| // RUN: tf-opt --tf-to-tosa-pipeline --verify-each %s | FileCheck %s |
| |
| // Operations for testing tf-to-tosa-pipeline |
| // TODO: These tests are fairly minimal. Expand the checks to be more robust. |
| |
| // ----- |
| |
| // CHECK-LABEL: test_conv2d |
| // CHECK: tosa.const |
| // CHECK: tosa.const |
| // CHECK: tosa.transpose |
| // CHECK: tosa.conv2d |
| func @test_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1x1x8x16xf32>) -> tensor<1x32x32x16xf32> { |
| %3 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<1x32x32x8xf32>, tensor<1x1x8x16xf32>) -> tensor<1x32x32x16xf32> |
| return %3 : tensor<1x32x32x16xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_depthwise_conv2d |
| // CHECK: tosa.const |
| // CHECK: tosa.depthwise_conv2d |
| func @test_depthwise_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1x1x8x2xf32>) -> tensor<1x32x32x16xf32> { |
| %5 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x32x32x8xf32>, tensor<1x1x8x2xf32>) -> tensor<1x32x32x16xf32> |
| %6 = "tf.Identity"(%5) : (tensor<1x32x32x16xf32>) -> tensor<1x32x32x16xf32> |
| return %6 : tensor<1x32x32x16xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_transpose_conv2d |
| // CHECK-DAG: "tosa.const"() {value = dense<[2, 0, 1, 3]> : tensor<4xi32>} |
| // CHECK-DAG: "tosa.const"() {value = dense<0.000000e+00> : tensor<16xf32>} |
| // CHECK-DAG: tosa.transpose |
| // CHECK: tosa.transpose_conv2d |
| func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1x1x16x8xf32>) -> tensor<1x32x32x16xf32> { |
| %3 = "tf.Const"() {value = dense<[1, 32, 32, 16]> : tensor<4xi32>} : () -> tensor<4xi32> |
| %4 = "tf.Conv2DBackpropInput"(%3, %arg1, %arg0) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<4xi32>, tensor<1x1x16x8xf32>, tensor<1x32x32x8xf32>) -> tensor<1x32x32x16xf32> |
| return %4 : tensor<1x32x32x16xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_add |
| // CHECK: tosa.add |
| func @test_add(%arg0: tensor<13x21x1xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Add"(%arg0, %arg1) : (tensor<13x21x1xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_sub |
| // CHECK: tosa.sub |
| func @test_sub(%arg0: tensor<1x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Sub"(%arg0, %arg1) : (tensor<1x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_mul |
| // CHECK: tosa.mul |
| func @test_mul(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Mul"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<13x1x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_exp |
| // CHECK: tosa.exp |
| func @test_exp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Exp"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_rcp |
| // CHECK: tosa.reciprocal |
| func @test_rcp(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Reciprocal"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_relu |
| // CHECK: tosa.reluN |
| func @test_relu(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Relu"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_relu6 |
| // CHECK: tosa.reluN |
| func @test_relu6(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Relu6"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_leaky_relu |
| func @test_leaky_relu(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.LeakyRelu"(%arg0) {alpha = 0.707330704 : f32} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_concat |
| // CHECK: tosa.concat |
| func @test_concat(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<26x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32> |
| %3 = "tf.ConcatV2"(%arg0, %arg1, %2) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<i32>) -> tensor<26x21x3xf32> |
| return %3 : tensor<26x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_bitwise_and |
| // CHECK: tosa.bitwise_and |
| func @test_bitwise_and(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x21x1xi32>) -> tensor<13x21x3xi32> { |
| %2 = "tf.BitwiseAnd"(%arg0, %arg1) : (tensor<13x21x3xi32>, tensor<13x21x1xi32>) -> tensor<13x21x3xi32> |
| return %2 : tensor<13x21x3xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_bitwise_or |
| // CHECK: tosa.bitwise_or |
| func @test_bitwise_or(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1x3xi32>) -> tensor<13x21x3xi32> { |
