_UpdatePendingAndEnqueueReady

Author: Oceania2018

src/TensorFlowNET.Core/Gradients/gradients_util.cs

@@ -17,6 +17,7 @@ limitations under the License.
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using Tensorflow.Operations.ControlFlows;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -82,6 +83,7 @@ public static Tensor[] _GradientsHelper(Tensor[] ys,
                     var stop_gradient_ops = stop_gradients.Select(x => x.op).ToList();
                     var (reachable_to_ops, pending_count, loop_state) = _PendingCount(to_ops, from_ops, colocate_gradients_with_ops, new List<object>(), xs);
 
+                    // Add the initial gradients for the ys.
                     foreach (var (y, grad_y) in zip(ys, grad_ys))
                         _SetGrad(grads, y, grad_y);
 
@@ -103,12 +105,25 @@ public static Tensor[] _GradientsHelper(Tensor[] ys,
                         }
                     }
 
+                    if(loop_state != null)
+                    {
+                        var loop_exits = loop_state.ProcessUnusedLoopExits(pending_count, to_ops_set);
+                        foreach(var y in loop_exits)
+                        {
+                            //if(IsTrainable(y))
+                            throw new NotImplementedException("");
+                        }
+                    }
+
                     var stop_ops = _StopOps(from_ops, stop_gradient_ops, pending_count, xs);
                     while (queue.Count > 0)
                     {
                         // generate gradient subgraph for op.
                         var op = queue.Dequeue();
+                        if(op.name == "rnn/while/basic_rnn_cell/Tanh")
+                        {
 
+                        }
                         _maybe_colocate_with(op, gradient_uid, colocate_gradients_with_ops);
                         //if (loop_state != null)
                         //loop_state.EnterGradWhileContext(op, before: true);
@@ -147,8 +162,8 @@ public static Tensor[] _GradientsHelper(Tensor[] ys,
                                 }
                             }
 
-                            // if (loop_state)
-                                //loop_state.EnterGradWhileContext(op, before: false);
+                            if (loop_state != null)
+                                loop_state.EnterGradWhileContext(op, before: false);
 
                             if ((is_func_call || grad_fn != null) && has_out_grads)
                             {
@@ -164,7 +179,7 @@ public static Tensor[] _GradientsHelper(Tensor[] ys,
                                         // will use SymbolicGradient get a zero gradient. Gradient
                                         // functions should ignore the gradient for other outputs.
                                         if (loop_state != null)
-                                            ;
+                                            out_grads[i] = new List<Tensor> { loop_state.ZerosLike(op, i) };
                                         else
                                             out_grads[i] = new List<Tensor> { control_flow_ops.ZerosLikeOutsideLoop(op, i) };
                                     }
@@ -275,7 +290,7 @@ private static void _maybe_colocate_with(Operation op, string gradient_uid, bool
         /// <param name="colocate_gradients_with_ops"></param>
         /// <param name="func_graphs"></param>
         /// <param name="xs"></param>
-        private static (Operation[], Dictionary<string, int>, object) _PendingCount(List<Operation> to_ops, List<Operation> from_ops, bool colocate_gradients_with_ops, List<object> func_graphs, Tensor[] xs)
+        private static (Operation[], Dictionary<string, int>, ControlFlowState) _PendingCount(List<Operation> to_ops, List<Operation> from_ops, bool colocate_gradients_with_ops, List<object> func_graphs, Tensor[] xs)
         {
             // Mark reachable ops from from_ops.
             var reached_ops = new List<Operation>();
@@ -308,6 +323,7 @@ private static (Operation[], Dictionary<string, int>, object) _PendingCount(List
             // 'loop_state' is None if there are no while loops.
             var loop_state = control_flow_ops.MaybeCreateControlFlowState(between_op_list, between_ops, colocate_gradients_with_ops);
 
