Avoid memory 'leak' in Go with multple inputs

Fixes #40758. Go map iteration order is random. If we send randomly ordered lists of inputs to the C core it will create a key in the session executors_ cache for each of the random orderings. With more than a handful of inputs this can get big very quickly.

tensorflow/go/session.go

@@ -24,6 +24,7 @@ import (
 	"errors"
 	"fmt"
 	"runtime"
+	"sort"
 	"sync"
 	"unsafe"
 )
@@ -359,16 +360,56 @@ type cRunArgs struct {
 	targets      []*C.TF_Operation
 }
 
+type feedsort struct {
+	feeds       []C.TF_Output
+	feedTensors []*C.TF_Tensor
+}
+
+func (f *feedsort) Less(i, j int) bool {
+	// Ideally we would sort on the output names. But that's not easy for us to
+	// do efficiently as loads of Go name strings would be created from the C
+	// strings and destroyed. But we can sort on the addresses of the operation
+	// names. This won't sort alphabetically, but for a given set of feeds it
+	// should give consistent results from one run to the next.
+	ni := uintptr(unsafe.Pointer(C.TF_OperationName(f.feeds[i].oper)))
+	nj := uintptr(unsafe.Pointer(C.TF_OperationName(f.feeds[j].oper)))
+	if ni == nj {
+		// if the names are the same the index may differ
+		return f.feeds[i].index < f.feeds[j].index
+	}
+	return ni < nj
+}
+
+func (f *feedsort) Swap(i, j int) {
+	f.feeds[i], f.feeds[j] = f.feeds[j], f.feeds[i]
+	f.feedTensors[i], f.feedTensors[j] = f.feedTensors[j], f.feedTensors[i]
+}
+
+func (f *feedsort) Len() int {
+	return len(f.feeds)
+}
+
 func newCRunArgs(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) *cRunArgs {
 	c := &cRunArgs{
 		fetches:      make([]C.TF_Output, len(fetches)),
 		fetchTensors: make([]*C.TF_Tensor, len(fetches)),
 		targets:      make([]*C.TF_Operation, len(targets)),
 	}
+	// Go map iteration order is random. So our list of input names will be
+	// random for each Run. This interacts badly with the TF core code which
+	// builds a executor cache key from these names in the order we provide
+	// them. We'll eventually enumerate every possible order and store it in the
+	// executor cache. With n inputs that's n! entries. That gets very big very
+	// quickly.
 	for o, t := range feeds {
 		c.feeds = append(c.feeds, o.c())
 		c.feedTensors = append(c.feedTensors, t.c)
 	}
+	if len(c.feeds) > 1 {
+		fs := feedsort{feeds: c.feeds, feedTensors: c.feedTensors}
+		sort.Sort(&fs)
+	}
+
 	for i, o := range fetches {
 		c.fetches[i] = o.c()
 	}

tensorflow/go/session_test.go

@@ -74,6 +74,120 @@ func TestSessionRunNeg(t *testing.T) {
 	}
 }
 
+func TestMultipleInput(t *testing.T) {
+	// The inputs to the graph get sorted. This test checks that process works
+	// OK and that we still get the right output.
+	graph := NewGraph()
+
+	inputs := make([]Output, 20)
+	layer2 := make([]Output, len(inputs))
+	for i := range inputs {
+		in, err := Placeholder(graph, fmt.Sprintf("input%d", i), Int64)
+		if err != nil {
+			t.Fatal(err)
+		}
+		inputs[i] = in
+
+		factor, err := Const(graph, fmt.Sprintf("factor%d", i), int64(i+1))
+		if err != nil {
+			t.Fatal(err)
+		}
+		l2, err := graph.AddOperation(OpSpec{
+			Type: "Mul",
+			Name: fmt.Sprintf("Mul%d", i),
+			Input: []Input{
+				in,
+				factor,
+			},
+		})
+		if err != nil {
+			t.Fatal(err)
+		}
+		layer2[i] = l2.Output(0)
+	}
+
+	fetch, err := graph.AddOperation(OpSpec{
+		Type: "AddN",
+		Input: []Input{
+			OutputList(layer2),
+		},
+	})
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	session, err := NewSession(graph, nil)
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer func() {
+		if err := session.Close(); err != nil {
+			t.Fatal(err)
+		}
+	}()
+
+	feeds := make(map[Output]*Tensor, len(inputs))
+	for i, in := range inputs {
+		tensor, err := NewTensor(int64(i + 1))
+		if err != nil {
+			t.Fatal(err)
+		}
+		feeds[in] = tensor
+	}
+
+	output, err := session.Run(
+		feeds,
+		[]Output{
+			fetch.Output(0),
+		},
+		nil,
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	var exp int64
+	for i := range inputs {
+		exp += int64((i + 1) * (i + 1))
+	}
+	if v := output[0].Value().(int64); v != exp {
+		t.Fatalf("expected %d got %d", exp, v)
+	}
+}
+
+func TestInputOrderStable(t *testing.T) {
+	graph := NewGraph()
+
+	inputs := make([]Output, 20)
+	for i := range inputs {
+		in, err := Placeholder(graph, fmt.Sprintf("input%d", i), Int64)
+		if err != nil {
+			t.Fatal(err)
+		}
+		in.Index = i
+		inputs[i] = in
+	}
+
+	makeArgs := func() *cRunArgs {
+		feeds := make(map[Output]*Tensor, len(inputs))
+		for i, in := range inputs {
+			tensor, err := NewTensor(int64(i + 1))
+			if err != nil {
+				t.Fatal(err)
+			}
+			feeds[in] = tensor
+		}
+
+		return newCRunArgs(feeds, nil, nil)
+	}
+	args1 := makeArgs()
+	args2 := makeArgs()
+
+	if !reflect.DeepEqual(args1.feeds, args2.feeds) {
+		t.Fatalf("order is not stable")
+	}
+}
+
 func TestSessionRunConcat(t *testing.T) {
 	// Runs the Concat operation on two matrices: m1 and m2, along the
 	// first dimension (dim1).