| // RUN: tf-opt %s -tfl-legalize-tf | FileCheck %s --dump-input-on-failure |
| |
| func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { |
| %0 = "tf.Add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| return %0: tensor<1xf32> |
| |
| // CHECK-LABEL: add |
| // CHECK: tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32> |
| // CHECK: return |
| } |
| |
| // CHECK-LABEL: testAddHighDimsHaveSameShape |
| func @testAddHighDimsHaveSameShape(%arg0: tensor<1x2x3x4x5x6x7x8xi32>, %arg1: tensor<1x2x3x4x5x6x7x8xi32>) -> tensor<1x2x3x4x5x6x7x8xi32> { |
| // CHECK: tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} |
| %0 = "tf.Add"(%arg0, %arg1) : (tensor<1x2x3x4x5x6x7x8xi32>, tensor<1x2x3x4x5x6x7x8xi32>) -> tensor<1x2x3x4x5x6x7x8xi32> |
| return %0 : tensor<1x2x3x4x5x6x7x8xi32> |
| } |
| |
| // CHECK-LABEL: testAddTooHighBroadcastableDims |
| func @testAddTooHighBroadcastableDims(%arg0: tensor<1x2x3x4x5x6xi32>, %arg1: tensor<1x2x3x4x5x1xi32>) -> tensor<1x2x3x4x5x6xi32> { |
| // expected-error @+1 {{'tfl.add' op failed to verify that operand #0 and operand #1 have the same shape or broadcastable shapes within the rank 4}} |
| %0 = "tf.Add"(%arg0, %arg1) : (tensor<1x2x3x4x5x6xi32>, tensor<1x2x3x4x5x1xi32>) -> tensor<1x2x3x4x5x6xi32> |
| return %0 : tensor<1x2x3x4x5x6xi32> |
| } |
| |
| func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> { |
| %2 = "tf.LeakyRelu"(%arg0) {alpha = 0.1 : f32} : (tensor<1xf32>) -> tensor<1xf32> |
| return %2: tensor<1xf32> |
| |
| // CHECK-LABEL: LeakyRelu |
| // CHECK: "tfl.leaky_relu"(%arg0) {alpha = 1.000000e-01 : f32} : (tensor<1xf32>) -> tensor<1xf32> |
| } |
| |
| func @biasAdd(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> { |
| %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32> |
| return %0 : tensor<1x10x10x32xf32> |
| |
| // CHECK-LABEL: biasAdd |
| // CHECK: "tfl.add"(%arg0, %arg1) {fused_activation_function = "NONE"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32> |
| } |
| |
| func @biasAddInt(%arg0: tensor<1x10x10x32xi32>, %arg1: tensor<32xi32>) -> tensor<1x10x10x32xi32> { |
| %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xi32>, tensor<32xi32>) -> tensor<1x10x10x32xi32> |
| return %0 : tensor<1x10x10x32xi32> |
| |
| // CHECK-LABEL: biasAddInt |
| // CHECK: "tf.BiasAdd"(%arg0, %arg1) |
| } |
| |
| func @squeezeAndReshape(%arg0: tensor<1x1x10xf32>, %arg1: tensor<?x10xf32>) -> i32 { |
| %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [0]} : (tensor<1x1x10xf32>) -> tensor<1x10xf32> |
| %1 = "tf.Squeeze"(%arg1) : (tensor<?x10xf32>) -> tensor<*xf32> |
| %2 = "tf.Const"() { value = dense<[2, 5]> : tensor<2xi32> } : () -> tensor<2xi32> |
| %3 = "tf.Reshape" (%0, %2) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<2x5xf32> |
| %4 = "some_op"(%1, %3) : (tensor<*xf32>, tensor<2x5xf32>) -> i32 |
| return %4 : i32 |
| // CHECK-LABEL: squeezeAndReshape |
| // CHECK: "tfl.squeeze"(%arg0) {squeeze_dims = [0]} : (tensor<1x1x10xf32>) -> tensor<1x10xf32> |
| // CHECK: %1 = "tfl.squeeze"(%arg1) {squeeze_dims = []} : (tensor<?x10xf32>) -> tensor<*xf32> |
| // CHECK: %cst = constant dense<[2, 5]> : tensor<2xi32> |
| // CHECK: %2 = "tfl.reshape"(%0, %cst) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<2x5xf32> |
| // CHECK: %3 = "some_op"(%1, %2) : (tensor<*xf32>, tensor<2x5xf32>) -> i32 |
| // CHECK: return |
| } |
| |
| func @dynamicReshape(%arg0: tensor<*xf32>, %arg1: tensor<2xi32>) -> tensor<?x?xf32> { |
| %0 = "tf.Reshape"(%arg0, %arg1) : (tensor<*xf32>, tensor<2xi32>) -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| |
| // CHECK-LABEL: dynamicReshape |
| // CHECK-NEXT: %[[reshape:.*]] = "tfl.reshape"(%arg0, %arg1) : (tensor<*xf32>, tensor<2xi32>) -> tensor<?x?xf32> |
| // CHECK-NEXT: return %[[reshape]] : tensor<?x?xf32> |
| } |
| |
| func @dynamicReshapeI64(%arg0: tensor<*xf32>, %arg1: tensor<2xi64>) -> tensor<?x?xf32> { |
| %0 = "tf.Reshape"(%arg0, %arg1) : (tensor<*xf32>, tensor<2xi64>) -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| |
| // CHECK-LABEL: dynamicReshapeI64 |
| // CHECK-NEXT: %[[cast:.*]] = "tfl.cast"(%arg1) : (tensor<2xi64>) -> tensor<2xi32> |
| // CHECK-NEXT: %[[reshape:.*]] = "tfl.reshape"(%arg0, %[[cast]]) : (tensor<*xf32>, tensor<2xi32>) -> tensor<?x?xf32> |
| // CHECK-NEXT: return %[[reshape]] : tensor<?x?xf32> |
| } |
| |
| func @dynamicReshapeI64Fold(%arg0: tensor<*xf32>) -> tensor<1x2xf32> { |
| %cst = constant dense<[1, 2]> : tensor<2xi64> |
| %0 = "tf.Reshape"(%arg0, %cst) : (tensor<*xf32>, tensor<2xi64>) -> tensor<1x2xf32> |
| return %0 : tensor<1x2xf32> |
| |
| // CHECK-LABEL: dynamicReshapeI64Fold |
| // CHECK: %[[cst:.*]] = constant dense<[1, 2]> : tensor<2xi32> |
| // CHECK-NEXT: %[[reshape:.*]] = "tfl.reshape"(%arg0, %[[cst]]) : (tensor<*xf32>, tensor<2xi32>) -> tensor<1x2xf32> |
| // CHECK-NEXT: return %[[reshape]] : tensor<1x2xf32> |
| } |
| |
| func @avgPool2D(%arg0: tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> { |
| // OK |
| %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> |
| // Unsupported data format |
| %1 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NCHW", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> |
| // Unsupported ksize |
| %2 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [3, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> |
| // Unsupported strides |
| %3 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 3]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> |
| |
| %5 = addf %0, %1 : tensor<1x1x1x16xf32> |
| %6 = addf %2, %3 : tensor<1x1x1x16xf32> |
| %7 = addf %5, %6 : tensor<1x1x1x16xf32> |
| return %7 : tensor<1x1x1x16xf32> |
| |
| // CHECK-LABEL: func @avgPool2D |
| // CHECK: "tfl.average_pool_2d"(%arg0) {filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> |
| // CHECK: %1 = "tf.AvgPool"(%arg0) |
| // CHECK: %2 = "tf.AvgPool"(%arg0) |
| // CHECK: %3 = "tf.AvgPool"(%arg0) |
| } |
| |
| func @softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Softmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: softmax |
| // CHECK: "tfl.softmax"(%arg0) {beta = 1.000000e+00 : f32} : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @softplus(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Softplus"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: softplus |
| // CHECK: %[[exp:.*]] = "tfl.exp"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| // CHECK: %[[cst:.*]] = constant dense<1.000000e+00> : tensor<f32> |
| // CHECK: %[[add:.*]] = "tfl.add"(%[[exp]], %[[cst]]) {fused_activation_function = "NONE"} : (tensor<8x16xf32>, tensor<f32>) -> tensor<8x16xf32> |
| // CHECK: %[[log:.*]] = "tfl.log"(%[[add]]) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @fakeQuantArgsFalse(%arg0: tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> { |
| %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {min = -0.1 : f32, max = 0.2 : f32, num_bits = 3, narrow_range = false} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> |
| return %0 : tensor<8x8x8x8xf32> |
| |
| // CHECK-LABEL: fakeQuantArgsFalse |
| // CHECK: "tfl.quantize"(%arg0) {qtype = tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>} |
| // CHECK: %1 = "tfl.dequantize"(%0) : (tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>) -> tensor<8x8x8x8xf32> |
| } |
| |
| func @fakeQuantArgsTrue(%arg0: tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> { |
| %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {min = -0.1 : f32, max = 0.2 : f32, num_bits = 3, narrow_range = true} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> |
| return %0 : tensor<8x8x8x8xf32> |
| |
| // CHECK-LABEL: fakeQuantArgsTrue |
| // CHECK: "tfl.quantize"(%arg0) {qtype = tensor<8x8x8x8x!quant.uniform<u8<1:255>:f32, 0.001181102379804521:86>>} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8x!quant.uniform<u8<1:255>:f32, 0.001181102379804521:86>> |
| // CHECK: %1 = "tfl.dequantize"(%0) : (tensor<8x8x8x8x!quant.uniform<u8<1:255>:f32, 0.001181102379804521:86>>) -> tensor<8x8x8x8xf32> |
| } |
| |
| func @fakeQuantVarsFalse(%arg0: tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> { |
| %arg1 = "tf.Const"() { value = dense<-0.1> : tensor<f32> } : () -> tensor<f32> |
| %arg2 = "tf.Const"() { value = dense<0.2> : tensor<f32> } : () -> tensor<f32> |
| %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<8x8x8x8xf32>, tensor<f32>, tensor<f32>) -> tensor<8x8x8x8xf32> |
| return %0 : tensor<8x8x8x8xf32> |
| |
| // CHECK-LABEL: fakeQuantVarsFalse |
| // CHECK: "tfl.quantize"(%arg0) {qtype = tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>} |
| // CHECK: %1 = "tfl.dequantize"(%0) : (tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>) -> tensor<8x8x8x8xf32> |
| } |
| |
| func @fakeQuantVarsTrue(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<8x8x8x8xf32> { |
| %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {min = 0.0 : f32, max = 1.0 : f32, num_bits = 3, narrow_range = true} : (tensor<8x8x8x8xf32>, tensor<f32>, tensor<f32>) -> tensor<8x8x8x8xf32> |
| return %0 : tensor<8x8x8x8xf32> |
| |
| // CHECK-LABEL: fakeQuantVarsTrue |