+            // Initialize pending count for between ops.
             var pending_count = new Dictionary<string, int>();
             foreach (var op in between_op_list)
             {
@@ -550,7 +566,7 @@ private static void _UpdatePendingAndEnqueueReady(Dictionary<string, List<List<T
             Operation op,
             Queue<Operation> queue,
             Dictionary<string, int> pending_count,
-            object loop_state,
+            ControlFlowState loop_state,
             Tensor[] xs)
         {
             foreach (var x in _NonEagerInputs(op, xs))
@@ -564,14 +580,49 @@ private static void _UpdatePendingAndEnqueueReady(Dictionary<string, List<List<T
 
                 if (loop_state != null && !ready)
                 {
-
+                    ready = pending_count[x.op.name] > 0 && control_flow_util.IsLoopSwitch(x.op);
                 }
 
                 if (ready)
                 {
+                    // if x is an exit without real gradient, defer processing them.
                     if (control_flow_util.IsLoopExit(x.op))
                     {
-
+                        var grad_state = loop_state.GetGradState(x.op, before: false);
+                        grad_state.deferred_exits.append(x);
+                        grad_state.pending_exits_count -= 1;
+                        // We now have all the exits so process them.
+                        if (grad_state.pending_exits_count == 0)
+                        {
+                            var has_not_none_grad = false;
+                            foreach(var y in grad_state.deferred_exits)
+                            {
+                                if (_HasAnyNotNoneGrads(grads, y.op))
+                                {
+                                    has_not_none_grad = true;
+                                    queue.Enqueue(y.op);
+                                }
+                                else
+                                    grad_state.unused_exits.append(y);
+                            }
+                            if (has_not_none_grad)
+                            {
+                                // For an unused exit, if it has trainable outputs, backprop
+                                // a zero gradient. Otherwise, just ignore it.
+                                foreach (var y in grad_state.unused_exits)
+                                {
+                                    if (IsTrainable(y))
+                                        _SetGrad(grads, y, loop_state.ZerosLikeForExit(y));
+                                    queue.Enqueue(y.op);
+                                }
+                            }
+                            else
+                            {
+                                // All exits are "unused" so use None as gradient.
+                                foreach (var y in grad_state.unused_exits)
+                                    queue.Enqueue(y.op);
+                            }
+                        }
                     }
                     else
                     {
@@ -581,6 +632,32 @@ private static void _UpdatePendingAndEnqueueReady(Dictionary<string, List<List<T
             }
         }
 
+        private static bool IsTrainable(Tensor tensor)
+        {
+            var dtype = tensor.dtype.as_base_dtype();
+            return new TF_DataType[] { dtypes.float16, dtypes.float32, dtypes.float64,
+                              dtypes.complex64, dtypes.complex128,
+                              dtypes.resource, dtypes.variant}.Contains(dtype);
+        }
+
+        /// <summary>
+        /// Return true if op has real gradient.
+        /// </summary>
+        /// <param name="grads"></param>
+        /// <param name="op"></param>
+        /// <returns></returns>
+        private static bool _HasAnyNotNoneGrads(Dictionary<string, List<List<Tensor>>> grads, Operation op)
+        {
+            var out_grads = _GetGrads(grads, op);
+            foreach(var out_grad in out_grads)
+            {
+                if (out_grad.Exists(g => g != null))
+                    return true;
+            }
+            return false;
+        }
+
+
         private static Tensor[] _MaybeCompile(string scope, Operation op, Tensor[] out_grads, Action func, Func<Operation, Tensor[], Tensor[]> grad_fn)
         {
             scope = scope.EndsWith("/") ? scope.Substring(0, scope.Length - 1) : scope;
@@ -589,6 +666,9 @@ private static Tensor[] _MaybeCompile(string scope, Operation op, Tensor[] out_g
 
         private static void _VerifyGeneratedGradients(Tensor[] grads, Operation op)
         {
+            if (op.type == "While" || op.type == "StatelessWhile")
+                return;
+
             if (grads.Count() != op.inputs._inputs.Count())
                 throw new ValueError($"Num gradients {grads.Length} generated for op {op.node_def} do not match num " +
                     $"inputs {op.inputs._inputs.Count()}");