def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# Resource tensors are not accumulated and handled specially.
if tensor.dtype == dtypes.resource:
return self._resource_capture_helper(tensor)
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
#
# Note: We clear the control dependencies to avoid a cycle in case a
# control tensor has an input path to an output of the forward While.
#
# E.g.:
# x = tf.while_loop(...)
# y = f(x)
# with tf.control_dependencies([y]):
# tf.gradients(y, x)
#
# Since the EmptyTensorList is fed back into the forward While, not
# removing the control edge would cause a cycle.
with self._forward_graph.outer_graph.as_default():
with util.clear_control_inputs():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations,
name=_build_accumulator_name(tensor))
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(
tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph,
self)._capture_helper(accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[tensor] = captured_tensor
self.popped_tensor_lists[captured_accumulator] = new_tensor_list
return captured_tensor
def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph, self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(ops.tensor_id(tensor))
if captured_tensor is not None:
return captured_tensor
# Do not accumulate loop invariants.
if (any(tensor is t for t in self._forward_graph.inputs) and
any(tensor is t for t in self._forward_graph.outputs)):
captured_tensor = super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
# Add to `popped_tensor_lists` so that this gets added to the list of
# outputs.
# TODO(srbs): Rename popped_tensor_lists.
self.popped_tensor_lists[ops.tensor_id(captured_tensor)] = captured_tensor
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
return captured_tensor
# Do not accumulate Const nodes. Instead copy them directly in the backward
# graph.
# TODO(srbs): This just checks for `Const` nodes. Consider checking for
# graph compile time consts in general.
# TODO(srbs): Consider making this a loop input.
if constant_op.is_constant(tensor):
real_value = constant_op.constant(
tensor_util.constant_value(tensor), dtype=tensor.dtype)
self._indirect_captures[ops.tensor_id(tensor)] = real_value
return real_value
# Resource tensors are not accumulated and handled specially.
if tensor.dtype == dtypes.resource:
return self._resource_capture_helper(tensor)
# No need to accumulate loop invariants. Capture them directly.
# The captured tensor gets resolved to the corresponding while output in
# `_resolve_grad_captures`.
if _is_loop_invariant(tensor, self._forward_graph_inputs,
self._forward_graph_outputs):
captured_tensor = super(_WhileBodyGradFuncGraph,
self)._capture_helper(tensor, name)
return captured_tensor
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
#
# Note: We clear the control dependencies to avoid a cycle in case a
# control tensor has an input path to an output of the forward While.
#
# E.g.:
# x = tf.while_loop(...)
# y = f(x)
# with tf.control_dependencies([y]):
# tf.gradients(y, x)
#
# Since the EmptyTensorList is fed back into the forward While, not
# removing the control edge would cause a cycle.
with self._forward_graph.outer_graph.as_default():
with util.clear_control_inputs():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations,
name=_build_accumulator_name(tensor))
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph, self)._capture_helper(
accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[ops.tensor_id(tensor)] = captured_tensor
self.popped_tensor_lists[ops.tensor_id(
captured_accumulator)] = new_tensor_list
return captured_tensor
def _capture_helper(self, tensor, name):
if tensor.graph is not self._forward_graph:
return super(_WhileBodyGradFuncGraph, self)._capture_helper(tensor, name)
while tensor.op.type == "Identity":
# We do not accumulate the output of identity nodes so we try to capture
# the input of the Identity node instead.
tensor = tensor.op.inputs[0]
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# Resource tensors are not accumulated and handled specially.
if tensor.dtype == dtypes.resource:
return self._resource_capture_helper(tensor)
# Create or find an existing accumulator output for `tensor` in the forward
# graph, and fetch from this accumulator in the gradient graph to get the
# raw intermediate value.
accumulator = _get_accumulator(tensor)
if accumulator is None:
# Create the initial empty tensor list.
#
# Note: We clear the control dependencies to avoid a cycle in case a
# control tensor has an input path to an output of the forward While.
#
# E.g.:
# x = tf.while_loop(...)
# y = f(x)
# with tf.control_dependencies([y]):
# tf.gradients(y, x)
#
# Since the EmptyTensorList is fed back into the forward While, not
# removing the control edge would cause a cycle.
with self._forward_graph.outer_graph.as_default():
with util.clear_control_inputs():
tensor_list = list_ops.empty_tensor_list(
element_dtype=tensor.dtype,
element_shape=tensor.shape,
max_num_elements=self._maximum_iterations,
name=_build_accumulator_name(tensor))
self.empty_tensor_lists.append(tensor_list)
# Push the intermediate tensor to the tensor list. This captures
# `tensor_list`.
with self._forward_graph.as_default():
accumulator = list_ops.tensor_list_push_back(tensor_list, tensor)
# Add the modified tensor list to the list of outputs. This output will be
# all the accumulated values.
self._forward_graph.outputs.append(accumulator)
# Capture in the cond graph as well so the forward cond and body inputs
# match.
with self._forward_cond_graph.as_default():
self._forward_cond_graph.capture(tensor_list)
# Capture the accumulator tensor list in the gradient graph directly from
# the forward graph -- we'll later modify this to capture the final list
# output by the forward While op instead.
captured_accumulator = super(_WhileBodyGradFuncGraph, self)._capture_helper(
accumulator, name)
# Pop the intermediate value from the tensor list in the gradient graph.
new_tensor_list, captured_tensor = list_ops.tensor_list_pop_back(
captured_accumulator, element_dtype=tensor.dtype)
self._indirect_captures[tensor] = captured_tensor
self.popped_tensor_lists[captured_accumulator] = new_tensor_list
return captured_tensor