| // CHECK: "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {max = 1.000000e+00 : f32, min = 0.000000e+00 : f32, narrow_range = true, num_bits = 3 : i64} |
| } |
| |
| func @const() -> tensor<2xi32> { |
| %0 = "tf.Const"() {device = "", name = "weights_quant/min", dtype = "tfdtype$DT_INT32", value = opaque<"tf", "0x746674656E736F722464747970653A2044545F494E5433320A74656E736F725F7368617065207B0A202064696D207B0A2020202073697A653A20320A20207D0A7D0A74656E736F725F636F6E74656E743A20225C3230305C3030305C3030305C3030305C3230305C3030305C3030305C303030220A"> : tensor<2xi32>} : () -> (tensor<2xi32>) |
| return %0: tensor<2xi32> |
| |
| // CHECK-LABEL: @const |
| // CHECK: "tfl.pseudo_const"() {value = opaque<"tf", "0x746674656E736F722464747970653A2044545F494E5433320A74656E736F725F7368617065207B0A202064696D207B0A2020202073697A653A20320A20207D0A7D0A74656E736F725F636F6E74656E743A20225C3230305C3030305C3030305C3030305C3230305C3030305C3030305C303030220A"> : tensor<2xi32>} : () -> tensor<2xi32> |
| } |
| |
| func @shape(%arg0: tensor<?x1001xf32>) -> tensor<2xi32> { |
| %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<?x1001xf32>) -> tensor<2xi32> |
| %1 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT"} : (tensor<?x1001xf32>) -> tensor<2xi32> |
| %2 = "tf.Add"(%0, %1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32> |
| return %2: tensor<2xi32> |
| |
| // CHECK-LABEL: shape |
| // CHECK: "tfl.shape"(%arg0) : (tensor<?x1001xf32>) -> tensor<2xi32> |
| // CHECK: %1 = "tfl.shape"(%arg0) : (tensor<?x1001xf32>) -> tensor<2xi32> |
| } |
| |
| func @fill(%arg0: tensor<3xi32>, %arg1: tensor<f32>) -> tensor<?x?x?xf32> { |
| %0 = "tf.Fill"(%arg0, %arg1) : (tensor<3xi32>, tensor<f32>) -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| |
| // CHECK-LABEL:fill |
| // CHECK: "tfl.fill"(%arg0, %arg1) : (tensor<3xi32>, tensor<f32>) -> tensor<?x?x?xf32> |
| } |
| |
| func @argmin(%arg0: tensor<3xi32>, %arg1: tensor<i32>) -> tensor<i32> { |
| %0 = "tf.ArgMin"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32> |
| return %0 : tensor<i32> |
| |
| // CHECK-LABEL: argmin |
| // CHECK: "tfl.arg_min"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32> |
| } |
| |
| func @sigmoid(%arg0: tensor<?x88xf32>) -> tensor<?x88xf32> { |
| %0 = "tf.Sigmoid"(%arg0) : (tensor<?x88xf32>) -> tensor<?x88xf32> |
| return %0 : tensor<?x88xf32> |
| // CHECK-LABEL: sigmoid |
| // CHECK: "tfl.logistic"(%arg0) : (tensor<?x88xf32>) -> tensor<?x88xf32> |
| } |
| |
| func @sqrt(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Sqrt"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: sqrt |
| // CHECK: "tfl.sqrt"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @square(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Square"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: square |
| // CHECK: "tfl.square"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @neg(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Neg"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: neg |
| // CHECK: "tfl.neg"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @log(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Log"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: log |
| // CHECK: "tfl.log"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @log_softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.LogSoftmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: log_softmax |
| // CHECK: "tfl.log_softmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @zeros_like(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.ZerosLike"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| // CHECK-LABEL: zeros_like |
| // CHECK: "tfl.zeros_like"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @div(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { |
| %0 = "tf.Div"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| return %0: tensor<1xf32> |
| |
| // CHECK-LABEL: div |
| // CHECK: tfl.div %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32> |
| // CHECK: return |
| } |
| |
| func @squaredDifferenceRelu(tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> { |
| ^bb0(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>): |
| %0 = "tf.SquaredDifference"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> |
| %1 = "tf.Relu6"(%0) : (tensor<1xf32>) -> tensor<1xf32> |
| return %1: tensor<1xf32> |
| |
| // CHECK-LABEL: squaredDifferenceRelu |
| // CHECK: tfl.squared_difference %arg0, %arg1 : tensor<1xf32> |
| // CHECK: %1 = "tfl.relu6"(%0) : (tensor<1xf32>) -> tensor<1xf32> |
| // CHECK: return |
| } |
| |
| func @maxPool2D(%arg0: tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> { |
| // OK |
| %0 = "tf.MaxPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> |
| |
| // Unsupported data_format |
| %1 = "tf.MaxPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NCHW", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> |
| |
| // Unsupported ksize |
| %2 = "tf.MaxPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [3, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> |
| |
| // Unsupported strides |
| %3 = "tf.MaxPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 3]} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> |
| |
| %5 = addf %0, %1 : tensor<1x1x1x16xf32> |
| %6 = addf %2, %3 : tensor<1x1x1x16xf32> |
| %7 = addf %5, %6 : tensor<1x1x1x16xf32> |
| return %7 : tensor<1x1x1x16xf32> |
| |
| // CHECK-LABEL: func @maxPool2D |
| // CHECK: "tfl.max_pool_2d"(%arg0) {filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32> |
| // CHECK: %1 = "tf.MaxPool"(%arg0) |
| // CHECK: %2 = "tf.MaxPool"(%arg0) |
| // CHECK: %3 = "tf.MaxPool"(%arg0) |
| } |
| |
| func @abs(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Abs"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL:abs |
| // CHECK: "tfl.abs"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @any(%arg0: tensor<2x2xi1>, %arg1: tensor<i32>) -> tensor<i1> { |
| %0 = "tf.Any"(%arg0, %arg1) {keep_dims = false} : (tensor<2x2xi1>, tensor<i32>) -> tensor<i1> |
| return %0 : tensor<i1> |
| |
| // CHECK-LABEL:any |
| // CHECK: "tfl.reduce_any"(%arg0, %arg1) {keep_dims = false} : (tensor<2x2xi1>, tensor<i32>) -> tensor<i1> |
| } |
| |
| func @ceil(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Ceil"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: ceil |
| // CHECK: "tfl.ceil"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| // CHECK: return |
| } |
| |
| func @cos(%arg0: tensor<f32>) -> tensor<f32> { |
| %0 = "tf.Cos"(%arg0) : (tensor<f32>) -> tensor<f32> |
| return %0 : tensor<f32> |
| |
| // CHECK-LABEL:cos |
| // CHECK: "tfl.cos"(%arg0) : (tensor<f32>) -> tensor<f32> |
| } |
| |
| func @elu(%arg0: tensor<11x16xf32>) -> tensor<11x16xf32> { |
| %0 = "tf.Elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32> |
| return %0 : tensor<11x16xf32> |
| |
| // CHECK-LABEL:elu |
| // CHECK: "tfl.elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32> |
| } |
| |
| func @expandDims(%arg0: tensor<2x2xf32>, %arg1: tensor<i32>) -> tensor<1x2x2xf32> { |
| %0 = "tf.ExpandDims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32> |
| return %0 : tensor<1x2x2xf32> |
| |
| // CHECK-LABEL:expandDims |
| // CHECK: "tfl.expand_dims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32> |
| } |
| |
| func @squeezeDefault(%arg0: tensor<1x2x2xf32>) -> tensor<2x2xf32> { |
| %0 = "tf.Squeeze"(%arg0) : (tensor<1x2x2xf32>) -> tensor<2x2xf32> |
| return %0 : tensor<2x2xf32> |
| |
| // CHECK-LABEL:squeezeDefault |
| // CHECK: "tfl.squeeze"(%arg0) {squeeze_dims = []} : (tensor<1x2x2xf32>) -> tensor<2x2xf32> |
| } |
| |
| func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> { |
| %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32> |
| return %0 : tensor<2x2xf32> |
| |
| // CHECK-LABEL:squeezeSingleAxis |
| // CHECK: "tfl.squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32> |
| } |
| |
| func @squeezeTwoAxes(%arg0: tensor<1x2x1x2xf32>) -> tensor<2x2xf32> { |
| %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [0, 2]} : (tensor<1x2x1x2xf32>) -> tensor<2x2xf32> |
| return %0 : tensor<2x2xf32> |
| |
| // CHECK-LABEL:squeezeTwoAxes |
| // CHECK: "tfl.squeeze"(%arg0) {squeeze_dims = [0, 2]} : (tensor<1x2x1x2xf32>) -> tensor<2x2xf32> |
| } |
| |
| func @gatherScalarIndices(%arg0 : tensor<3x2xf32>, %arg1 : tensor<i32>) -> tensor<2xf32> { |
| %0 = "tf.Gather"(%arg0, %arg1) : (tensor<3x2xf32>, tensor<i32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| |
| // CHECK-LABEL:gatherScalarIndices |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = 0 : i32} : (tensor<3x2xf32>, tensor<i32>) -> tensor<2xf32> |
| } |
| |
| func @gatherVectorIndices(%arg0 : tensor<2xf32>, %arg1 : tensor<3xi32>) -> tensor<3xf32> { |
| %0 = "tf.Gather"(%arg0, %arg1) : (tensor<2xf32>, tensor<3xi32>) -> tensor<3xf32> |
| return %0 : tensor<3xf32> |
| |
| // CHECK-LABEL:gatherVectorIndices |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = 0 : i32} : (tensor<2xf32>, tensor<3xi32>) -> tensor<3xf32> |
| } |
| |
| func @gatherHigherRankIndices(%arg0 : tensor<2x3x6xf32>, %arg1 : tensor<4x5xi32>) -> tensor<4x5x3x6xf32> { |
