| // RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s |
| |
| // CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwcm_tensor |
| func @depthwise_conv_2d_nhwc_hwcm_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> { |
| %zero = arith.constant 0.000000e+00 : f32 |
| %init = linalg.init_tensor [2, 3, 4, 2, 3] : tensor<2x3x4x2x3xf32> |
| %fill = linalg.fill(%zero, %init) : f32, tensor<2x3x4x2x3xf32> -> tensor<2x3x4x2x3xf32> |
| // CHECK: %{{.+}} = linalg.depthwise_conv_2d_nhwc_hwcm |
| // CHECK-SAME: {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<2x3x4x2x3xf32>) |
| %0 = linalg.depthwise_conv_2d_nhwc_hwcm |
| { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> } |
| ins(%input, %filter : tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>) |
| outs(%fill : tensor<2x3x4x2x3xf32>) -> tensor<2x3x4x2x3xf32> |
| return %0 : tensor<2x3x4x2x3xf32> |
| } |
| |
| // CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwcm_memref |
| func @depthwise_conv_2d_nhwc_hwcm_memref(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) { |
| // CHECK: linalg.depthwise_conv_2d_nhwc_hwcm |
| // CHECK-SAME: {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<2x3x4x2x3xf32>) |
| linalg.depthwise_conv_2d_nhwc_hwcm |
| { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> } |
| ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>) |
| outs(%output : memref<2x3x4x2x3xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: func @depthwise_conv_1d_nw_tensor |
| func @depthwise_conv_1d_nw_tensor(%input: tensor<1x113x96xf32>, %filter: tensor<3x96xf32>) -> tensor<1x56x96xf32> { |
| %init = linalg.init_tensor [1, 56, 96] : tensor<1x56x96xf32> |
| // CHECK: %{{.+}} = linalg.depthwise_conv_1d_nw |
| // CHECK-SAME: {dilations = dense<1> : vector<1xi64>, strides = dense<2> : vector<1xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x113x96xf32>, tensor<3x96xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x56x96xf32>) -> tensor<1x56x96xf32> |
| %0 = linalg.depthwise_conv_1d_nwc_wc {dilations = dense<1> : vector<1xi64>, strides = dense<2> : vector<1xi64>} |
| ins(%input, %filter: tensor<1x113x96xf32>, tensor<3x96xf32>) |
| outs(%init: tensor<1x56x96xf32>) -> tensor<1x56x96xf32> |
| return %0: tensor<1x56x96xf32> |
| } |
| |
| // CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwc_tensor |
| func @depthwise_conv_2d_nhwc_hwc_tensor(%input: tensor<1x113x113x96xf32>, %filter: tensor<3x3x96xf32>) -> tensor<1x56x56x96xf32> { |
| %init = linalg.init_tensor [1, 56, 56, 96] : tensor<1x56x56x96xf32> |
| // CHECK: %{{.+}} = linalg.depthwise_conv_2d_nhwc_hwc |
| // CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x113x113x96xf32>, tensor<3x3x96xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32> |
| %0 = linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>} |
| ins(%input, %filter: tensor<1x113x113x96xf32>, tensor<3x3x96xf32>) |
| outs(%init: tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32> |
| return %0: tensor<1x56x56x96xf32> |
| } |
| |
| // CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwc_memref |
| func @depthwise_conv_2d_nhwc_hwc_memref(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) { |
| // CHECK: linalg.depthwise_conv_2d_nhwc_hwc |
| // CHECK-SAME: {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x56x56x96xf32>) |
| linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>} |
| ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| outs(%output: memref<1x56x56x96xf32>) |
| return |
| } |
| |
| func @depthwise_conv_2d_nhwc_hwcm_tensor_dilated(%input: tensor<2x8x9x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x6x7x2x3xf32> { |
| %zero = arith.constant 0.000000e+00 : f32 |
| %init = linalg.init_tensor [2, 6, 7, 2, 3] : tensor<2x6x7x2x3xf32> |
| %fill = linalg.fill(%zero, %init) : f32, tensor<2x6x7x2x3xf32> -> tensor<2x6x7x2x3xf32> |
| // CHECK: %{{.+}} = linalg.depthwise_conv_2d_nhwc_hwcm |
| // CHECK-SAME: {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<2x8x9x2xf32>, tensor<2x2x2x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<2x6x7x2x3xf32>) |
| %0 = linalg.depthwise_conv_2d_nhwc_hwcm |
| { dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> } |
| ins(%input, %filter : tensor<2x8x9x2xf32>, tensor<2x2x2x3xf32>) |
| outs(%fill : tensor<2x6x7x2x3xf32>) -> tensor<2x6x7x2x3xf32> |
| return %0 : tensor<2x6x7x2x3xf32> |
