| // 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<16xf32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<[3, 0, 1, 2]> : tensor<4xi32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.transpose"(%arg1, %[[VAR1]]) |
| // CHECK: %[[VAR3:.*]] = "tosa.conv2d"(%arg0, %2, %0) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<16xf32>} |
| // CHECK: %[[VAR1:.*]] = "tosa.depthwise_conv2d"(%arg0, %arg1, %0) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} |
| 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: %[[VAR0:.*]] = "tosa.const"() {value = dense<[2, 0, 1, 3]> : tensor<4xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<16xf32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.transpose"(%arg1, %0) |
| // CHECK: %[[VAR3:.*]] = "tosa.transpose_conv2d"(%arg0, %2, %1) {dilation = [1, 1], out_pad = [0, 0], out_shape = [1, 32, 32, 16], stride = [1, 1]} |
| 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: %[[VAR0:.*]] = "tosa.add"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.sub"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.mul"(%arg0, %arg1) {shift = 0 : i32} |
| 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: %[[VAR0:.*]] = "tosa.exp"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.reciprocal"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.reluN"(%arg0) {max_fp = 3.40282347E+38 : f32, max_int = 0 : i64} |
| 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: %[[VAR0:.*]] = "tosa.reluN"(%arg0) {max_fp = 6.000000e+00 : f32, max_int = 0 : i64} |
| 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 |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<f32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<5.000000e-01> : tensor<f32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [1, 1]} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.mul"(%arg0, %[[VAR2]]) {shift = 0 : i32} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1]} |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.greater_equal"(%arg0, %[[VAR4]]) |
| // CHECK: %[[VAR6:.*]] = "tosa.select"(%[[VAR5]], %arg0, %[[VAR3]]) |
| func @test_leaky_relu(%arg0: tensor<4x4xf32>) -> tensor<4x4xf32> { |
| %0 = "tf.LeakyRelu"(%arg0) {alpha = 0.5 : f32} : (tensor<4x4xf32>) -> tensor<4x4xf32> |
| return %0 : tensor<4x4xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_concat |
| // CHECK: %[[VAR0:.*]] = "tosa.concat"(%arg0, %arg1) {axis = 0 : i64} |
| 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: %[[VAR0:.*]] = "tosa.bitwise_and"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.bitwise_or"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.bitwise_not"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.bitwise_xor"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.logical_and"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.logical_or"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.logical_not"(%arg0) |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_any"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_all"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_min"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_max"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<0.0769230798> : tensor<f32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [21, 3]} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1]} |
| // CHECK: %[[VAR4:.*]] = "tosa.mul"(%[[VAR2]], %[[VAR3]]) {shift = 0 : i32} |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [21, 3]} |
| 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: %[[VAR0:.*]] = "tosa.minimum"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.maximum"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.pow"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.abs"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.ceil"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.floor"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.log"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.negate"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.rsqrt"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.sigmoid"(%arg0) |
| 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: %[[VAR0:.*]] = "tosa.mul"(%arg0, %arg0) {shift = 0 : i32} |
| 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: %[[VAR0:.*]] = "tosa.equal"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.greater_equal"(%arg0, %arg1) |
| 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: %[[VAR0:.*]] = "tosa.greater"(%arg0, %arg1) |