| %0 = "tf.Gather"(%arg0, %arg1) : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32> |
| return %0 : tensor<4x5x3x6xf32> |
| |
| // CHECK-LABEL:gatherHigherRankIndices |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = 0 : i32} : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32> |
| } |
| |
| func @gatherNdVectorIndices(%arg0 : tensor<3x2x2xf32>, %arg1 : tensor<2xi32>) -> tensor<2xf32> { |
| %0 = "tf.GatherNd"(%arg0, %arg1) : (tensor<3x2x2xf32>, tensor<2xi32>) -> tensor<2xf32> |
| return %0 : tensor<2xf32> |
| |
| // CHECK-LABEL:gatherNdVectorIndices |
| // CHECK: "tfl.gather_nd"(%arg0, %arg1) : (tensor<3x2x2xf32>, tensor<2xi32>) -> tensor<2xf32> |
| } |
| |
| func @gatherNdHigherRankIndices(%arg0 : tensor<4x3x2xf32>, %arg1 : tensor<2x2xi32>) -> tensor<2x2xf32> { |
| %0 = "tf.GatherNd"(%arg0, %arg1) : (tensor<4x3x2xf32>, tensor<2x2xi32>) -> tensor<2x2xf32> |
| return %0 : tensor<2x2xf32> |
| |
| // CHECK-LABEL:gatherNdHigherRankIndices |
| // CHECK: "tfl.gather_nd"(%arg0, %arg1) : (tensor<4x3x2xf32>, tensor<2x2xi32>) -> tensor<2x2xf32> |
| } |
| |
| func @gatherV2VectorIndices(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> { |
| %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32> |
| %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x3x5x20xf32> |
| return %1 : tensor<1x3x5x20xf32> |
| |
| // CHECK-LABEL:gatherV2VectorIndices |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = 1 : i32} : (tensor<1x2x20xf32>, tensor<3x5xi32>) -> tensor<1x3x5x20xf32> |
| } |
| |
| func @gatherV2VectorIndices_I64Axis(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> { |
| %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi64> } : () -> tensor<1xi64> |
| %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi64>) -> tensor<1x3x5x20xf32> |
| return %1 : tensor<1x3x5x20xf32> |
| |
| // CHECK-LABEL:gatherV2VectorIndices_I64Axis |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = 1 : i32} : (tensor<1x2x20xf32>, tensor<3x5xi32>) -> tensor<1x3x5x20xf32> |
| } |
| |
| func @gatherV2VectorIndicesNegAxis(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x2x3x5xf32> { |
| %0 = "tf.Const"() { value = dense<[-1]> : tensor<1xi32> } : () -> tensor<1xi32> |
| %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x2x3x5xf32> |
| return %1 : tensor<1x2x3x5xf32> |
| |
| // CHECK-LABEL:gatherV2VectorIndices |
| // CHECK: "tfl.gather"(%arg0, %arg1) {axis = -1 : i32} : (tensor<1x2x20xf32>, tensor<3x5xi32>) -> tensor<1x2x3x5xf32> |
| } |
| |
| func @gatherV2NonZeroBatchDims(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x2x3x5xf32> { |
| %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32> |
| %1 = "tf.GatherV2"(%arg0, %arg1, %0) {batch_dims = 1 : i64} : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x2x3x5xf32> |
| return %1 : tensor<1x2x3x5xf32> |
| |
| // CHECK-LABEL:gatherV2NonZeroBatchDims |
| // CHECK: tf.GatherV2 |
| } |
| |
| func @greater(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> { |
| %0 = "tf.Greater"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| return %0 : tensor<8x16xi1> |
| |
| // CHECK-LABEL: greater |
| // CHECK: "tfl.greater"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| // CHECK: return |
| } |
| |
| func @greater_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> { |
| %0 = "tf.GreaterEqual"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| return %0 : tensor<8x16xi1> |
| |
| // CHECK-LABEL: greater_equal |
| // CHECK: "tfl.greater_equal"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| // CHECK: return |
| } |
| |
| //TODO(b/136498739): Add failure test for non-broadcastable types, since currently |
| // we can't catch this error. |
| func @less_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> { |
| %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| return %0 : tensor<8x16xi1> |
| |
| // CHECK-LABEL: less_equal |
| // CHECK: "tfl.less_equal"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| // CHECK: return |
| } |
| |
| func @rank(%arg0: tensor<*xf32>) -> tensor<1xi32> { |
| %0 = "tf.Rank"(%arg0) : (tensor<*xf32>) -> tensor<1xi32> |
| return %0 : tensor<1xi32> |
| |
| // CHECK-LABEL:rank |
| // CHECK: "tfl.rank"(%arg0) : (tensor<*xf32>) -> tensor<1xi32> |
| } |
| |
| func @floor(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Floor"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: floor |
| // CHECK: "tfl.floor"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| // CHECK: return |
| } |
| |
| func @floor_div(tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> { |
| ^bb0(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>): |
| %0 = "tf.FloorDiv"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: floor_div |
| // CHECK: tfl.floor_div %arg0, %arg1 : tensor<8x16xf32> |
| // CHECK: return |
| } |
| |
| func @not_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> { |
| %0 = "tf.NotEqual"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| return %0 : tensor<8x16xi1> |
| |
| // CHECK-LABEL: not_equal |
| // CHECK: "tfl.not_equal"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| // CHECK: return |
| } |
| |
| func @select(%arg0: tensor<8xi1>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>) -> tensor<8xf32> { |
| %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<8xi1>, tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32> |
| return %0: tensor<8xf32> |
| |
| // CHECK-LABEL: select |
| // CHECK: "tfl.select"(%arg0, %arg1, %arg2) |
| // CHECK: return |
| } |
| |
| func @select_multidim(%arg0: tensor<8xi1>, %arg1: tensor<8x3xf32>, %arg2: tensor<8x3xf32>) -> tensor<8x3xf32> { |
| %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<8xi1>, tensor<8x3xf32>, tensor<8x3xf32>) -> tensor<8x3xf32> |
| return %0: tensor<8x3xf32> |
| |
| // CHECK-LABEL: select_multidim |
| // CHECK: "tfl.select"(%arg0, %arg1, %arg2) |
| // CHECK: return |
| } |
| |
| func @select_v2_same_shape(%arg0: tensor<8xi1>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>) -> tensor<8xf32> { |
| %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<8xi1>, tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32> |
| return %0: tensor<8xf32> |
| |
| // CHECK-LABEL: select_v2_same_shape |
| // CHECK: "tfl.select"(%arg0, %arg1, %arg2) |
| // CHECK: return |
| } |
| |
| func @select_v2_multidim(%arg0: tensor<3xi1>, %arg1: tensor<8x3xf32>, %arg2: tensor<8x3xf32>) -> tensor<8x3xf32> { |
| %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<3xi1>, tensor<8x3xf32>, tensor<8x3xf32>) -> tensor<8x3xf32> |
| return %0: tensor<8x3xf32> |
| |
| // CHECK-LABEL: select_v2_multidim |
| // CHECK: "tfl.select_v2"(%arg0, %arg1, %arg2) |
| // CHECK: return |
| } |
| |
| func @select_v2_broadcast(%arg0: tensor<4xi1>, %arg1: tensor<3x4xf32>, %arg2: tensor<8x3x4xf32>) -> tensor<8x3x4xf32> { |
| %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<4xi1>, tensor<3x4xf32>, tensor<8x3x4xf32>) -> tensor<8x3x4xf32> |
| return %0: tensor<8x3x4xf32> |
| |
| // CHECK-LABEL: select_v2_broadcast |
| // CHECK: "tfl.select_v2"(%arg0, %arg1, %arg2) |
| // CHECK: return |
| } |
| |
| func @sin(%arg0: tensor<f32>) -> tensor<f32> { |
| %0 = "tf.Sin"(%arg0) : (tensor<f32>) -> tensor<f32> |
| return %0 : tensor<f32> |
| |
| // CHECK-LABEL:sin |
| // CHECK: "tfl.sin"(%arg0) : (tensor<f32>) -> tensor<f32> |
| } |
| |
| func @topk(%arg0: tensor<8xf32>, %arg1: tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>) { |
| %0, %1 = "tf.TopKV2"(%arg0, %arg1) : (tensor<8xf32>, tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>) |
| return %0, %1: tensor<?xf32>, tensor<?xi32> |
| |
| // CHECK-LABEL: topk |
| // CHECK: "tfl.topk_v2"(%arg0, %arg1) |
| // CHECK: return |
| } |
| |
| func @topk_2(%arg0: tensor<8xf32>) -> (tensor<2xf32>, tensor<2xi32>) { |
| %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32> |
| %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<8xf32>, tensor<i32>) -> (tensor<2xf32>, tensor<2xi32>) |
| return %1#0, %1#1: tensor<2xf32>, tensor<2xi32> |
| |
| // CHECK-LABEL: topk_2 |
| // CHECK: "tfl.topk_v2"(%arg0, %cst) |
| // CHECK: return |
| } |
| |
| func @topk_3(%arg0: tensor<?x8xf32>) -> (tensor<?x2xf32>, tensor<?x2xi32>) { |
| %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32> |
| %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<?x8xf32>, tensor<i32>) -> (tensor<?x2xf32>, tensor<?x2xi32>) |
| return %1#0, %1#1: tensor<?x2xf32>, tensor<?x2xi32> |
| |
| // CHECK-LABEL: topk_3 |
| // CHECK: "tfl.topk_v2"(%arg0, %cst) : (tensor<?x8xf32>, tensor<i32>) -> (tensor<?x2xf32>, tensor<?x2xi32>) |
| // CHECK: return |
| } |
| |
| func @topk_4(%arg0: tensor<1x2x3x4xf32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>) { |
| %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32> |
| %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<1x2x3x4xf32>, tensor<i32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>) |
| return %1#0, %1#1: tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32> |
| |
| // CHECK-LABEL: topk_4 |
| // CHECK: "tfl.topk_v2"(%arg0, %cst) |
| // CHECK: return |
| } |
| |