| } |
| |
| // CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwcm_memref_dilated |
| func @depthwise_conv_2d_nhwc_hwcm_memref_dilated(%input: memref<2x8x9x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x6x7x2x3xf32>) { |
| // CHECK: linalg.depthwise_conv_2d_nhwc_hwcm |
| // CHECK-SAME: {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<2x8x9x2xf32>, memref<2x2x2x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<2x6x7x2x3xf32>) |
| linalg.depthwise_conv_2d_nhwc_hwcm |
| { dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> } |
| ins(%input, %filter : memref<2x8x9x2xf32>, memref<2x2x2x3xf32>) |
| outs(%output : memref<2x6x7x2x3xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func @depthwise_conv_2d_input_nhwc_filter_missing_stride(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) { |
| // expected-error @+1 {{missing indexing map required attribute 'strides'}} |
| linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>} |
| ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| outs(%output: memref<1x56x56x96xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func @depthwise_conv_2d_input_nhwc_filter_missing_dilations(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) { |
| // expected-error @+1 {{missing indexing map required attribute 'dilations'}} |
| linalg.depthwise_conv_2d_nhwc_hwc {strides = dense<1> : vector<2xi64>} |
| ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| outs(%output: memref<1x56x56x96xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_element_type(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) { |
| // expected-error @+1 {{incorrect element type for indexing map required attribute 'strides'}} |
| linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2.0> : vector<2xf32>} |
| ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| outs(%output: memref<1x56x56x96xf32>) |
| return |
| } |
| |
| // ----- |
| |
| func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) { |
| // expected-error @+1 {{incorrect shape for indexing map required attribute 'strides'}} |
| linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<3xi64> } |
| ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>) |
| outs(%output: memref<1x56x56x96xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_1d_nwc_wcf |
| func @conv_1d_nwc_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> { |
| // CHECK: %{{.+}} = linalg.conv_1d_nwc_wcf |
| // CHECK-SAME: dilations = dense<1> : tensor<1xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<1xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?xf32>, tensor<?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> |
| %0 = linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: tensor<?x?x?xf32>, tensor<?x?x?xf32>) |
| outs (%init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_1d_nwc_wcf |
| func @conv_1d_nwc_wcf(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>, %output: memref<?x?x?xf32>) { |
| // CHECK: linalg.conv_1d_nwc_wcf |
| // CHECK-SAME: dilations = dense<1> : tensor<1xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<1xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>) |
| linalg.conv_1d_nwc_wcf {dilations = dense<1> : tensor<1xi64>, |
| strides = dense<1> : tensor<1xi64>} |
| ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>) |
| outs (%output: memref<?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_2d_nhwc_hwcf |
| func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> { |
| // CHECK: %{{.+}} = linalg.conv_2d_nhwc_hwcf |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> |
| %0 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) |
| outs (%init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> |
| return %0 : tensor<?x?x?x?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_2d_nhwc_hwcf |
| func @conv_2d_nhwc_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) { |
| // CHECK: linalg.conv_2d_nhwc_hwcf |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>) |
| linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, |
| strides = dense<1> : tensor<2xi64>} |
| ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) |
| outs (%output: memref<?x?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_3d_ndhwc_dhwcf |
| func @conv_3d_ndhwc_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> { |
