 0.5484674 ], |
| [-0.08769934, 0.1368679 ], |
| [-0.9624204, 0.23527099], |
| [ 0.22419144, 0.23386799], |
| [ 0.8874962, 0.3636406 ]]]]> : tensor<1x4x5x2xf32> |
| %weights = util.unfoldable_constant dense< |
| [[[[-0.2809842, -0.12593609, 0.3952624 ], |
| [-0.8795491, 0.33353344, 0.34127575]], |
| [[-0.5792349, -0.7421474, -0.3691433 ], |
| [-0.27257845, 0.14039354, -0.12279698]]], |
| [[[ 0.9767477, -0.79591036, -0.5822465 ], |
| [-0.677381, 0.30621666, -0.4934168 ]], |
| [[-0.06737845, -0.5111488, -0.68206084], |
| [-0.7792497, 0.31265917, -0.7236341 ]]]]> : tensor<2x2x2x3xf32> |
| %res = "stablehlo.convolution"(%inputs, %weights) { |
| 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 = array<i64: 2, 1>, |
| window_strides = array<i64: 1, 1> |
| } : (tensor<1x4x5x2xf32>, tensor<2x2x2x3xf32>) -> tensor<1x2x4x3xf32> |
| check.expect_almost_eq_const(%res, dense< |
| [[[[-0.45181108, -0.37253797, -1.1074474 ], |
| [-0.74972206, 0.8691965, 0.21864426], |
| [-1.9352274, 1.6551838, 0.13848126], |
| [-2.296763, 0.32046723, -0.02542188]], |
| [[-1.4578199, 0.59465677, 0.0599021 ], |
| [-0.3617443, 1.4647548, 1.2320882 ], |
| [ 0.04506956, 1.4347346, -0.22625303], |
| [-1.122044, -0.41301775, -1.5628793 ]]]]> : tensor<1x2x4x3xf32>) : tensor<1x2x4x3xf32> |
| return |
| } |
| |
| func.func @depthwise_conv_non_1_channel_multiplier() { |
| %arg0 = util.unfoldable_constant dense<1.0> : tensor<2x4x5x2xf32> |
| %arg1 = util.unfoldable_constant dense<1.0> : tensor<2x2x1x6xf32> |
| %res = "stablehlo.convolution"(%arg0, %arg1) { |
| 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 = 2 : i64, |
| padding = dense<0> : tensor<2x2xi64>, |
| rhs_dilation = array<i64: 1, 1>, |
| window_strides = array<i64: 1, 1>} : (tensor<2x4x5x2xf32>, tensor<2x2x1x6xf32>) -> tensor<2x3x4x6xf32> |
| check.expect_almost_eq_const(%res, dense<4.0> : tensor<2x3x4x6xf32>) : tensor<2x3x4x6xf32> |
| return |
| } |