| func @topk_5(%arg0: tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi32>) { |
| %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32> |
| %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<*xf32>, tensor<i32>) -> (tensor<*xf32>, tensor<*xi32>) |
| return %1#0, %1#1: tensor<*xf32>, tensor<*xi32> |
| |
| // CHECK-LABEL: topk_5 |
| // CHECK: "tfl.topk_v2"(%arg0, %cst) |
| // CHECK: return |
| } |
| |
| func @logicalAnd(%arg0: tensor<8xi1>, %arg1: tensor<8xi1>) -> tensor<8xi1> { |
| %0 = "tf.LogicalAnd"(%arg0, %arg1) : (tensor<8xi1>, tensor<8xi1>) -> tensor<8xi1> |
| return %0: tensor<8xi1> |
| |
| // CHECK-LABEL: logicalAnd |
| // CHECK: tfl.logical_and %arg0, %arg1 : tensor<8xi1> |
| // CHECK: return |
| } |
| |
| func @logicalNot(%arg0: tensor<8xi1>) -> tensor<8xi1> { |
| %0 = "tf.LogicalNot"(%arg0) : (tensor<8xi1>) -> tensor<8xi1> |
| return %0 : tensor<8xi1> |
| // CHECK-LABEL: logicalNot |
| // CHECK: "tfl.logical_not"(%arg0) : (tensor<8xi1>) -> tensor<8xi1> |
| } |
| |
| func @logicalOr(%arg0: tensor<8xi1>, %arg1: tensor<8xi1>) -> tensor<8xi1> { |
| %0 = "tf.LogicalOr"(%arg0, %arg1) : (tensor<8xi1>, tensor<8xi1>) -> tensor<8xi1> |
| return %0: tensor<8xi1> |
| |
| // CHECK-LABEL: logicalOr |
| // CHECK: tfl.logical_or %arg0, %arg1 : tensor<8xi1> |
| // CHECK: return |
| } |
| |
| func @addV2(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> { |
| %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> |
| return %0 : tensor<1xi32> |
| |
| // CHECK-LABEL: addV2 |
| // CHECK: tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xi32> |
| } |
| |
| func @addN(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> { |
| %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32> |
| return %0 : tensor<2x3xi32> |
| |
| // CHECK-LABEL: addN |
| // CHECK: "tfl.add_n"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32> |
| // CHECK: return |
| } |
| |
| func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> { |
| %0 = "tf.ReverseV2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32> |
| return %0 : tensor<1x2x3x4xf32> |
| |
| // CHECK-LABEL:reverse_v2 |
| // CHECK: "tfl.reverse_v2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32> |
| // CHECK: return |
| } |
| |
| func @matrix_diag(%arg0: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| %0 = "tf.MatrixDiag"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16x16xf32> |
| return %0 : tensor<8x16x16xf32> |
| |
| // CHECK-LABEL:matrix_diag |
| // CHECK: "tfl.matrix_diag"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16x16xf32> |
| } |
| |
| func @matrix_diag_v2_no_match(%arg0: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // this should have been 0. |
| %0 = constant dense<[1]> : tensor<1xi32> |
| |
| %1 = constant dense<[-1]> : tensor<1xi32> |
| %2 = constant dense<[-1]> : tensor<1xi32> |
| %3 = constant dense<[0, 0]> : tensor<2xi32> |
| %4 = "tf.MatrixDiagV2"(%arg0, %0, %1, %2, %3) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| return %4 : tensor<8x16x16xf32> |
| |
| // CHECK-LABEL: func @matrix_diag_v2_no_match( |
| // CHECK-SAME: [[VAL_0:%.*]]: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // CHECK: [[VAL_1:%.*]] = constant dense<1> : tensor<1xi32> |
| // CHECK: [[VAL_2:%.*]] = constant dense<-1> : tensor<1xi32> |
| // CHECK: [[VAL_5:%.*]] = constant dense<-1> : tensor<1xi32> |
| // CHECK: [[VAL_3:%.*]] = constant dense<0> : tensor<2xi32> |
| // CHECK: [[VAL_4:%.*]] = "tf.MatrixDiagV2"([[VAL_0]], [[VAL_1]], [[VAL_2]], [[VAL_5]], [[VAL_3]]) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| // CHECK: return [[VAL_4]] : tensor<8x16x16xf32> |
| } |
| |
| func @matrix_diag_v2(%arg0: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| %0 = constant dense<[0]> : tensor<1xi32> |
| %1 = constant dense<[-1]> : tensor<1xi32> |
| %2 = constant dense<[-1]> : tensor<1xi32> |
| %3 = constant dense<[0, 0]> : tensor<2xi32> |
| %4 = "tf.MatrixDiagV2"(%arg0, %0, %1, %2, %3) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| return %4 : tensor<8x16x16xf32> |
| |
| // CHECK-LABEL: func @matrix_diag_v2( |
| // CHECK-SAME: [[VAL_5:%.*]]: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // CHECK: [[VAL_6:%.*]] = "tfl.matrix_diag"([[VAL_5]]) : (tensor<8x16xf32>) -> tensor<8x16x16xf32> |
| // CHECK: return [[VAL_6]] : tensor<8x16x16xf32> |
| } |
| |
| func @matrix_diag_v3_no_match(%arg0: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // this should have been 0. |
| %0 = constant dense<[1]> : tensor<1xi32> |
| |
| %1 = constant dense<[-1]> : tensor<1xi32> |
| %2 = constant dense<[-1]> : tensor<1xi32> |
| %3 = constant dense<[0, 0]> : tensor<2xi32> |
| %4 = "tf.MatrixDiagV3"(%arg0, %0, %1, %2, %3) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| return %4 : tensor<8x16x16xf32> |
| |
| // CHECK-LABEL: func @matrix_diag_v3_no_match( |
| // CHECK-SAME: [[VAL_0:%.*]]: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // CHECK: [[VAL_1:%.*]] = constant dense<1> : tensor<1xi32> |
| // CHECK: [[VAL_2:%.*]] = constant dense<-1> : tensor<1xi32> |
| // CHECK: [[VAL_5:%.*]] = constant dense<-1> : tensor<1xi32> |
| // CHECK: [[VAL_3:%.*]] = constant dense<0> : tensor<2xi32> |
| // CHECK: [[VAL_4:%.*]] = "tf.MatrixDiagV3"([[VAL_0]], [[VAL_1]], [[VAL_2]], [[VAL_5]], [[VAL_3]]) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| // CHECK: return [[VAL_4]] : tensor<8x16x16xf32> |
| } |
| |
| func @matrix_diag_v3(%arg0: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| %0 = constant dense<[0]> : tensor<1xi32> |
| %1 = constant dense<[-1]> : tensor<1xi32> |
| %2 = constant dense<[-1]> : tensor<1xi32> |
| %3 = constant dense<[0, 0]> : tensor<2xi32> |
| %4 = "tf.MatrixDiagV3"(%arg0, %0, %1, %2, %3) : (tensor<8x16xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<2xi32>) -> tensor<8x16x16xf32> |
| return %4 : tensor<8x16x16xf32> |
| |
| // CHECK-LABEL: func @matrix_diag_v3( |
| // CHECK-SAME: [[VAL_5:%.*]]: tensor<8x16xf32>) -> tensor<8x16x16xf32> { |
| // CHECK: [[VAL_6:%.*]] = "tfl.matrix_diag"([[VAL_5]]) : (tensor<8x16xf32>) -> tensor<8x16x16xf32> |
| // CHECK: return [[VAL_6]] : tensor<8x16x16xf32> |
| } |
| |
| func @matrix_set_diag(%arg0: tensor<3x3xi32>, %arg1: tensor<3xi32>) -> tensor<3x3xi32> { |
| %0 = "tf.MatrixSetDiag"(%arg0, %arg1) : (tensor<3x3xi32>, tensor<3xi32>) -> tensor<3x3xi32> |
| return %0 : tensor<3x3xi32> |
| |
| // CHECK-LABEL: func @matrix_set_diag( |
| // CHECK: [[VAL_0:%.*]] = "tfl.matrix_set_diag"(%arg0, %arg1) : (tensor<3x3xi32>, tensor<3xi32>) -> tensor<3x3xi32> |
| // CHECK: return [[VAL_0]] |
| } |
| |
| func @maximum(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Maximum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL:maximum |
| // CHECK: "tfl.maximum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @minimum(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Minimum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL:minimum |
| // CHECK: "tfl.minimum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| } |
| |
| func @realDiv(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.RealDiv"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: realDiv |
| // CHECK: tfl.div %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<8x16xf32> |
| } |
| |
| func @equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> { |
| %0 = "tf.Equal"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| return %0 : tensor<8x16xi1> |
| |
| // CHECK-LABEL: equal |
| // CHECK: "tfl.equal"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> |
| // CHECK: return |
| } |
| |
| func @pad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> { |
| ^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>): |
| %0 = "tf.Pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> |
| return %0#0 : tensor<? x f32> |
| |
| // CHECK-LABEL: pad |
| // CHECK: "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32> |
| // CHECK: return |
| } |
| |
| func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> { |
| %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32> |
| return %0 : tensor<2x1x3xf32> |
| |
| // CHECK-LABEL: pow |
| // CHECK: %[[pow:.*]] = "tfl.pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32> |
| // CHECK: return |
| } |
| |
| func @tile(tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> { |
| ^bb0(%arg0: tensor<2x3xf32>, %arg1: tensor<2xi32>): |
| %cst = "tf.Const"() { value = dense<[1, 2]> : tensor<2xi32> } : () -> tensor<2xi32> |
| %0 = "tf.Tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> |
| return %0 : tensor<2x6xf32> |
| |
| // CHECK-LABEL: tile |
| // CHECK: "tfl.tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> |
| // CHECK: return |
| } |
| |
| func @padv2(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> { |
| ^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>): |
| %cst = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32> |
| %0 = "tf.PadV2"(%arg0, %arg1, %cst) : (tensor<2x1x3xf32>, tensor<3x2xi32>, tensor<f32>) -> tensor<? x f32> |
| return %0#0 : tensor<? x f32> |
| |
| // CHECK-LABEL: padv2 |
| // CHECK: "tfl.padv2"(%arg0, %arg1, %cst) : (tensor<2x1x3xf32>, tensor<3x2xi32>, tensor<f32>) -> tensor<?xf32> |