| // CHECK: %{{.+}} = linalg.conv_3d_ndhwc_dhwcf |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> |
| %0 = linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>, |
| strides = dense<1> : tensor<3xi64>} |
| ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) |
| outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> |
| return %0 : tensor<?x?x?x?x?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @conv_3d_ndhwc_dhwcf |
| func @conv_3d_ndhwc_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) { |
| // CHECK: linalg.conv_3d_ndhwc_dhwcf |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xf32>) |
| linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>, |
| strides = dense<1> : tensor<3xi64>} |
| ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) |
| outs (%output: memref<?x?x?x?x?xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_sum_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_sum |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| func @pooling_nhwc_sum_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x1xf32> -> tensor<1x2x2x1xf32> |
| %res = linalg.pooling_nhwc_sum {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| outs(%fill: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| return %res : tensor<1x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_sum |
| // CHECK: linalg.pooling_nhwc_sum |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xf32>, memref<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xf32>) |
| func @pooling_nhwc_sum(%input: memref<1x4x4x1xf32>, %fake: memref<3x3xf32>, %output: memref<1x2x2x1xf32>) { |
| linalg.pooling_nhwc_sum {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xf32>, memref<3x3xf32>) |
| outs(%output: memref<1x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| func @pooling_nhwc_max_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x1xf32> -> tensor<1x2x2x1xf32> |
| %res = linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| outs(%fill: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| return %res : tensor<1x2x2x1xf32> |
| } |
| |
| // ----- |
| // CHECK-LABEL: func @pooling_nchw_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nchw_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x1x4x4xf32>, tensor<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x1x2x2xf32>) -> tensor<1x1x2x2xf32> |
| |
| func @pooling_nchw_max_tensor(%input: tensor<1x1x4x4xf32>) -> tensor<1x1x2x2xf32> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xf32> |
| %init = linalg.init_tensor [1, 1, 2, 2] : tensor<1x1x2x2xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x1x2x2xf32> -> tensor<1x1x2x2xf32> |
| %res = linalg.pooling_nchw_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x1x4x4xf32>, tensor<3x3xf32>) |
| outs(%fill: tensor<1x1x2x2xf32>) -> tensor<1x1x2x2xf32> |
| return %res : tensor<1x1x2x2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_max |
| // CHECK: linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xf32>, memref<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xf32>) |
| func @pooling_nhwc_max(%input: memref<1x4x4x1xf32>, %fake: memref<3x3xf32>, %output: memref<1x2x2x1xf32>) { |
| linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xf32>, memref<3x3xf32>) |
| outs(%output: memref<1x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i8_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xi8>, tensor<3x3xi8>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xi8>) -> tensor<1x2x2x1xi8> |
| func @pooling_nhwc_i8_max_tensor(%input: tensor<1x4x4x1xi8>) -> tensor<1x2x2x1xi8> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xi8> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xi8> |
| %cst = arith.constant 0 : i8 |
| %fill = linalg.fill(%cst, %init) : i8, tensor<1x2x2x1xi8> -> tensor<1x2x2x1xi8> |
| %res = linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xi8>, tensor<3x3xi8>) |
| outs(%fill: tensor<1x2x2x1xi8>) -> tensor<1x2x2x1xi8> |
| return %res : tensor<1x2x2x1xi8> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i8_max |
| // CHECK: linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xi8>, memref<3x3xi8>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xi8>) |
| func @pooling_nhwc_i8_max(%input: memref<1x4x4x1xi8>, %fake: memref<3x3xi8>, %output: memref<1x2x2x1xi8>) { |
| linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xi8>, memref<3x3xi8>) |