| %2 = "tf.BitwiseOr"(%arg0, %arg1) : (tensor<13x21x3xi32>, tensor<13x1x3xi32>) -> tensor<13x21x3xi32> |
| return %2 : tensor<13x21x3xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_bitwise_not |
| // CHECK: tosa.bitwise_not |
| func @test_bitwise_not(%arg0: tensor<13x21x1xi32>) -> tensor<13x21x1xi32> { |
| %2 = "tf.Invert"(%arg0) : (tensor<13x21x1xi32>) -> tensor<13x21x1xi32> |
| return %2 : tensor<13x21x1xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_bitwise_xor |
| // CHECK: tosa.bitwise_xor |
| func @test_bitwise_xor(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> { |
| %2 = "tf.BitwiseXor"(%arg0, %arg1) : (tensor<13x21x1xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32> |
| return %2 : tensor<13x21x3xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_logical_and |
| // CHECK: tosa.logical_and |
| func @test_logical_and(%arg0: tensor<13x21x3xi1>, %arg1: tensor<13x21x1xi1>) -> tensor<13x21x3xi1> { |
| %2 = "tf.LogicalAnd"(%arg0, %arg1) : (tensor<13x21x3xi1>, tensor<13x21x1xi1>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_logical_or |
| // CHECK: tosa.logical_or |
| func @test_logical_or(%arg0: tensor<13x1x3xi1>, %arg1: tensor<13x21x3xi1>) -> tensor<13x21x3xi1> { |
| %2 = "tf.LogicalOr"(%arg0, %arg1) : (tensor<13x1x3xi1>, tensor<13x21x3xi1>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_logical_not |
| // CHECK: tosa.logical_not |
| func @test_logical_not(%arg0: tensor<1x21x3xi1>) -> tensor<1x21x3xi1> { |
| %2 = "tf.LogicalNot"(%arg0) : (tensor<1x21x3xi1>) -> tensor<1x21x3xi1> |
| return %2 : tensor<1x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_any |
| // CHECK: tosa.reduce_any |
| // CHECK: tosa.reshape |
| func @test_reduce_any(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Any"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xi1>, tensor<1xi32>) -> tensor<21x3xi1> |
| return %3 : tensor<21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_all |
| // CHECK: tosa.reduce_all |
| // CHECK: tosa.reshape |
| func @test_reduce_all(%arg0: tensor<13x21x3xi1>) -> tensor<21x3xi1> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.All"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xi1>, tensor<1xi32>) -> tensor<21x3xi1> |
| return %3 : tensor<21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_min |
| // CHECK: tosa.reduce_min |
| // CHECK: tosa.reshape |
| func @test_reduce_min(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Min"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<21x3xf32> |
| return %3 : tensor<21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_max |
| // CHECK: tosa.reduce_max |
| // CHECK: tosa.reshape |
| func @test_reduce_max(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Max"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<21x3xf32> |
| return %3 : tensor<21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_sum |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reshape |
| func @test_reduce_sum(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Sum"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<21x3xf32> |
| return %3 : tensor<21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_mean |
| // CHECK: "tosa.const"() {value = dense<0.0769230798> |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reshape |
| // CHECK: tosa.reshape |
| // CHECK: tosa.mul |
| func @test_reduce_mean(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Mean"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<21x3xf32> |
| return %3 : tensor<21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reduce_product |
| // CHECK: tosa.reduce_prod |
| // CHECK: tosa.reshape |
| func @test_reduce_product(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Prod"(%arg0, %2) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<21x3xf32> |
| return %3 : tensor<21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_min |
| // CHECK: tosa.minimum |
| func @test_min(%arg0: tensor<13x21x3xf32>, %arg1: tensor<1x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Minimum"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<1x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_max |