| 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-DAG: %[[VAR0:.*]] = "tosa.greater_equal"(%arg0, %arg1) |
| // CHECK: %[[VAR1:.*]] = "tosa.logical_not"(%[[VAR0]]) |
| 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-DAG: %[[VAR0:.*]] = "tosa.greater"(%arg0, %arg1) |
| // CHECK: %[[VAR1:.*]] = "tosa.logical_not"(%[[VAR0]]) |
| 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: %[[VAR0:.*]] = "tosa.argmax"(%arg0) {axis = 0 : i64} |
| 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: %[[VAR0:.*]] = "tosa.avg_pool2d"(%arg0) {kernel = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} |
| 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: %[[VAR0:.*]] = "tosa.max_pool2d"(%arg0) {kernel = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} |
| 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: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 819]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} |
| // CHECK: %[[VAR1:.*]] = "tosa.transpose"(%arg0, %[[VAR0]]) |
| 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: %[[VAR0:.*]] = "tosa.slice"(%arg0) {size = [4, 11, 1], start = [6, 8, 0]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.slice"(%arg0) {size = [9, 21, 2], start = [4, 0, 1]} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [9, 1, 7, 3, 2, 1]} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.slice"(%[[VAR1]]) {size = [9, 1, 7, 1, 2, 1], start = [0, 0, 0, 0, 0, 0]} |
| // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [9, 7, 2]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<false> : tensor<1xi1>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK: %[[VAR2:.*]] = "tosa.select"(%[[VAR1]], %arg0, %arg1) |
| 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-DAG: %[[VAR0:.*]] = "tosa.add"(%arg0, %arg1) |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.add"(%arg2, %[[VAR0]]) |
| // CHECK: %[[VAR2:.*]] = "tosa.add"(%arg3, %[[VAR1]]) |
| 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: %[[VAR0:.*]] = "tosa.concat"(%arg0, %arg1, %arg2, %arg3) {axis = 0 : i64} |
| 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-DAG: %[[VAR0:.*]] = "tosa.concat"(%arg0, %arg1, %arg2, %arg3) {axis = 0 : i64} |
| // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [4, 13, 21, 3]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.slice"(%arg0) {size = [1, 32, 32, 8], start = [0, 0, 0, 0]} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [32, 32, 8]} |
| // CHECK: %[[VAR2:.*]] = "tosa.identityn"(%[[VAR1]]) |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<0> : tensor<3x2xi32>} |
| // CHECK: %[[VAR1:.*]] = "tosa.pad"(%arg0, %[[VAR0]]) |
| 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: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 13, 21, 3]} |
| 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: %[[VAR0:.*]] = "tosa.const"() {value = dense<[13, 21, 3]> : tensor<3xi32>} |
| 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: %[[VAR0:.*]] = "tosa.const"() {value = dense<3> : tensor<i32>} |
| func @test_rank() -> tensor<i32> { |
| %3 = "tf.Const"() {value = dense<3> : tensor<i32>} : () -> tensor<i32> |
| return %3 : tensor<i32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_elu |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<f32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.exp"(%arg0) |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.sub"(%[[VAR2]], %[[VAR3]]) |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR6:.*]] = "tosa.greater_equal"(%arg0, %[[VAR5]]) |
| // CHECK: %[[VAR7:.*]] = "tosa.select"(%[[VAR6]], %arg0, %[[VAR4]]) |
| 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-DAG: %[[VAR0:.*]] = "tosa.exp"(%arg0) |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reduce_sum"(%[[VAR0]]) {axis = 2 : i64} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reciprocal"(%[[VAR1]]) |
| // CHECK: %[[VAR3:.*]] = "tosa.mul"(%[[VAR0]], %[[VAR2]]) {shift = 0 : i32} |
| 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-DAG: %[[VAR0:.*]] = "tosa.exp"(%arg0) |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reduce_sum"(%[[VAR0]]) {axis = 2 : i64} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reciprocal"(%[[VAR1]]) |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.mul"(%[[VAR0]], %[[VAR2]]) {shift = 0 : i32} |
| // CHECK: %[[VAR4:.*]] = "tosa.log"(%[[VAR3]]) |
| 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: %[[VAR0:.*]] = "tosa.matmul"(%arg0, %arg1) |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK: %[[VAR2:.*]] = "tosa.add"(%arg0, %[[VAR1]]) |