| outs(%output: memref<1x2x2x1xi8>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i16_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xi16>, tensor<3x3xi16>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xi16>) -> tensor<1x2x2x1xi16> |
| func @pooling_nhwc_i16_max_tensor(%input: tensor<1x4x4x1xi16>) -> tensor<1x2x2x1xi16> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xi16> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xi16> |
| %cst = arith.constant 0 : i16 |
| %fill = linalg.fill(%cst, %init) : i16, tensor<1x2x2x1xi16> -> tensor<1x2x2x1xi16> |
| %res = linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xi16>, tensor<3x3xi16>) |
| outs(%fill: tensor<1x2x2x1xi16>) -> tensor<1x2x2x1xi16> |
| return %res : tensor<1x2x2x1xi16> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i16_max |
| // CHECK: linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xi16>, memref<3x3xi16>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xi16>) |
| func @pooling_nhwc_i16_max(%input: memref<1x4x4x1xi16>, %fake: memref<3x3xi16>, %output: memref<1x2x2x1xi16>) { |
| linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xi16>, memref<3x3xi16>) |
| outs(%output: memref<1x2x2x1xi16>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i32_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xi32>, tensor<3x3xi32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32> |
| func @pooling_nhwc_i32_max_tensor(%input: tensor<1x4x4x1xi32>) -> tensor<1x2x2x1xi32> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xi32> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xi32> |
| %cst = arith.constant 0 : i32 |
| %fill = linalg.fill(%cst, %init) : i32, tensor<1x2x2x1xi32> -> tensor<1x2x2x1xi32> |
| %res = linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xi32>, tensor<3x3xi32>) |
| outs(%fill: tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32> |
| return %res : tensor<1x2x2x1xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_i32_max |
| // CHECK: linalg.pooling_nhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xi32>, memref<3x3xi32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xi32>) |
| func @pooling_nhwc_i32_max(%input: memref<1x4x4x1xi32>, %fake: memref<3x3xi32>, %output: memref<1x2x2x1xi32>) { |
| linalg.pooling_nhwc_max {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xi32>, memref<3x3xi32>) |
| outs(%output: memref<1x2x2x1xi32>) |
| return |
| } |
| |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_min_tensor |
| // CHECK: %{{.+}} = linalg.pooling_nhwc_min |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| func @pooling_nhwc_min_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3] : tensor<3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 1] : tensor<1x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x1xf32> -> tensor<1x2x2x1xf32> |
| %res = linalg.pooling_nhwc_min {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: tensor<1x4x4x1xf32>, tensor<3x3xf32>) |
| outs(%fill: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> |
| return %res : tensor<1x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_nhwc_min |
| // CHECK: linalg.pooling_nhwc_min |
| // CHECK-SAME: dilations = dense<1> : tensor<2xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<2xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x1xf32>, memref<3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x1xf32>) |
| func @pooling_nhwc_min(%input: memref<1x4x4x1xf32>, %fake: memref<3x3xf32>, %output: memref<1x2x2x1xf32>) { |
| linalg.pooling_nhwc_min {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} |
| ins(%input, %fake: memref<1x4x4x1xf32>, memref<3x3xf32>) |
| outs(%output: memref<1x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_sum_tensor |
| // CHECK: %{{.+}} = linalg.pooling_ndhwc_sum |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| func @pooling_ndhwc_sum_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32> |
| %res = linalg.pooling_ndhwc_sum {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| return %res : tensor<1x2x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_sum |
| // CHECK: linalg.pooling_ndhwc_sum |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x2x1xf32>) |
| func @pooling_ndhwc_sum(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) { |