| // CHECK: tosa.maximum |
| func @test_max(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x1xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Maximum"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_pow |
| // CHECK: tosa.pow |
| func @test_pow(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x1xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Pow"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<13x21x1xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_abs |
| // CHECK: tosa.abs |
| func @test_abs(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Abs"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_ceil |
| // CHECK: tosa.ceil |
| func @test_ceil(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Ceil"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_floor |
| // CHECK: tosa.floor |
| func @test_floor(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Floor"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_log |
| // CHECK: tosa.log |
| func @test_log(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Log"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_negate |
| // CHECK: tosa.negate |
| func @test_negate(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Neg"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_rsqrt |
| // CHECK: tosa.rsqrt |
| func @test_rsqrt(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Rsqrt"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_sigmoid |
| // CHECK: tosa.sigmoid |
| func @test_sigmoid(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Sigmoid"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_square |
| // CHECK: tosa.mul |
| func @test_square(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Square"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_equal |
| // CHECK: tosa.equal |
| func @test_equal(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xi1> { |
| %2 = "tf.Equal"(%arg0, %arg1) {incompatible_shape_error = true} : (tensor<13x21x3xf32>, tensor<13x1x3xf32>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_greater_equal |
| // CHECK: tosa.greater_equal |
| func @test_greater_equal(%arg0: tensor<13x1x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xi1> { |
| %2 = "tf.GreaterEqual"(%arg0, %arg1) : (tensor<13x1x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_greater |
| // CHECK: tosa.greater |
| func @test_greater(%arg0: tensor<13x21x1xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xi1> { |
| %2 = "tf.Greater"(%arg0, %arg1) : (tensor<13x21x1xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_less |
| // CHECK: tosa.greater_equal |
| // CHECK: tosa.logical_not |
| func @test_less(%arg0: tensor<13x1x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xi1> { |
| %2 = "tf.Less"(%arg0, %arg1) : (tensor<13x1x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_less_equal |
| // CHECK: tosa.greater |
| // CHECK: tosa.logical_not |
| func @test_less_equal(%arg0: tensor<13x21x3xf32>, %arg1: tensor<1x21x3xf32>) -> tensor<13x21x3xi1> { |
| %2 = "tf.LessEqual"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<1x21x3xf32>) -> tensor<13x21x3xi1> |
| return %2 : tensor<13x21x3xi1> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_argmax |
| // CHECK: tosa.argmax |
| func @test_argmax(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xi32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32> |
| %3 = "tf.ArgMax"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<i32>) -> tensor<21x3xi32> |
| return %3 : tensor<21x3xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_avg_pool2d |
| // CHECK: tosa.avg_pool2d |
| func @test_avg_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> { |
| %2 = "tf.AvgPool"(%arg0) {data_format = "NHWC", ksize = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> |
| return %2 : tensor<1x32x32x8xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_max_pool2d |
| // CHECK: tosa.max_pool2d |
| func @test_max_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> { |
| %2 = "tf.MaxPool"(%arg0) {data_format = "NHWC", explicit_paddings = [], ksize = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> |
