| //===----------------------------------------------------------------------===// |
| // Pointwise convolution ops. |
| // Naming convention: '_'.join( |
| // [conv, |
| // {input-spatial-dims}, |
| // {output-spatial-dims}, |
| // {filter-dims}, |
| // {output-size x input-size}]) |
| //===----------------------------------------------------------------------===// |
| |
| func.func @conv_244_112_3x3_3x32() -> tensor<1x112x112x32xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x224x224x3xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<3x3x3x32xf32> |
| %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, 1], [0, 1]]> : tensor<2x2xi64>, |
| rhs_dilation = dense<1> : tensor<2xi64>, |
| window_strides = dense<2> : tensor<2xi64> |
| } : (tensor<1x224x224x3xf32>, tensor<3x3x3x32xf32>) -> tensor<1x112x112x32xf32> |
| return %0 : tensor<1x112x112x32xf32> |
| } |
| |
| func.func @conv_112_112_1x1_32x64() -> tensor<1x112x112x64xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x112x112x32xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<1x1x32x64xf32> |
| %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<1x112x112x32xf32>, tensor<1x1x32x64xf32>) -> tensor<1x112x112x64xf32> |
| return %0 : tensor<1x112x112x64xf32> |
| } |
| |
| func.func @conv_7_7_1x1_1024x1024() -> tensor<1x7x7x1024xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x7x7x1024xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<1x1x1024x1024xf32> |
| %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<1x7x7x1024xf32>, tensor<1x1x1024x1024xf32>) -> tensor<1x7x7x1024xf32> |
| return %0 : tensor<1x7x7x1024xf32> |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // Depthwise convolution ops. |
| // Naming convention: '_'.join( |
| // [depthwise_conv, |
| // {input-spatial-dim}, |
| // {output-spatial-dim}, |
| // {filter-size}, |
| // {output-size x input-size}, |
| // {feature-group}]) |
| //===----------------------------------------------------------------------===// |
| |
| func.func @depthwise_conv_15x1_1x1_15x1_1x1024_1024() -> tensor<1x1x1x1024xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x15x1x1024xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<15x1x1x1024xf32> |
| %res = "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 = 1024 : i64, |
| padding = dense<0> : tensor<2x2xi64>, |
| rhs_dilation = dense<1> : tensor<2xi64>, |
| window_strides = dense<1> : tensor<2xi64> |
| } : (tensor<1x15x1x1024xf32>, tensor<15x1x1x1024xf32>) -> tensor<1x1x1x1024xf32> |
| return %res : tensor<1x1x1x1024xf32> |
| } |
| |
| func.func @depthwise_conv_15x1_1x1_15x1_1x512_512() -> tensor<1x1x1x512xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x15x1x512xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<15x1x1x512xf32> |
| %res = "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 = 512 : i64, |
| padding = dense<0> : tensor<2x2xi64>, |
| rhs_dilation = dense<1> : tensor<2xi64>, |
| window_strides = dense<1> : tensor<2xi64> |
| } : (tensor<1x15x1x512xf32>, tensor<15x1x1x512xf32>) -> tensor<1x1x1x512xf32> |
| return %res : tensor<1x1x1x512xf32> |
| } |
| |
| func.func @depthwise_conv_16x1_2x1_16x1_1x512_512() -> tensor<1x2x1x512xf32> { |
| %input = util.unfoldable_constant dense<1.0> : tensor<1x16x1x512xf32> |
| %filter = util.unfoldable_constant dense<1.0> : tensor<15x1x1x512xf32> |
| %res = "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 = 512 : i64, |
| padding = dense<0> : tensor<2x2xi64>, |
| rhs_dilation = dense<1> : tensor<2xi64>, |
| window_strides = dense<1> : tensor<2xi64> |
| } : (tensor<1x16x1x512xf32>, tensor<15x1x1x512xf32>) -> tensor<1x2x1x512xf32> |
| return %res : tensor<1x2x1x512xf32> |
| } |