| // CHECK: return |
| } |
| |
| func @pack2Tensors(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2x2xi32> { |
| %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32> |
| return %0 : tensor<2x2xi32> |
| |
| // CHECK-LABEL: pack2Tensors |
| // CHECK: "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32> |
| } |
| |
| func @pack3Tensors(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>, %arg2 : tensor<2xi32>) -> tensor<2x3xi32> { |
| %0 = "tf.Pack"(%arg0, %arg1, %arg2) {axis = 1 : i64} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32> |
| return %0 : tensor<2x3xi32> |
| |
| // CHECK-LABEL: pack3Tensors |
| // CHECK: "tfl.pack"(%arg0, %arg1, %arg2) {axis = 1 : i32, values_count = 3 : i32} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32> |
| } |
| |
| func @packStringWithFlex(%arg0: tensor<2x!tf.string>, %arg1: tensor<2x!tf.string>) -> tensor<2x2x!tf.string> { |
| %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2x!tf.string>, tensor<2x!tf.string>) -> tensor<2x2x!tf.string> |
| return %0 : tensor<2x2x!tf.string> |
| |
| // CHECK-LABEL: packStringWithFlex |
| // CHECK: "tf.Pack"(%arg0, %arg1) : (tensor<2x!tf.string>, tensor<2x!tf.string>) -> tensor<2x2x!tf.string> |
| } |
| |
| func @packNegAxis(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>, %arg2 : tensor<2xi32>) -> tensor<2x3xi32> { |
| %0 = "tf.Pack"(%arg0, %arg1, %arg2) {axis = -1 : i64} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32> |
| return %0 : tensor<2x3xi32> |
| |
| // CHECK-LABEL: packNegAxis |
| // CHECK: "tfl.pack"(%arg0, %arg1, %arg2) {axis = -1 : i32, values_count = 3 : i32} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32> |
| } |
| |
| func @unpack2Tensors(%arg0: tensor<2x2xi32>) -> tensor<2xi32> { |
| %0:2 = "tf.Unpack"(%arg0) : (tensor<2x2xi32>) -> (tensor<2xi32>, tensor<2xi32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: unpack2Tensors |
| // CHECK: "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x2xi32>) -> (tensor<2xi32>, tensor<2xi32>) |
| } |
| |
| func @unpack3Tensors(%arg0: tensor<2x3xi32>) -> tensor<2xi32> { |
| %0:3 = "tf.Unpack"(%arg0) {axis = 1 : i64} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: unpack3Tensors |
| // CHECK: "tfl.unpack"(%arg0) {axis = 1 : i32, num = 3 : i32} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) |
| } |
| |
| func @unpackNegAxis(%arg0: tensor<2x3xi32>) -> tensor<2xi32> { |
| %0:3 = "tf.Unpack"(%arg0) {axis = -1 : i64} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: unpackNegAxis |
| // CHECK: "tfl.unpack"(%arg0) {axis = -1 : i32, num = 3 : i32} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) |
| } |
| |
| func @mean(%arg0: tensor<2x2xf32>, %arg1: tensor<1xi32>) -> tensor<1x2xf32> { |
| %0 = "tf.Mean"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32> |
| return %0 : tensor<1x2xf32> |
| |
| // CHECK-LABEL: mean |
| // CHECK: "tfl.mean"(%arg0, %arg1) {keep_dims = false} : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32> |
| } |
| |
| func @mean_true(%arg0: tensor<2x2xf32>, %arg1: tensor<1xi32>) -> tensor<1x2xf32> { |
| %0 = "tf.Mean"(%arg0, %arg1) {keep_dims = true} : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32> |
| return %0 : tensor<1x2xf32> |
| |
| // CHECK-LABEL: mean_true |
| // CHECK: "tfl.mean"(%arg0, %arg1) {keep_dims = true} : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32> |
| } |
| |
| func @sum(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Sum"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: sum |
| // CHECK: "tfl.sum"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @sum_true(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Sum"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: sum_true |
| // CHECK: "tfl.sum"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_min(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Min"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_min |
| // CHECK: "tfl.reduce_min"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_min_true(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Min"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_min_true |
| // CHECK: "tfl.reduce_min"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_max(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Max"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_max |
| // CHECK: "tfl.reduce_max"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_max_true(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Max"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_max_true |
| // CHECK: "tfl.reduce_max"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_prod(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Prod"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_prod |
| // CHECK: "tfl.reduce_prod"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @reduce_prod_true(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> { |
| %0 = "tf.Prod"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: reduce_prod_true |
| // CHECK: "tfl.reduce_prod"(%arg0, %arg1) {keep_dims = true} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32> |
| } |
| |
| func @batch_to_space_nd(%arg0: tensor<4x2x2x3xf32>, %arg1: tensor<2xi32>, %arg2: tensor<2x2xi32>) -> tensor<?xf32> { |
| %0 = "tf.BatchToSpaceND"(%arg0, %arg1, %arg2) : (tensor<4x2x2x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: batch_to_space_nd |
| // CHECK: "tfl.batch_to_space_nd"(%arg0, %arg1, %arg2) : (tensor<4x2x2x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<?xf32> |
| } |
| |
| func @space_to_batch_nd(%arg0: tensor<1x4x4x3xf32>, %arg1: tensor<2xi32>, %arg2: tensor<2x2xi32>) -> tensor<?xf32> { |
| %0 = "tf.SpaceToBatchND"(%arg0, %arg1, %arg2) : (tensor<1x4x4x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: space_to_batch_nd |
| // CHECK: "tfl.space_to_batch_nd"(%arg0, %arg1, %arg2) : (tensor<1x4x4x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<?xf32> |
| } |
| |
| func @split(%arg0: tensor<i32>, %arg1: tensor<1x4x3x3xf32>) -> tensor<1x4x3xf32> { |
| %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>) |
| return %0#0 : tensor<1x4x3xf32> |
| |
| // CHECK-LABEL: split |
| // CHECK: "tfl.split"(%arg0, %arg1) {num_splits = 3 : i32} : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>) |
| } |
| |
| func @splitv(%arg0: tensor<1x4x3x3xf32>, %arg1: tensor<2xi32>, %arg2: tensor<i32>) -> tensor<1x4x2x3xf32> { |
| %0:2 = "tf.SplitV"(%arg0, %arg1, %arg2) : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>) |
| return %0#0 : tensor<1x4x2x3xf32> |
| |
| // CHECK-LABEL: splitv |
| // CHECK: "tfl.split_v"(%arg0, %arg1, %arg2) {num_splits = 2 : i32} : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>) |
| } |
| |
| func @matmul_transposed(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>) -> tensor<40x40xf32> { |
| %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", transpose_a = false, transpose_b = true} : |
| (tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> |
| return %0 : tensor<40x40xf32> |
| // CHECK-LABEL: matmul_transposed |
| // CHECK: "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, none) -> tensor<40x40xf32> |
| } |
| |
| func @concat2Tensors(%arg0: tensor<2x1xi32>, %arg1: tensor<2x1xi32>) -> tensor<2x2xi32> { |
| %0 = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32> |
| %1 = "tf.Concat"(%0, %arg0, %arg1) : (tensor<i32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x2xi32> |
| return %1 : tensor<2x2xi32> |
| |
| // CHECK-LABEL: concat2Tensors |
| // CHECK: "tfl.concatenation"(%arg0, %arg1) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x2xi32> |
| } |
| |
| func @concat3Tensors(%arg0: tensor<2x1xi32>, %arg1: tensor<2x1xi32>, %arg2: tensor<2x1xi32>) -> tensor<2x3xi32> { |
| %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32> |
| %1 = "tf.Concat"(%0, %arg0, %arg1, %arg2) : (tensor<i32>, tensor<2x1xi32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x3xi32> |
| return %1 : tensor<2x3xi32> |
| |
| // CHECK-LABEL: concat3Tensors |
| // CHECK: "tfl.concatenation"(%arg0, %arg1, %arg2) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<2x1xi32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x3xi32> |
| } |
| |
| func @concatv2With3Tensors(%arg0: tensor<2x1xi32>, %arg1: tensor<2x1xi32>, %arg2: tensor<2x1xi32>) -> tensor<2x3xi32> { |
| %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32> |
| %1 = "tf.ConcatV2"(%arg0, %arg1, %arg2, %0) : (tensor<2x1xi32>, tensor<2x1xi32>, tensor<2x1xi32>, tensor<i32>) -> tensor<2x3xi32> |
| return %1 : tensor<2x3xi32> |
| |
| // CHECK-LABEL: concatv2With3Tensors |
| // CHECK: "tfl.concatenation"(%arg0, %arg1, %arg2) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<2x1xi32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x3xi32> |
| } |
| |
| func @resize_with_bilinear(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> { |
| %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: resize_with_bilinear |
| // CHECK: "tfl.resize_bilinear"(%arg0, %arg1) {align_corners = true, half_pixel_centers = false} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| } |