| return %2 : tensor<1x32x32x8xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reshape |
| // CHECK: tosa.reshape |
| func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> { |
| %0 = "tf.Const"() {value = dense<[1, 819]> : tensor<2xi32>} : () -> tensor<2xi32> |
| %3 = "tf.Reshape"(%arg0, %0) : (tensor<13x21x3xf32>, tensor<2xi32>) -> tensor<1x819xf32> |
| %4 = "tf.Identity"(%3) : (tensor<1x819xf32>) -> tensor<1x819xf32> |
| return %4 : tensor<1x819xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_transpose |
| // CHECK: tosa.const |
| // CHECK: tosa.transpose |
| func @test_transpose(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> { |
| %2 = "tf.Const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32> |
| %3 = "tf.Transpose"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21xf32> |
| return %3 : tensor<3x13x21xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_slice |
| // CHECK: tosa.slice |
| func @test_slice(%arg0: tensor<13x21x3xf32>) -> tensor<4x11x1xf32> { |
| %2 = "tf.Const"() {value = dense<[6, 8, 0]> : tensor<3xi64>} : () -> tensor<3xi64> |
| %3 = "tf.Const"() {value = dense<[4, 11, 1]> : tensor<3xi64>} : () -> tensor<3xi64> |
| %4 = "tf.Slice"(%arg0, %2, %3) : (tensor<13x21x3xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<4x11x1xf32> |
| return %4 : tensor<4x11x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_strided_slice |
| // CHECK: tosa.slice |
| // CHECK: tosa.reshape |
| // CHECK: tosa.slice |
| // CHECK: tosa.reshape |
| func @test_strided_slice(%arg0: tensor<13x21x3xf32>) -> tensor<9x7x2xf32> { |
| %2 = "tf.Const"() {value = dense<[4, 0, 1]> : tensor<3xi64>} : () -> tensor<3xi64> |
| %3 = "tf.Const"() {value = dense<[13, 21, 3]> : tensor<3xi64>} : () -> tensor<3xi64> |
| %4 = "tf.Const"() {value = dense<[1, 3, 1]> : tensor<3xi64>} : () -> tensor<3xi64> |
| %5 = "tf.StridedSlice"(%arg0, %2, %3, %4) {begin_mask = 2 : i64, ellipsis_mask = 0 : i64, end_mask = 3 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<13x21x3xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<9x7x2xf32> |
| return %5 : tensor<9x7x2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_select |
| // CHECK: tosa.const |
| // CHECK: tosa.reshape |
| // CHECK: tosa.select |
| func @test_select(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<false> : tensor<1xi1>} : () -> tensor<1xi1> |
| %3 = "tf.SelectV2"(%2, %arg0, %arg1) : (tensor<1xi1>, tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %3 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_addn |
| // CHECK: tosa.add |
| // CHECK: tosa.add |
| // CHECK: tosa.add |
| func @test_addn(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>, %arg2: tensor<13x21x3xf32>, %arg3: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.AddN"(%arg0, %arg1, %arg2, %arg3) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_concatv2 |
| // CHECK: tosa.concat |
| // CHECK: tosa.concat |
| // CHECK: tosa.concat |
| func @test_concatv2(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>, %arg2: tensor<13x21x3xf32>, %arg3: tensor<13x21x3xf32>) -> tensor<52x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32> |
| %3 = "tf.ConcatV2"(%arg0, %arg1, %arg2, %arg3, %2) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<i32>) -> tensor<52x21x3xf32> |
| return %3 : tensor<52x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_stack |
| // CHECK: tosa.concat |
| // CHECK: tosa.concat |
| // CHECK: tosa.concat |
| // CHECK: tosa.reshape |
| func @test_stack(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>, %arg2: tensor<13x21x3xf32>, %arg3: tensor<13x21x3xf32>) -> tensor<4x13x21x3xf32> { |
| %2 = "tf.Pack"(%arg0, %arg1, %arg2, %arg3) {axis = 0 : i64} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<4x13x21x3xf32> |
| return %2 : tensor<4x13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_unstack |
| // CHECK: tosa.slice |
| // CHECK: tosa.reshape |
| // CHECK: tosa.identityn |
| func @test_unstack(%arg0: tensor<1x32x32x8xf32>) -> tensor<32x32x8xf32> { |
| %2 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<1x32x32x8xf32>) -> tensor<32x32x8xf32> |
| %3 = "tf.Identity"(%2) : (tensor<32x32x8xf32>) -> tensor<32x32x8xf32> |