| 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-DAG: %[[VAR0:.*]] = "tosa.reduce_sum"(%arg1) {axis = 0 : i64} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reduce_sum"(%[[VAR0]]) {axis = 1 : i64} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [3]} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [1, 1, 3]} |
| // CHECK: %[[VAR4:.*]] = "tosa.add"(%arg0, %[[VAR3]]) |
| 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_split |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.slice"(%arg0) {size = [13, 7, 3], start = [0, 0, 0]} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.slice"(%arg0) {size = [13, 7, 3], start = [0, 7, 0]} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.slice"(%arg0) {size = [13, 7, 3], start = [0, 14, 0]} |
| // CHECK: %[[VAR3:.*]] = "tosa.identityn"(%[[VAR0]], %[[VAR1]], %[[VAR2]]) |
| 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: %[[VAR0:.*]] = "tosa.const"() {value = dense<{{\[}}[0, 0], [0, 1], [0, 0]]> : tensor<3x2xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<[2, 0, 1, 3]> : tensor<4xi32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.pad"(%arg0, %[[VAR0]]) |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [13, 11, 2, 3]} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.transpose"(%[[VAR3]], %[[VAR1]]) |
| // CHECK: %[[VAR5:.*]] = "tosa.reshape"(%[[VAR4]]) {new_shape = [26, 11, 3]} |
| 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: %[[VAR0:.*]] = "tosa.const"() {value = dense<[3, 1, 2, 0]> : tensor<4xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<[2, 3, 0, 4, 1, 5]> : tensor<6xi32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.transpose"(%arg0, %[[VAR0]]) |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [2, 2, 2, 32, 32, 1]} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.transpose"(%[[VAR3]], %[[VAR1]]) |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.reshape"(%[[VAR4]]) {new_shape = [2, 64, 64, 1]} |
| // CHECK: %[[VAR6:.*]] = "tosa.slice"(%[[VAR5]]) {size = [2, 64, 64, 1], start = [0, 0, 0, 0]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 16, 2, 16, 2, 8]} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.transpose"(%[[VAR1]], %[[VAR0]]) |
| // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [1, 16, 16, 32]} |
| 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-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 32, 32, 2, 2, 2]} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.transpose"(%[[VAR1]], %[[VAR0]]) |
| // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [1, 64, 64, 2]} |
| 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_left_shift |
| // CHECK: %[[VAR0:.*]] = "tosa.logical_left_shift"(%arg0, %arg1) |
| func @test_left_shift(%arg0: tensor<4x4xi32>, %arg1: tensor<1x1xi32>) -> tensor<4x4xi32> { |
| %0 = "tf.LeftShift"(%arg0, %arg1) : (tensor<4x4xi32>, tensor<1x1xi32>) -> tensor<4x4xi32> |
| return %0 : tensor<4x4xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_right_shift |
| // CHECK: %[[VAR0:.*]] = "tosa.arithmetic_right_shift"(%arg0, %arg1) {round = false} |
| func @test_right_shift(%arg0: tensor<4x4xi32>, %arg1: tensor<1x1xi32>) -> tensor<4x4xi32> { |
| %0 = "tf.RightShift"(%arg0, %arg1) : (tensor<4x4xi32>, tensor<1x1xi32>) -> tensor<4x4xi32> |
| return %0 : tensor<4x4xi32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_one_hot |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.tile"(%[[VAR1]]) {multiples = [16, 1, 1]} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%arg2) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.tile"(%[[VAR3]]) {multiples = [16, 2, 1]} |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.reshape"(%arg0) {new_shape = [16, 1]} |
| // CHECK-DAG: %[[VAR6:.*]] = "tosa.scatter"(%[[VAR4]], %[[VAR5]], %[[VAR2]]) |
| // CHECK-DAG: %[[VAR7:.*]] = "tosa.reshape"(%[[VAR6]]) {new_shape = [16, 1, 2]} |
| // CHECK-DAG: %[[VAR8:.*]] = "tosa.transpose"(%[[VAR7]], %[[VAR0]]) |
| // CHECK: %[[VAR9:.*]] = "tosa.reshape"(%[[VAR8]]) {new_shape = [4, 4, 2]} |
| func @test_one_hot(%arg0: tensor<4x4xi32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<4x4x2xf32> { |
| %0 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32> |
| %1 = "tf.OneHot"(%arg0, %0, %arg1, %arg2) {axis = -1 : i64} : (tensor<4x4xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<4x4x2xf32> |