| |
| func @resize_with_bilinear_with_half_pixel_centers(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> { |
| %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = false, half_pixel_centers = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: resize_with_bilinear_with_half_pixel_centers |
| // CHECK: "tfl.resize_bilinear"(%arg0, %arg1) {align_corners = false, half_pixel_centers = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| } |
| |
| func @strided_slice(%arg0: tensor<12x2x2x5xf32>, %arg1: tensor<1xi32>, %arg2: tensor<1xi32>, %arg3: tensor<1xi32>) -> tensor<1x2x2x5xf32> { |
| %0 = "tf.StridedSlice"(%arg0, %arg1, %arg2, %arg3) {begin_mask = 0 : i64, ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<12x2x2x5xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x2x2x5xf32> |
| return %0 : tensor<1x2x2x5xf32> |
| // CHECK-LABEL: strided_slice |
| // CHECK: "tfl.strided_slice"(%arg0, %arg1, %arg2, %arg3) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 0 : i32} : (tensor<12x2x2x5xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x2x2x5xf32> |
| } |
| |
| func @strided_slice_with_constant_attributes(%arg0: tensor<10x10x10xf32>, %arg1: tensor<1xi32>, %arg2: tensor<1xi32>, %arg3: tensor<1xi32>) -> tensor<10x10xf32> { |
| %cst = constant dense<-1> : tensor<1xi32> |
| %cst_1 = constant dense<0> : tensor<1xi32> |
| %cst_2 = constant dense<1> : tensor<1xi32> |
| %0 = "tf.StridedSlice"(%arg0, %cst, %cst_1, %cst_2) {begin_mask = 0 : i64, ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<10x10x10xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<10x10xf32> |
| return %0 : tensor<10x10xf32> |
| // CHECK-LABEL: strided_slice_with_constant_attributes |
| // CHECK-DAG: [[BEGIN:%cst.*]] = constant dense<[-1, 0, 0]> : tensor<3xi32> |
| // CHECK-DAG: [[END:%cst.*]] = constant dense<[0, 10, 10]> : tensor<3xi32> |
| // CHECK-DAG: [[STRIDES:%cst.*]] = constant dense<1> : tensor<3xi32> |
| // CHECK-NEXT: "tfl.strided_slice"(%arg0, [[BEGIN]], [[END]], [[STRIDES]]) {begin_mask = 6 : i32, ellipsis_mask = 0 : i32, end_mask = 6 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<10x10x10xf32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> tensor<10x10xf32> |
| } |
| |
| func @slice1Tensor(%arg0: tensor<2x3x5xf32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<?x3x5xf32> { |
| %0 = "tf.Slice"(%arg0, %arg1, %arg2) : (tensor<2x3x5xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32> |
| return %0 : tensor<?x3x5xf32> |
| // CHECK-LABEL: slice1Tensor |
| // CHECK: "tfl.slice"(%arg0, %arg1, %arg2) : (tensor<2x3x5xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32> |
| } |
| |
| func @mirror_pad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> { |
| ^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>): |
| %0 = "tf.MirrorPad"(%arg0, %arg1) { mode = "SYMMETRIC" }: (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> |
| return %0#0 : tensor<? x f32> |
| |
| // CHECK-LABEL: mirror_pad |
| // CHECK: "tfl.mirror_pad"(%arg0, %arg1) {mode = "SYMMETRIC"} : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32> |
| // CHECK: return |
| } |
| |
| func @mirror_pad_reflect(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> { |
| ^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>): |
| %0 = "tf.MirrorPad"(%arg0, %arg1) { mode = "REFLECT" }: (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> |
| return %0#0 : tensor<? x f32> |
| |
| // CHECK-LABEL: mirror_pad_reflect |
| // CHECK: "tfl.mirror_pad"(%arg0, %arg1) {mode = "REFLECT"} : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32> |
| // CHECK: return |
| } |
| |
| func @Tanh(%arg0: tensor<1xf32>) -> tensor<1xf32> { |
| %0 = "tf.Tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> |
| return %0: tensor<1xf32> |
| |
| // CHECK-LABEL: Tanh |
| // CHECK: "tfl.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> |
| } |
| |
| func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> { |
| %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> |
| return %0 : tensor<1x2x2x5xf32> |
| |
| // CHECK-LABEL: cast |
| // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> |
| } |
| |
| func @castComplex(%arg0: tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xcomplex<f32>> { |
| %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xcomplex<f32>> |
| return %0 : tensor<1x2x2x5xcomplex<f32>> |
| |
| // CHECK-LABEL: castComplex |
| // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xcomplex<f32>> |
| } |
| |
| func @unique(%arg0: tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi32>) { |
| %0, %1 = "tf.Unique"(%arg0) : (tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi32>) |
| return %0, %1 : tensor<?xf32> , tensor<?xi32> |
| |
| // CHECK-LABEL: unique |
| // CHECK: "tfl.unique"(%arg0) : (tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi32>) |
| } |
| |
| func @unique64(%arg0: tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi64>) { |
| %0, %1 = "tf.Unique"(%arg0) : (tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi64>) |
| return %0, %1 : tensor<?xf32> , tensor<?xi64> |
| |
| // CHECK-LABEL: unique64 |
| // CHECK: "tfl.unique"(%arg0) : (tensor<5xf32>) -> (tensor<?xf32>, tensor<?xi64>) |
| } |
| |
| func @ReverseSequence(%arg0: tensor<2x3xf32>, %arg1: tensor<2xi32>) -> tensor<2x3xf32> { |
| %0 = "tf.ReverseSequence"(%arg0, %arg1) {seq_dim = 0 : i64, batch_dim = 0 : i64}: (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x3xf32> |
| return %0: tensor<2x3xf32> |
| |
| // CHECK-LABEL: ReverseSequence |
| // CHECK: "tfl.reverse_sequence"(%arg0, %arg1) {batch_dim = 0 : i32, seq_dim = 0 : i32} : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x3xf32> |
| } |
| |
| func @LRN(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> { |
| %0 = "tf.LRN"(%arg0) {depth_radius = 5 :i64, bias = 1.0 :f32, alpha = 1.0 : f32, beta = 0.5 :f32} : (tensor<2x3x4x5xf32>) -> (tensor<2x3x4x5xf32>) |
| return %0: tensor<2x3x4x5xf32> |
| |
| // CHECK-LABEL: LRN |
| // CHECK: "tfl.local_response_normalization"(%arg0) {alpha = 1.000000e+00 : f32, beta = 5.000000e-01 : f32, bias = 1.000000e+00 : f32, radius = 5 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> |
| // CHECK: return %0 : tensor<2x3x4x5xf32> |
| } |
| |
| func @OneHot(%arg0: tensor<3xi32>, %arg1: tensor<i32>, %arg2: tensor<f32>, %arg3: tensor<f32>) -> tensor<*xf32> { |
| %0 = "tf.OneHot"(%arg0, %arg1, %arg2, %arg3) {axis = -1 : i64} : (tensor<3xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<*xf32> |
| return %0: tensor<*xf32> |
| |
| // CHECK-LABEL: OneHot |
| // CHECK: "tfl.one_hot"(%arg0, %arg1, %arg2, %arg3) {axis = -1 : i32} : (tensor<3xi32>, tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<*xf32> |
| } |
| |
| func @argmax(%arg0: tensor<3xi32>, %arg1: tensor<i32>) -> tensor<i32> { |
| %0 = "tf.ArgMax"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32> |
| return %0 : tensor<i32> |
| |
| // CHECK-LABEL: argmax |
| // CHECK: "tfl.arg_max"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32> |
| } |
| |
| func @argmax64(%arg0: tensor<3xi32>, %arg1: tensor<i32>) -> tensor<i64> { |
| %0 = "tf.ArgMax"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i64> |
| return %0 : tensor<i64> |
| |
| // CHECK-LABEL: argmax64 |
| // CHECK: "tfl.arg_max"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i64> |
| } |
| |
| func @space_to_depth(%arg0: tensor<1x2x2x1xf32>) -> tensor<?xf32> { |
| %0 = "tf.SpaceToDepth"(%arg0) {block_size = 2: i64, data_format = "NHWC"}: (tensor<1x2x2x1xf32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| |
| // CHECK-LABEL: space_to_depth |
| // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32> |
| // CHECK: "tfl.space_to_depth"(%[[ARG]]) {block_size = 2 : i32} : (tensor<1x2x2x1xf32>) -> tensor<?xf32> |
| } |
| |
| func @round(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { |
| %0 = "tf.Round"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| return %0 : tensor<8x16xf32> |
| |
| // CHECK-LABEL: round |
| // CHECK: %[[ARG:.*]]: tensor<8x16xf32> |
| // CHECK: %[[RESULT:.*]] = "tfl.round"(%[[ARG]]) : (tensor<8x16xf32>) -> tensor<8x16xf32> |
| // CHECK: return %[[RESULT]] : tensor<8x16xf32> |
| } |
| |
| func @resize_nearest_neighbor(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> { |
| %0 = "tf.ResizeNearestNeighbor"(%arg0, %arg1) {align_corners = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: resize_nearest_neighbor |
| // CHECK: "tfl.resize_nearest_neighbor"(%arg0, %arg1) {align_corners = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| } |
| |
| // Note: half_pixel_centers isn't supported by TFLite, so it's not legalized. |
| func @resize_nearest_neighbor_with_half_pixel_centers(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> { |
| %0 = "tf.ResizeNearestNeighbor"(%arg0, %arg1) {align_corners = true, half_pixel_centers = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| // CHECK-LABEL: resize_nearest_neighbor_with_half_pixel_centers |
| // CHECK: "tf.ResizeNearestNeighbor"(%arg0, %arg1) {align_corners = true, half_pixel_centers = true} |