| return %3 : tensor<32x32x8xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_pad |
| // CHECK: tosa.const |
| // CHECK: tosa.pad |
| func @test_pad(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32> |
| %3 = "tf.Pad"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<3x2xi32>) -> tensor<13x21x3xf32> |
| return %3 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_expand_dims |
| // CHECK: tosa.reshape |
| func @test_expand_dims(%arg0: tensor<13x21x3xf32>) -> tensor<1x13x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32> |
| %3 = "tf.ExpandDims"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<i32>) -> tensor<1x13x21x3xf32> |
| return %3 : tensor<1x13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_shape |
| // CHECK: tosa.const |
| func @test_shape() -> tensor<3xi32> { |
| %3 = "tf.Const"() {value = dense<[13, 21, 3]> : tensor<3xi32>} : () -> tensor<3xi32> |
| return %3 : tensor<3xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_rank |
| // CHECK: tosa.const |
| func @test_rank() -> tensor<i32> { |
| %3 = "tf.Const"() {value = dense<3> : tensor<i32>} : () -> tensor<i32> |
| return %3 : tensor<i32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_elu |
| // CHECK: tosa.const |
| // CHECK: tosa.const |
| // CHECK: tosa.exp |
| // CHECK: tosa.reshape |
| // CHECK: tosa.sub |
| // CHECK: tosa.reshape |
| // CHECK: tosa.greater_equal |
| // CHECK: tosa.select |
| func @test_elu(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Elu"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_softmax |
| // CHECK: tosa.exp |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reciprocal |
| // CHECK: tosa.mul |
| func @test_softmax(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Softmax"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_log_softmax |
| // CHECK: tosa.exp |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reciprocal |
| // CHECK: tosa.mul |
| // CHECK: tosa.log |
| func @test_log_softmax(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.LogSoftmax"(%arg0) : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_matmul |
| // CHECK: tosa.matmul |
| func @test_matmul(%arg0: tensor<14x19xf32>, %arg1: tensor<19x28xf32>) -> tensor<14x28xf32> { |
| %2 = "tf.MatMul"(%arg0, %arg1) {transpose_a = false, transpose_b = false} : (tensor<14x19xf32>, tensor<19x28xf32>) -> tensor<14x28xf32> |
| return %2 : tensor<14x28xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_add_scalar |
| // CHECK: tosa.const |
| // CHECK: tosa.reshape |
| // CHECK: tosa.add |
| func @test_add_scalar(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32> |
| %3 = "tf.Add"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<f32>) -> tensor<13x21x3xf32> |
| return %3 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_add_1d |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reduce_sum |
| // CHECK: tosa.reshape |
| // CHECK: tosa.reshape |
| // CHECK: tosa.add |
| func @test_add_1d(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %0 = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> tensor<2xi32> |
| %3 = "tf.Sum"(%arg1, %0) {keep_dims = false} : (tensor<13x21x3xf32>, tensor<2xi32>) -> tensor<3xf32> |
| %4 = "tf.Add"(%arg0, %3) : (tensor<13x21x3xf32>, tensor<3xf32>) -> tensor<13x21x3xf32> |
| return %4 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_add_1d_const |
| // CHECK: tosa.add |
| func @test_add_1d_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %3 = "tf.Add"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %3 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_split |
| // CHECK: tosa.slice |
| // CHECK: tosa.slice |
| // CHECK: tosa.slice |
| // CHECK: tosa.identityn |
| func @test_split(%arg0: tensor<13x21x3xf32>) -> (tensor<13x7x3xf32>, tensor<13x7x3xf32>, tensor<13x7x3xf32>) { |
| %6 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32> |
| %7:3 = "tf.Split"(%6, %arg0) : (tensor<i32>, tensor<13x21x3xf32>) -> (tensor<13x7x3xf32>, tensor<13x7x3xf32>, tensor<13x7x3xf32>) |
| return %7#0, %7#1, %7#2 : tensor<13x7x3xf32>, tensor<13x7x3xf32>, tensor<13x7x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_tile |
| // CHECK: tosa.tile |