| linalg.pooling_ndhwc_sum {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| outs(%output: memref<1x2x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_max_tensor |
| // CHECK: %{{.+}} = linalg.pooling_ndhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| func @pooling_ndhwc_max_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32> |
| %res = linalg.pooling_ndhwc_max {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| return %res : tensor<1x2x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_max |
| // CHECK: linalg.pooling_ndhwc_max |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x2x1xf32>) |
| func @pooling_ndhwc_max(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) { |
| linalg.pooling_ndhwc_max {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| outs(%output: memref<1x2x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_min_tensor |
| // CHECK: %{{.+}} = linalg.pooling_ndhwc_min |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| func @pooling_ndhwc_min_tensor(%input: tensor<1x4x4x4x1xf32>) -> tensor<1x2x2x2x1xf32> { |
| %fake = linalg.init_tensor [3, 3, 3] : tensor<3x3x3xf32> |
| %init = linalg.init_tensor [1, 2, 2, 2, 1] : tensor<1x2x2x2x1xf32> |
| %cst = arith.constant 0.000000e+00 : f32 |
| %fill = linalg.fill(%cst, %init) : f32, tensor<1x2x2x2x1xf32> -> tensor<1x2x2x2x1xf32> |
| %res = linalg.pooling_ndhwc_min {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: tensor<1x4x4x4x1xf32>, tensor<3x3x3xf32>) |
| outs(%fill: tensor<1x2x2x2x1xf32>) -> tensor<1x2x2x2x1xf32> |
| return %res : tensor<1x2x2x2x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @pooling_ndhwc_min |
| // CHECK: linalg.pooling_ndhwc_min |
| // CHECK-SAME: dilations = dense<1> : tensor<3xi64> |
| // CHECK-SAME: strides = dense<1> : tensor<3xi64> |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| // CHECK-SAME: outs(%{{.+}} : memref<1x2x2x2x1xf32>) |
| func @pooling_ndhwc_min(%input: memref<1x4x4x4x1xf32>, %fake: memref<3x3x3xf32>, %output: memref<1x2x2x2x1xf32>) { |
| linalg.pooling_ndhwc_min {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>} |
| ins(%input, %fake: memref<1x4x4x4x1xf32>, memref<3x3x3xf32>) |
| outs(%output: memref<1x2x2x2x1xf32>) |
| return |
| } |
| |
| // ----- |
| |
| #map0 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 * 2, d2 * 2 + d5, d6)> |
| #map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)> |
| #map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> |
| func @conv_interface_wrong_input_indexing_map( |
| %arg0 : tensor<?x?x?x?xf32>, %arg2 : tensor<?x?x?x?xf32>, %arg1 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> { |
| // expected-error @+1 {{unexpected input index map for convolutions}} |
| %0 = "linalg.conv_2d_nhwc_hwcf"(%arg0, %arg1, %arg2) ( { |
| ^bb0(%arg3: f32, %arg4: f32, %arg5 : f32): // no predecessors |
| %1 = "arith.mulf"(%arg3, %arg4) : (f32, f32) -> f32 |
| %2 = "arith.addf"(%arg5, %1) : (f32, f32) -> f32 |
| "linalg.yield"(%2) : (f32) -> () |
| }) {dilations = dense<1> : tensor<2xi64>, linalg.memoized_indexing_maps = [#map0, #map1, #map2], operand_segment_sizes = dense<[2, 1]> : vector<2xi32>, strides = dense<2> : tensor<2xi64>} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> |
| return %0 : tensor<?x?x?x?xf32> |
| } |
| |
| // ----- |
| |
| #map0 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 + d4, d2 + d5, d6)> |
| #map1 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3, d5 + 1)> |
| #map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> |
| func @conv_interface_wrong_num_operands( |
| %arg0 : tensor<?x?x?x?xf32>, %arg1 : tensor<?x?x?x?x?xf32>, %arg2 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> { |
| // expected-error @+1 {{expected output/filter indexing maps to be projected permutations}} |
| %0 = "linalg.conv_2d_nhwc_hwcf"(%arg0, %arg1, %arg2) ( { |
| ^bb0(%arg3: f32, %arg4: f32, %arg5 : f32): // no predecessors |
| %1 = "arith.mulf"(%arg3, %arg4) : (f32, f32) -> f32 |
| %2 = "arith.addf"(%arg5, %1) : (f32, f32) -> f32 |
| "linalg.yield"(%2) : (f32) -> () |
| }) {dilations = dense<1> : tensor<2xi64>, linalg.memoized_indexing_maps = [#map0, #map1, #map2], operand_segment_sizes = dense<[2, 1]> : vector<2xi32>, strides = dense<1> : tensor<2xi64>} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> |
| return %0 : tensor<?x?x?x?xf32> |
| } |