| } |
| |
| func @sparse_to_dense_with_scalar_sparse_indices(%arg0: tensor<i32>, %arg1: tensor<3xi32>, %arg2: tensor<f32>, %arg3: tensor<f32>) -> tensor<?x?x?xf32> { |
| %0 = "tf.SparseToDense"(%arg0, %arg1, %arg2, %arg3) {validate_indices = true}: (tensor<i32>, tensor<3xi32>, tensor<f32>, tensor<f32>) -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| // CHECK-LABEL: sparse_to_dense_with_scalar_sparse_indices |
| // CHECK: "tfl.sparse_to_dense"(%arg0, %arg1, %arg2, %arg3) : (tensor<i32>, tensor<3xi32>, tensor<f32>, tensor<f32>) -> tensor<?x?x?xf32> |
| } |
| |
| func @sparse_to_dense_with_vector_sparse_indices(%arg0: tensor<3xi32>, %arg1: tensor<3xi32>, %arg2: tensor<3xf32>, %arg3: tensor<f32>) -> tensor<?x?x?xf32> { |
| %0 = "tf.SparseToDense"(%arg0, %arg1, %arg2, %arg3) {validate_indices = true}: (tensor<3xi32>, tensor<3xi32>, tensor<3xf32>, tensor<f32>) -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| // CHECK-LABEL: sparse_to_dense_with_vector_sparse_indices |
| // CHECK: "tfl.sparse_to_dense"(%arg0, %arg1, %arg2, %arg3) : (tensor<3xi32>, tensor<3xi32>, tensor<3xf32>, tensor<f32>) -> tensor<?x?x?xf32> |
| } |
| |
| func @sparse_to_dense_with_2d_sparse_indices(%arg0: tensor<3x2xi32>, %arg1: tensor<3xi32>, %arg2: tensor<2xf32>, %arg3: tensor<f32>) -> tensor<?x?x?xf32> { |
| %0 = "tf.SparseToDense"(%arg0, %arg1, %arg2, %arg3) {validate_indices = true}: (tensor<3x2xi32>, tensor<3xi32>, tensor<2xf32>, tensor<f32>) -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| // CHECK-LABEL: sparse_to_dense_with_2d_sparse_indices |
| // CHECK: "tfl.sparse_to_dense"(%arg0, %arg1, %arg2, %arg3) : (tensor<3x2xi32>, tensor<3xi32>, tensor<2xf32>, tensor<f32>) -> tensor<?x?x?xf32> |
| } |
| |
| func @where(%arg0: tensor<3x5xi1>) -> tensor<?x2xi64> { |
| %0 = "tf.Where"(%arg0) : (tensor<3x5xi1>) -> tensor<?x2xi64> |
| return %0 : tensor<?x2xi64> |
| // CHECK-LABEL: where |
| // CHECK: "tfl.where"(%arg0) : (tensor<3x5xi1>) -> tensor<?x2xi64> |
| } |
| |
| func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> { |
| %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32> |
| return %0 : tensor<5xf32> |
| // CHECK-LABEL: floor_mod |
| // CHECK: "tfl.floor_mod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32> |
| } |
| |
| func @exp(%arg0: tensor<5xf32>) -> tensor<5xf32> { |
| %0 = "tf.Exp"(%arg0) : (tensor<5xf32>) -> tensor<5xf32> |
| return %0 : tensor<5xf32> |
| // CHECK-LABEL: exp |
| // CHECK: "tfl.exp"(%arg0) : (tensor<5xf32>) -> tensor<5xf32> |
| } |
| |
| func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> { |
| %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64, data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> |
| return %0 : tensor<1x2x2x1xf32> |
| |
| // CHECK-LABEL: depth_to_space |
| // CHECK: %[[ARG:.*]]: tensor<1x1x1x4xf32> |
| // CHECK: "tfl.depth_to_space"(%[[ARG]]) {block_size = 2 : i32} : (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> |
| } |
| |
| func @non_max_suppression_v4(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>) -> tensor<2xi32> { |
| %0:2 = "tf.NonMaxSuppressionV4"(%arg0, %arg1, %arg2, %arg3, %arg4) {pad_to_max_output_size = true}: (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<i32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: non_max_suppression_v4 |
| // CHECK: "tfl.non_max_suppression_v4"(%arg0, %arg1, %arg2, %arg3, %arg4) : (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<i32>) |
| } |
| |
| func @non_max_suppression_v4_no_pad(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>) -> tensor<2xi32> { |
| %0:2 = "tf.NonMaxSuppressionV4"(%arg0, %arg1, %arg2, %arg3, %arg4) {pad_to_max_output_size = false}: (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<i32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: non_max_suppression_v4_no_pad |
| // CHECK: "tfl.non_max_suppression_v4"(%arg0, %arg1, %arg2, %arg3, %arg4) : (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<i32>) |
| } |
| |
| func @non_max_suppression_v5(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>, %arg5: tensor<f32>) -> tensor<2xi32> { |
| %0:3 = "tf.NonMaxSuppressionV5"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) {pad_to_max_output_size = true}: (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<2xf32>, tensor<i32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: non_max_suppression_v5 |
| // CHECK: "tfl.non_max_suppression_v5"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) : (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<2xf32>, tensor<i32>) |
| } |
| |
| func @non_max_suppression_v5_no_pad(%arg0: tensor<3x4xf32>, %arg1: tensor<3xf32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<f32>, %arg5: tensor<f32>) -> tensor<2xi32> { |
| %0:3 = "tf.NonMaxSuppressionV5"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) {pad_to_max_output_size = false}: (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<2xf32>, tensor<i32>) |
| return %0#0 : tensor<2xi32> |
| |
| // CHECK-LABEL: non_max_suppression_v5_no_pad |
| // CHECK: "tfl.non_max_suppression_v5"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) : (tensor<3x4xf32>, tensor<3xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<2xi32>, tensor<2xf32>, tensor<i32>) |
| } |
| |
| func @conv2d_backprop_input(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { |
| %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| %1 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="VALID", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| %2 = "tf.Add"(%0, %1): (tensor<15x28x28x1xf32>, tensor<15x28x28x1xf32>) -> tensor<15x28x28x1xf32> |
| return %2 : tensor<15x28x28x1xf32> |
| |
| // CHECK-LABEL: conv2d_backprop_input |
| // CHECK: %[[CST:.*]] = constant dense<[2, 0, 1, 3]> : tensor<4xi32> |
| // CHECK: %[[ARG0:.*]] = "tfl.transpose"(%arg1, %[[CST]]) : (tensor<3x3x1x32xf32>, tensor<4xi32>) -> tensor<1x3x3x32xf32> |
| // CHECK: %[[ARG1:.*]] = "tfl.transpose_conv"(%arg0, %[[ARG0]], %arg2) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<4xi32>, tensor<1x3x3x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| // CHECK: %[[CST_1:.*]] = constant dense<[2, 0, 1, 3]> : tensor<4xi32> |
| // CHECK: %[[ARG2:.*]] = "tfl.transpose"(%arg1, %[[CST_1]]) : (tensor<3x3x1x32xf32>, tensor<4xi32>) -> tensor<1x3x3x32xf32> |
| // CHECK: %[[ARG3:.*]] = "tfl.transpose_conv"(%arg0, %[[ARG2]], %arg2) {padding = "VALID", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<4xi32>, tensor<1x3x3x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| // CHECK: %[[RESULT:.*]] = tfl.add %[[ARG1]], %[[ARG3]] {fused_activation_function = "NONE"} : tensor<15x28x28x1xf32> |
| // CHECK: return %[[RESULT]] : tensor<15x28x28x1xf32> |
| } |
| |
| func @conv2d_backprop_input_unsupported_paddings(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { |
| %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], explicit_paddings = [1, 1, 1, 1, 1, 1, 1, 1], padding="EXPLICIT", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| return %0 : tensor<15x28x28x1xf32> |
| |
| // CHECK-LABEL: conv2d_backprop_input_unsupported_paddings |
| // CHECK: tf.Conv2DBackpropInput |
| } |
| |
| func @conv2d_backprop_unsupported_strides(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { |
| %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {dilations = [1, 1, 1, 1], padding="SAME", strides = [2, 2, 2, 2]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| return %0 : tensor<15x28x28x1xf32> |
| |
| // CHECK-LABEL: conv2d_backprop_unsupported_strides |
| // CHECK: tf.Conv2DBackpropInput |
| } |
| |
| func @conv2d_backprop_unsupported_data_format(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> { |
| %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {data_format = "NCHW", dilations = [1, 1, 1, 1], padding="SAME", strides = [1, 2, 2, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> |
| return %0 : tensor<15x28x28x1xf32> |
| |
| // CHECK-LABEL: conv2d_backprop_unsupported_data_format |
| // CHECK: tf.Conv2DBackpropInput |
| } |
| |
| func @assert_remove(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi1> { |
| %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1> |
| "tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> () |
| return %0 : tensor<1xi1> |
| // CHECK-LABEL: assert_remove |
| // CHECK: tfl.less_equal |
| // CHECK-NOT: Assert |
| // CHECK: return |
| } |
| |
| func @reciprocal_f32(%arg0: tensor<8xf32>) -> tensor<8xf32> { |
| %0 = "tf.Reciprocal"(%arg0) : (tensor<8xf32>) -> tensor<8xf32> |
| return %0: tensor<8xf32> |
| |
| // CHECK-LABEL: reciprocal_f32 |
| // CHECK: %cst = constant dense<1.000000e+00> : tensor<f32> |
| // CHECK: "tfl.div"(%cst, %arg0) {fused_activation_function = "NONE"} : (tensor<f32>, tensor<8xf32>) -> tensor<8xf32> |
| // CHECK: return |
| } |
| |
| func @reciprocal_i32(%arg0: tensor<8xi32>) -> tensor<8xi32> { |
| %0 = "tf.Reciprocal"(%arg0) : (tensor<8xi32>) -> tensor<8xi32> |
| return %0: tensor<8xi32> |
| |
| // CHECK-LABEL: reciprocal_i32 |
| // CHECK: %cst = constant dense<1> : tensor<i32> |
