overlapping-outputs-input-comparison.png...

# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=output %s -o - --output-mlir -tf-custom-opdefs="name: 'BannaPotatoSaladWithColeslaw' input_arg: { name: 'a' type: DT_INT32 } input_arg: { name: 'b' type: DT_INT32 } output_arg: { name: 'c' type: DT_INT32 }" | FileCheck %s

node {
  name: "output"
  op: "BannaPotatoSaladWithColeslaw"
  input: "input0"
  input: "input1"
  attr {
    key: "T"

# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=Add %s -o - | flatbuffer_to_string - | FileCheck %s

# Add two tensor<4xi32> inputs and return the result

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "input0"
  op: "Placeholder"
  attr {

}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:      node {
// CHECK-NEXT:   name: "input1"
// CHECK-NEXT:   op: "_Arg"
// CHECK:      node {
// CHECK-NEXT:   name: "Add{{_.*_1}}"
// CHECK-NEXT:   op: "Add"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   input: "input1"
// CHECK:      node {
// CHECK-NEXT:   name: "Add"
// CHECK-NEXT:   op: "_Retval"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-data-types=DT_INT32 -tf-input-shapes='' -tf-output-arrays=while:2 -o - | FileCheck %s

# This check that we don't error out when importing GraphDef containing
# functions with arg name that are the same as the graph input name

# CHECK: func @main(%arg0: tensor<{{.*}}i32>) -> tensor<{{.*}}i32>
# CHECK: func private @while_body

     (AllElementsAreBool<"false"> $constant)]>;
  // select(logical_not(C), A, B) -> select(C, B, A)
  def Optimize#SelectOp#Not : Pat<
    (SelectOp (TFL_LogicalNotOp $condition), $input1, $input2),
    (SelectOp $condition, $input2, $input1)>;

  // Fuse select(broadcast_to(input, shape), x, y) -> selectV2(input, x, y)
  // Also, fuse selectv2(broadcast_to(input, shape), x, y) -> selectV2(input, x, y)

  // CHECK: %[[INPUT0:.*]] = "tf.Placeholder.input"
  %0 = "tf.Placeholder.input"(%arg0) : (tensor<f32>) -> tensor<f32>
  // CHECK: %[[INPUT1:.*]] = "tf.Placeholder.input"
  %1 = "tf.Placeholder.input"(%arg1) : (tensor<f32>) -> tensor<f32>
  %2 = arith.constant dense<true> : tensor<i1>

  // CHECK: "tf.Multiply"(%[[INPUT1]], %[[INPUT0]])

    def setup() {
        INVOCATION_COUNT.set(0)
    }

    def "input artifact selection is restored when using the in-memory cache"() {
        def transform = registerTransform(IdentityTransform)

        def inputArtifact1 = file("input1/input.txt")
        inputArtifact1.text = "Hello"

        def inputArtifact2 = file("input2/input.txt")
        inputArtifact2.text = "Hello"

        expect:

node {
  name: "replicated_input_0"
  op: "Identity"
  input: "input0"
  input: "^TPUReplicateMetadata"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "_tpu_replicate"
    value {
      s: "cluster"
    }
  }
}
node {
  name: "replicated_input_1"
  op: "Identity"
  input: "input1"
  input: "^TPUReplicateMetadata"
  attr {
    key: "T"

// Tests input and output names from FlatBuffer are added to `tf.entry_function` attribute.

// CHECK-LABEL: @main
func.func @main(%arg0: tensor<4xi8>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi8>)
// CHECK: attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}}
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} {