UnorderedMapEnumerable <long, List <Tensor> > InitialGradients(long[] target_tensor_ids,
UnorderedMap <long, TapeTensor> sources_that_are_targets,
Tensor[] output_gradients,
TensorTape tensor_tape,
OpTape <BackwardFunction, TapeTensor> op_tape)
{
var result = new UnorderedMapEnumerable <long, List <Tensor> >();
for (int i = 0; i < target_tensor_ids.Length; ++i)
{
var id = target_tensor_ids[i];
if (output_gradients.Length == 0 || output_gradients[i] == null)
{
if (tensor_tape.find(id, out var tensor_id) && tensor_id != -1)
{
if (!op_tape.find(tensor_tape[id], out var op_it))
{
throw new RuntimeError("Internal state of the gradient tape is invalid: " +
"failed to find operation producing a tensor");
}
bool found = false;
for (int j = 0; j < op_it.output_tensor_info.Length; ++j)
{
if (op_it.output_tensor_info[j].GetID() == id)
{
found = true;
var ones = op_it.output_tensor_info[j].OnesLike();
result[id].Add(ones);
break;
}
}
if (!found)
{
throw new ValueError("Internal state of the gradient tape is invalid: " +
"none of operations outputs match expected tensor");
}
}
else
{
if (sources_that_are_targets.find(id, out var source_tensor))
{
result[id].Add(source_tensor.OnesLike());
}
}
}
else
{
result[id].Add(output_gradients[i]);
}
}
return(result);
}
public Tape(bool persistent, bool watch_accessed_variables)
{
this.persistent_ = persistent;
this.watch_accessed_variables = watch_accessed_variables;
tensor_tape_ = new TensorTape();
op_tape_ = new OpTape <BackwardFunction, TapeTensor>();
tensor_usage_ = new UnorderedMap <long, long>();
nesting_id = ++tape_nesting_id_counter;
tf.GetTapeSet().Add(this);
}
/// <summary>
/// A deque-backed stack, whose element references are not invalidated by
/// pushes and pops at the back.
/// </summary>
// Stack<AccumulatorCallState> call_state_;
public Tape(bool persistent, bool watch_accessed_variables)
{
_persistent = persistent;
_created_eagerly = tf.Context.executing_eagerly();
tensor_tape_ = new TensorTape();
op_tape_ = new OpTape();
tensor_usage_ = new UnorderedMap <Tensor, long>();
if (_created_eagerly)
{
tf.Context.start_step();
}
// nesting_id = ++tape_nesting_id_counter;
}
Queue <long> InitialStack(OpTape <BackwardFunction, TapeTensor> op_tape,
UnorderedMap <long, long> op_missing_tensor)
{
var result = new Queue <long>();
foreach (var op_entry in op_tape)
{
if (!op_missing_tensor.find(op_entry.Key))
{
result.Enqueue(op_entry.Key);
}
}
return(result);
}
public BackpropInitialState()
{
op_tape = new OpTape();
tensor_usage_counts = new UnorderedMap <Tensor, long>();
op_missing_tensor = new UnorderedMap <long, long>();
}
public BackpropInitialState PrepareBackprop(Tensor[] target,
TensorTape tensor_tape,
OpTape op_tape,
UnorderedSet <Tensor> sources_set,
bool persistent_tape)
{
BackpropInitialState result = new BackpropInitialState();
var tensor_stack = new Queue <Tensor>(target);
while (tensor_stack.Count > 0)
{
var tensor_id = tensor_stack.Dequeue();
if (!tensor_tape.find(tensor_id, out var op_id))
{
continue;
}
if (op_id == -1 ||
!op_tape.find(op_id, out var op_it) ||
result.op_tape.find(op_id, out var result_op_it))
{
continue;
}
result.op_tape.emplace(op_id, op_it);
foreach (var it in op_it.input_tensor_id)
{
if (result.tensor_usage_counts.find(it))
{
result.tensor_usage_counts[it]++;
}
else
{
result.tensor_usage_counts[it] = 1;
if (tensor_tape.find(it))
{
tensor_stack.Enqueue(it);
}
}
}
if (!persistent_tape)
{
op_tape.Remove(op_id);
}
}
foreach (var pair in result.tensor_usage_counts)
{
if (tensor_tape.find(pair.Key, out var it) && it != -1)
{
result.op_missing_tensor[it] += 1;
}
}
if (!persistent_tape)
{
// Call destructors for all unneeded gradient functions and
// clear the op_tape. We can clear the tape because ownership of
// backward functions that will be used for gradient computation
// has been transferred to `result`.
/*for (const auto&op_pair : *op_tape) {
* op_pair.second.backward_function_deleter(
* op_pair.second.backward_function);
* }*/
op_tape.Clear();
}
return(result);
}
public BackpropInitialState()
{
op_tape = new OpTape <BackwardFunction, TapeTensor>();
tensor_usage_counts = new UnorderedMap <long, long>();
op_missing_tensor = new UnorderedMap <long, long>();
}