%0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.interface_name = "func_1"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %2 = func.call @func_2_CPU_FLOAT(%0, %1) {tac.interface_name = "func_2"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
    func.return %2 : tensor<2x1xf32>
  }

			return func(x int) int { // ERROR "can inline main.func12"
				return x + 1
			}, 42
		}() // ERROR "func literal does not escape" "inlining call to main.func12"
		if y(40) != 41 {
			ppanic("y(40) != 41")
		}
	}

	{
		func() { // ERROR "func literal does not escape"
			y := func(x int) int { // ERROR "func literal does not escape" "can inline main.func13.1"
				return x + 2
			}

    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<100xf32>, tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>
    %1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

  if (GetName(funcs[0]) == GetName(func0)) {
    AssertEqual(func0, funcs[0]);
    AssertEqual(func1, funcs[1]);
  } else {
    AssertEqual(func0, funcs[1]);
    AssertEqual(func1, funcs[0]);
  }

  TF_DeleteFunction(funcs[0]);
  TF_DeleteFunction(funcs[1]);

  TF_DeleteFunction(func0);
  TF_DeleteFunction(func1);
}

}  // namespace

// CHECK:       return %0 : tensor<i32>
// CHECK:     }
// CHECK:     func.func private @func_0_CPU_FLOAT(%arg0: tensor<i32>) -> tensor<i32> attributes {tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} {
// CHECK:       %0 = "tfl.while"(%arg0) ({
// CHECK:       ^bb0(%arg1: tensor<i32>):

func.func @func_0_CPU(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  // CHECK: tac.cost = 7.864320e+05
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<256x32x32x3xf32>, tensor<256x32x32x3xf32>) -> tensor<256x32x32x3xf32>

	}
	go func() { // func10
		c, err := ln.Accept()
		if err != nil {
			log.Printf("failed to accept: %v", err)
			return
		}
		c.Close()
	}()
	rp, wp, err := os.Pipe()
	if err != nil {
		log.Fatalf("failed to create a pipe: %v", err)
	}
	defer rp.Close()
	defer wp.Close()
	pipeReadDone := make(chan bool)
	go func() { // func11
		var data [1]byte
		rp.Read(data[:])

// See the example below:
//
// In main:
// %0 = tfl.const...
// %1 = tfl.const...
// %2 = call func_1(..., %0,...)
// %3 = call func_2(..., %0, ..., %1...)
// ...
//
// Then those consts will be copied into each function and replace their usage.
// func_1:
//   %0 = tfl.const...
// func_2:
//   %0 = tfl.const...
//   %1 = tfl.const...
class FoldConstantsToSubgraphPass

    %1 = call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %2 = call @func_2_CPU_FLOAT(%0, %1) {tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>

Robert Griesemer <******@****.***> 1607048150 -0800