| // CHECK: "tfl.div"(%cst, %arg0) {fused_activation_function = "NONE"} : (tensor<i32>, tensor<8xi32>) -> tensor<8xi32> |
| // CHECK: return |
| } |
| |
| func @random_uniform() -> tensor<2x5xf32> { |
| %0 = "tf.Const"() { value = dense<[2, 5]> : tensor<2xi32> } : () -> tensor<2xi32> |
| %1 = "tf.RandomUniform"(%0) { seed = 1, seed2 = 0} : (tensor<2xi32>) -> tensor<2x5xf32> |
| return %1 : tensor<2x5xf32> |
| |
| // CHECK-LABEL: random_uniform |
| // CHECK: %[[CST:.*]] = constant dense |
| // CHECK: return %[[CST:.*]] : tensor<2x5xf32> |
| } |
| |
| func @random_uniform_no_fold(%arg0: tensor<2xi32>) -> tensor<2x5xf32> { |
| %1 = "tf.RandomUniform"(%arg0) { seed = 0, seed2 = 0} : (tensor<2xi32>) -> tensor<2x5xf32> |
| return %1 : tensor<2x5xf32> |
| |
| // CHECK-LABEL: random_uniform_no_fold |
| // CHECK: %[[RANDOM:.*]] = "tf.RandomUniform" |
| } |
| |
| func @random_uniform_no_fold2(%arg0: tensor<2xi32>) -> tensor<*xf32> { |
| %1 = "tf.RandomUniform"(%arg0) { seed = 1, seed2 = 2} : (tensor<2xi32>) -> tensor<*xf32> |
| return %1 : tensor<*xf32> |
| |
| // CHECK-LABEL: random_uniform_no_fold2 |
| // CHECK: %[[RANDOM:.*]] = "tf.RandomUniform" |
| } |
| |
| func @random_uniform_no_fold3() -> tensor<2x5xf64> { |
| %0 = "tf.Const"() { value = dense<[2, 5]> : tensor<2xi32> } : () -> tensor<2xi32> |
| %1 = "tf.RandomUniform"(%0) { seed = 1, seed2 = 0} : (tensor<2xi32>) -> tensor<2x5xf64> |
| return %1 : tensor<2x5xf64> |
| |
| // CHECK-LABEL: random_uniform_no_fold3 |
| // CHECK: %[[RANDOM:.*]] = "tf.RandomUniform" |
| } |
| |
| func @LstmWithoutProjection(%arg: tensor<28x1x28xf32>) -> (tensor<28x1x16xf32>) { |
| %1 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x28xf32>} : () -> tensor<16x28xf32> |
| %2 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x16xf32>} : () -> tensor<16x16xf32> |
| %3 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16xf32>} : () -> tensor<16xf32> |
| %4 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<1x16xf32>} : () -> tensor<1x16xf32> |
| %5 = "tf.Const"() {device = "", dtype = f32, value = dense<-1.000000e+00> : tensor<1xf32>} : () -> tensor<1xf32> |
| %6:3 = "tf.UnidirectionalSequenceLstm"(%arg, %1, %1, %1, %1, %2, %2, %2, %2, %3, %3, %3, %3, %3, %3, %3, %5, %5, %4, %4) {_tflite_input_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19], device = ""} : (tensor<28x1x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1x16xf32>, tensor<1x16xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<28x1x16xf32>) |
| return %6#2 : tensor<28x1x16xf32> |
| } |
| |
| // CHECK: func @LstmWithoutProjection([[VAL_0:%.*]]: tensor<28x1x28xf32>) -> tensor<28x1x16xf32> { |
| // CHECK: [[VAL_1:%.*]] = constant dense<0.000000e+00> : tensor<16x28xf32> |
| // CHECK: [[VAL_2:%.*]] = constant dense<0.000000e+00> : tensor<16x16xf32> |
| // CHECK: [[VAL_3:%.*]] = constant dense<0.000000e+00> : tensor<16xf32> |
| // CHECK: [[VAL_4:%.*]] = constant dense<0.000000e+00> : tensor<1x16xf32> |
| // CHECK: [[VAL_5:%.*]] = constant unit |
| // CHECK: [[VAL_6:%.*]] = "tfl.unidirectional_sequence_lstm"([[VAL_0]], [[VAL_1]], [[VAL_1]], [[VAL_1]], [[VAL_1]], [[VAL_2]], [[VAL_2]], [[VAL_2]], [[VAL_2]], [[VAL_3]], [[VAL_3]], [[VAL_3]], [[VAL_3]], [[VAL_3]], [[VAL_3]], [[VAL_3]], [[VAL_5]], [[VAL_5]], [[VAL_4]], [[VAL_4]], [[VAL_5]], [[VAL_5]], [[VAL_5]], [[VAL_5]]) {cell_clip = 0.000000e+00 : f32, fused_activation_function = "TANH", proj_clip = 0.000000e+00 : f32, time_major = true} : (tensor<28x1x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, none, none, tensor<1x16xf32>, tensor<1x16xf32>, none, none, none, none) -> tensor<28x1x16xf32> |
| // CHECK: return [[VAL_6]] : tensor<28x1x16xf32> |
| // CHECK: } |
| |
| func @LstmWithProjection(%arg: tensor<28x1x16xf32>) -> (tensor<28x1x8xf32>) { |
| %1 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x16xf32>} : () -> tensor<16x16xf32> |
| %2 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x8xf32>} : () -> tensor<16x8xf32> |
| %3 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16xf32>} : () -> tensor<16xf32> |
| %4 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<1x16xf32>} : () -> tensor<1x16xf32> |
| %5 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<8x16xf32>} : () -> tensor<8x16xf32> |
| %6 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<1x8xf32>} : () -> tensor<1x8xf32> |
| %7 = "tf.Const"() {device = "", dtype = f32, value = dense<-1.000000e+00> : tensor<1xf32>} : () -> tensor<1xf32> |
| %8:3 = "tf.UnidirectionalSequenceLstm"(%arg, %1, %1, %1, %1, %2, %2, %2, %2, %7, %7, %7, %3, %3, %3, %3, %5, %7, %6, %4) {_tflite_input_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 18, 19], device = ""} : (tensor<28x1x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x8xf32>, tensor<16x8xf32>, tensor<16x8xf32>, tensor<16x8xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<8x16xf32>, tensor<1xf32>, tensor<1x8xf32>, tensor<1x16xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<28x1x8xf32>) |
| return %8#2 : tensor<28x1x8xf32> |
| } |
| |
| // CHECK-LABEL: func @LstmWithProjection( |
| // CHECK-SAME: [[VAL_7:%.*]]: tensor<28x1x16xf32>) -> tensor<28x1x8xf32> { |
| // CHECK: [[VAL_8:%.*]] = constant dense<0.000000e+00> : tensor<16x16xf32> |
| // CHECK: [[VAL_9:%.*]] = constant dense<0.000000e+00> : tensor<16x8xf32> |
| // CHECK: [[VAL_10:%.*]] = constant dense<0.000000e+00> : tensor<16xf32> |
| // CHECK: [[VAL_11:%.*]] = constant dense<0.000000e+00> : tensor<1x16xf32> |
| // CHECK: [[VAL_12:%.*]] = constant dense<0.000000e+00> : tensor<8x16xf32> |
| // CHECK: [[VAL_13:%.*]] = constant dense<0.000000e+00> : tensor<1x8xf32> |
| // CHECK: [[VAL_14:%.*]] = constant unit |
| // CHECK: [[VAL_15:%.*]] = "tfl.unidirectional_sequence_lstm"([[VAL_7]], [[VAL_8]], [[VAL_8]], [[VAL_8]], [[VAL_8]], [[VAL_9]], [[VAL_9]], [[VAL_9]], [[VAL_9]], [[VAL_14]], [[VAL_14]], [[VAL_14]], [[VAL_10]], [[VAL_10]], [[VAL_10]], [[VAL_10]], [[VAL_12]], [[VAL_14]], [[VAL_13]], [[VAL_11]], [[VAL_14]], [[VAL_14]], [[VAL_14]], [[VAL_14]]) {cell_clip = 0.000000e+00 : f32, fused_activation_function = "TANH", proj_clip = 0.000000e+00 : f32, time_major = true} : (tensor<28x1x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x8xf32>, tensor<16x8xf32>, tensor<16x8xf32>, tensor<16x8xf32>, none, none, none, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<8x16xf32>, none, tensor<1x8xf32>, tensor<1x16xf32>, none, none, none, none) -> tensor<28x1x8xf32> |
| // CHECK: return [[VAL_15]] : tensor<28x1x8xf32> |
| // CHECK: } |
| |
| func @UnidirectionalRnn(%arg: tensor<28x1x28xf32>) -> (tensor<28x1x28xf32>) { |
| %1 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<28x28xf32>} : () -> tensor<28x28xf32> |
| %2 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<28xf32>} : () -> tensor<28xf32> |
| %3 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<1x28xf32>} : () -> tensor<1x28xf32> |
| %4:2 = "tf.UnidirectionalSequenceRnn"(%arg, %1, %1, %2, %3) {_tflite_input_indices = [0, 1, 2, 3, 4], device = ""} : (tensor<28x1x28xf32>, tensor<28x28xf32>, tensor<28x28xf32>, tensor<28xf32>, tensor<1x28xf32>) -> (tensor<*xf32>, tensor<28x1x28xf32>) |
| return %4#1 : tensor<28x1x28xf32> |
| } |
| |
| // CHECK: func @UnidirectionalRnn([[VAL_0:%.*]]: tensor<28x1x28xf32>) -> tensor<28x1x28xf32> { |
| // CHECK: [[VAL_1:%.*]] = constant dense<0.000000e+00> : tensor<28x28xf32> |
| // CHECK: [[VAL_2:%.*]] = constant dense<0.000000e+00> : tensor<28xf32> |
| // CHECK: [[VAL_3:%.*]] = constant dense<0.000000e+00> : tensor<1x28xf32> |
| // CHECK: [[VAL_4:%.*]] = "tfl.unidirectional_sequence_rnn"([[VAL_0]], [[VAL_1]], [[VAL_1]], [[VAL_2]], [[VAL_3]]) {fused_activation_function = "TANH", time_major = true} : (tensor<28x1x28xf32>, tensor<28x28xf32>, tensor<28x28xf32>, tensor<28xf32>, tensor<1x28xf32>) -> tensor<28x1x28xf32> |
| // CHECK: return [[VAL_4]] : tensor<28x1x28xf32> |
| // CHECK: } |
| |
| func @broadcast_to_f32(%arg0: tensor<3xf32>, %arg1: tensor<2xi32>) -> tensor<3x3xf32> { |
| %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xf32>, tensor<2xi32>) -> tensor<3x3xf32> |
| return %0: tensor<3x3xf32> |
| |
| // CHECK-LABEL: broadcast_to_f32 |
| // CHECK: [[CST:%.*]] = constant dense<1.000000e+00> : tensor<f32> |
| // CHECK: [[FILL:%.*]] = "tfl.fill"(%arg1, [[CST]]) : (tensor<2xi32>, tensor<f32>) -> tensor<3x3xf32> |
| // CHECK: [[MUL:%.*]] = "tfl.mul"(%arg0, [[FILL]]) {fused_activation_function = "NONE"} : (tensor<3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32> |
| // CHECK return [[MUL]] : tensor<3x3xf32> |
| } |
| |
| func @broadcast_to_i32(%input: tensor<3xi32>, %shape: tensor<2xi32>) -> tensor<3x3xi32> { |
| %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi32>, tensor<2xi32>) -> tensor<3x3xi32> |
| return %0: tensor<3x3xi32> |
| |
| // CHECK-LABEL: broadcast_to_i32 |
| // CHECK: [[CST:%.*]] = constant dense<1> : tensor<i32> |
| // CHECK: [[FILL:%.*]] = "tfl.fill"(%arg1, [[CST]]) : (tensor<2xi32>, tensor<i32>) -> tensor<3x3xi32> |
| // CHECK: [[MUL:%.*]] = "tfl.mul"(%arg0, [[FILL]]) {fused_activation_function = "NONE"} : (tensor<3xi32>, tensor<3x3xi32>) -> tensor<3x3xi32> |
| // CHECK return [[MUL]] : tensor<3x3xi32> |
| } |