| func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> { |
| %2 = "tf.Const"() {value = dense<[3, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32> |
| %3 = "tf.Tile"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<39x21x6xf32> |
| %4 = "tf.Identity"(%3) : (tensor<39x21x6xf32>) -> tensor<39x21x6xf32> |
| return %4 : tensor<39x21x6xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_reverse |
| // CHECK: tosa.reverse |
| func @test_reverse(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.ReverseV2"(%arg0, %2) : (tensor<13x21x3xf32>, tensor<1xi32>) -> tensor<13x21x3xf32> |
| return %3 : tensor<13x21x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_space_to_batch |
| // CHECK-DAG: "tosa.const"() {value = dense<{{\[}}[0, 0], [0, 1], [0, 0]]> |
| // CHECK-DAG: "tosa.const"() {value = dense<[2, 0, 1, 3]> |
| // CHECK: tosa.pad |
| // CHECK: tosa.reshape |
| // CHECK: tosa.transpose |
| // CHECK: tosa.reshape |
| func @test_space_to_batch(%arg0: tensor<13x21x3xf32>) -> tensor<26x11x3xf32> { |
| %2 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32> |
| %3 = "tf.Const"() {value = dense<[[0, 1]]> : tensor<1x2xi32>} : () -> tensor<1x2xi32> |
| %4 = "tf.SpaceToBatchND"(%arg0, %2, %3) : (tensor<13x21x3xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<26x11x3xf32> |
| return %4 : tensor<26x11x3xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_batch_to_space |
| // CHECK-DAG: "tosa.const"() {value = dense<[3, 1, 2, 0]> |
| // CHECK-DAG: "tosa.const"() {value = dense<[2, 3, 0, 4, 1, 5]> |
| // CHECK: tosa.transpose |
| // CHECK: tosa.reshape |
| // CHECK: tosa.transpose |
| // CHECK: tosa.reshape |
| // CHECK: tosa.slice |
| func @test_batch_to_space(%arg0: tensor<1x32x32x8xf32>) -> tensor<2x64x64x1xf32> { |
| %2 = "tf.Const"() {value = dense<2> : tensor<2xi32>} : () -> tensor<2xi32> |
| %3 = "tf.Const"() {value = dense<0> : tensor<2x2xi32>} : () -> tensor<2x2xi32> |
| %4 = "tf.Const"() {value = dense<[3, 1, 2, 0]> : tensor<4xi32>} : () -> tensor<4xi32> |
| %5 = "tf.Transpose"(%arg0, %4) : (tensor<1x32x32x8xf32>, tensor<4xi32>) -> tensor<8x32x32x1xf32> |
| %6 = "tf.BatchToSpaceND"(%5, %2, %3) : (tensor<8x32x32x1xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<2x64x64x1xf32> |
| return %6 : tensor<2x64x64x1xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_space_to_depth |
| // CHECK: "tosa.const"() {value = dense<[0, 1, 3, 2, 4, 5]> |
| // CHECK: tosa.reshape |
| // CHECK: tosa.transpose |
| // CHECK: tosa.reshape |
| func @test_space_to_depth(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x16x16x32xf32> { |
| %2 = "tf.SpaceToDepth"(%arg0) {block_size = 2 : i64, data_format = "NHWC"} : (tensor<1x32x32x8xf32>) -> tensor<1x16x16x32xf32> |
| return %2 : tensor<1x16x16x32xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_depth_to_space |
| // CHECK: "tosa.const"() {value = dense<[0, 1, 3, 2, 4, 5]> |
| // CHECK: tosa.reshape |
| // CHECK: tosa.transpose |
| // CHECK: tosa.reshape |
| func @test_depth_to_space(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x2xf32> { |
| %2 = "tf.DepthToSpace"(%arg0) {block_size = 2 : i64, data_format = "NHWC"} : (tensor<1x32x32x8xf32>) -> tensor<1x64x64x2xf32> |
| return %2 : tensor<1x64x64x2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_fakequant_with_min_max_args |
| // CHECK-DAG: "tosa.const"() {value = dense<16383.75> : tensor<f32>} |
| // CHECK-DAG: "tosa.const"() {value = dense<-1.000000e+00> : tensor<f32>} |
| // CHECK-DAG: "tosa.const"() {value = dense<6.10360876E-5> : tensor<f32>} |
| // CHECK: tosa.reshape |
| // CHECK: tosa.mul |
| // CHECK: tosa.reshape |
| // CHECK: tosa.add |
| // CHECK: tosa.cast |
| // CHECK: tosa.rescale |
| // CHECK: tosa.rescale |
| // CHECK: tosa.cast |
| // CHECK: tosa.reshape |
| // CHECK: tosa.sub |
| // CHECK: tosa.reshape |
| // CHECK: tosa.mul |
| func @test_fakequant_with_min_max_args(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> { |
| %2 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {max = 2.000000e+00 : f32, min = -2.000000e+00 : f32, narrow_range = false, num_bits = 16 : i64} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32> |
| return %2 : tensor<13x21x3xf32> |
| } |