| return %1 : tensor<4x4x2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_fakequant_with_min_max_args |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<-2.00003052> : tensor<f32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<1.99996948> : tensor<f32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.const"() {value = dense<6.10360876E-5> : tensor<f32>} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.const"() {value = dense<16383.75> : tensor<f32>} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.const"() {value = dense<5.000000e-01> : tensor<f32>} |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR6:.*]] = "tosa.minimum"(%arg0, %[[VAR5]]) |
| // CHECK-DAG: %[[VAR7:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR8:.*]] = "tosa.maximum"(%[[VAR6]], %[[VAR7]]) |
| // CHECK-DAG: %[[VAR9:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR10:.*]] = "tosa.sub"(%[[VAR8]], %[[VAR9]]) |
| // CHECK-DAG: %[[VAR11:.*]] = "tosa.reshape"(%[[VAR3]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR12:.*]] = "tosa.mul"(%[[VAR10]], %[[VAR11]]) {shift = 0 : i32} |
| // CHECK-DAG: %[[VAR13:.*]] = "tosa.reshape"(%[[VAR4]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR14:.*]] = "tosa.add"(%[[VAR12]], %[[VAR13]]) |
| // CHECK-DAG: %[[VAR15:.*]] = "tosa.floor"(%[[VAR14]]) |
| // CHECK-DAG: %[[VAR16:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [1, 1, 1]} |
| // CHECK-DAG: %[[VAR17:.*]] = "tosa.mul"(%[[VAR15]], %[[VAR16]]) {shift = 0 : i32} |
| // CHECK-DAG: %[[VAR18:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 1, 1]} |
| // CHECK: %[[VAR19:.*]] = "tosa.add"(%[[VAR17]], %[[VAR18]]) |
| 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> |
| } |
| |
| // ----- |
| // CHECK-LABEL: test_gather |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<{{.*}}> : tensor<7x7xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<[0, 1, 2]> : tensor<3xi32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.const"() {value = dense<[0, 1, 2, 3]> : tensor<4xi32>} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.transpose"(%arg0, %[[VAR1]]) |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.reshape"(%[[VAR3]]) {new_shape = [1, 13, 63]} |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [1, 49]} |
| // CHECK-DAG: %[[VAR6:.*]] = "tosa.gather"(%[[VAR4]], %[[VAR5]]) |
| // CHECK-DAG: %[[VAR7:.*]] = "tosa.reshape"(%[[VAR6]]) {new_shape = [7, 7, 21, 3]} |
| // CHECK: %[[VAR8:.*]] = "tosa.transpose"(%[[VAR7]], %[[VAR2]]) |
| func @test_gather(%arg0: tensor<13x21x3xf32>) -> tensor<7x7x21x3xf32> { |
| %0 = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32> |
| %1 = "tf.Const"() {device = "", value = dense<[[9, 8, 11, 10, 11, 0, 12], [7, 0, 1, 5, 2, 9, 6], [7, 9, 10, 8, 0, 3, 0], [8, 9, 10, 4, 9, 12, 6], [0, 2, 4, 3, 6, 11, 8], [5, 8, 7, 2, 4, 10, 5], [4, 12, 8, 3, 12, 9, 1]]> : tensor<7x7xi32>} : () -> tensor<7x7xi32> |
| %2 = "tf.GatherV2"(%arg0, %1, %0) {batch_dims = 0 : i64, device = ""} : (tensor<13x21x3xf32>, tensor<7x7xi32>, tensor<i32>) -> tensor<7x7x21x3xf32> |
| %3 = "tf.Identity"(%2) {device = ""} : (tensor<7x7x21x3xf32>) -> tensor<7x7x21x3xf32> |
| return %2 : tensor<7x7x21x3xf32> |
| } |
| |
| // ----- |
| // CHECK-LABEL: test_gather_nd |
| // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() {value = dense<{{.*}}> : tensor<6x7x1xi32>} |
| // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() {value = dense<1> : tensor<1xi32>} |
| // CHECK-DAG: %[[VAR2:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 13, 63]} |
| // CHECK-DAG: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR0]]) {new_shape = [42, 1]} |
| // CHECK-DAG: %[[VAR4:.*]] = "tosa.reshape"(%[[VAR1]]) {new_shape = [1, 1]} |
| // CHECK-DAG: %[[VAR5:.*]] = "tosa.mul"(%[[VAR3]], %[[VAR4]]) {shift = 0 : i32} |
| // CHECK-DAG: %[[VAR6:.*]] = "tosa.reduce_sum"(%[[VAR5]]) {axis = 1 : i64} |
| // CHECK-DAG: %[[VAR7:.*]] = "tosa.reshape"(%[[VAR6]]) {new_shape = [1, 42]} |
| // CHECK-DAG: %[[VAR8:.*]] = "tosa.gather"(%[[VAR2]], %[[VAR7]]) |
| // CHECK: %[[VAR9:.*]] = "tosa.reshape"(%[[VAR8]]) {new_shape = [6, 7, 21, 3]} |
| func @test_gather_nd(%arg0: tensor<13x21x3xf32>) -> tensor<6x7x21x3xf32> { |
| %0 = "tf.Const"() {device = "", value = dense<[[[0], [5], [3], [12], [2], [4], [3]], [[11], [1], [11], [10], [3], [12], [8]], [[5], [3], [1], [11], [3], [10], [0]], [[0], [8], [4], [7], [3], [12], [2]], [[7], [6], [11], [4], [2], [10], [11]], [[11], [1], [11], [1], [1], [11], [8]]]> : tensor<6x7x1xi32>} : () -> tensor<6x7x1xi32> |
| %1 = "tf.GatherNd"(%arg0, %0) {device = ""} : (tensor<13x21x3xf32>, tensor<6x7x1xi32>) -> tensor<6x7x21x3xf32> |
| %2 = "tf.Identity"(%1) {device = ""} : (tensor<6x7x21x3xf32>) -> tensor<6x7x21x3xf32> |
| return %1 : tensor<6x7x21x3xf32> |
| } |
