def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
Returns:
An `Optional` that wraps `value`.
"""
# TODO(b/110122868): Consolidate this destructuring logic with the
# similar code in `Dataset.from_tensors()`.
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value = nest.pack_sequence_as(value, [
sparse_tensor_lib.SparseTensor.from_value(t)
if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(value))
])
encoded_value = nest.flatten(sparse.serialize_sparse_tensors(value))
output_classes = sparse.get_classes(value)
output_shapes = nest.pack_sequence_as(
value, [t.get_shape() for t in nest.flatten(value)])
output_types = nest.pack_sequence_as(
value, [t.dtype for t in nest.flatten(value)])
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
output_shapes, output_types, output_classes)
def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
Returns:
An `Optional` that wraps `value`.
"""
# TODO(b/110122868): Consolidate this destructuring logic with the
# similar code in `Dataset.from_tensors()`.
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value = nest.pack_sequence_as(value, [
sparse_tensor_lib.SparseTensor.from_value(t)
if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(value))
])
encoded_value = nest.flatten(sparse.serialize_sparse_tensors(value))
output_classes = sparse.get_classes(value)
output_shapes = nest.pack_sequence_as(
value, [t.get_shape() for t in nest.flatten(value)])
output_types = nest.pack_sequence_as(
value, [t.dtype for t in nest.flatten(value)])
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
output_shapes, output_types, output_classes)
def _capture_helper(self, tensor, name):
if (tensor.graph is not self._forward_graph or
any(tensor is t for t in self._forward_graph.inputs) or
any(tensor is t for t in self._forward_graph.outputs)):
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
if control_flow_util.GraphOrParentsInXlaContext(ops.get_default_graph()):
# XLA does not yet support optionals, so capture intermediates directly.
# TODO(skyewm,jpienaar): can XLA support optionals?
if tensor not in self.external_captures:
self.xla_intermediates.append(tensor)
self.op_needs_rewrite = True
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
tensor_id = ops.tensor_id(tensor)
captured_tensor = self._indirect_captures.get(tensor_id)
if captured_tensor is not None:
return captured_tensor
# 'tensor' is an uncaptured intermediate in the forward graph.
# If it is not a resource, we wrap it in an optional in the forward graph
# and capture the optional normally. We then unwrap the captured optional
# value in the gradient graph to get the raw intermediate value.
# If it is a resource, we trace the resource upto the input in the forward
# graph and capture that.
if tensor.dtype == dtypes.resource:
# Index of the forward graph input corresponding to the resource tensor.
index = util.resource_input_index(
tensor.name, [t.name for t in self._forward_graph.inputs],
{op.name: op.node_def for op in self._forward_graph.get_operations()},
self._forward_graph._functions)
# This gets mapped to the corresponding If op input in
# `_resolve_grad_inputs`.
captured_tensor = super(_CondGradFuncGraph, self)._capture_helper(
self._forward_graph.inputs[index], name)
else:
if tensor_id not in self._wrapped_intermediates:
# If the gradient has already been computed for this If op, 'tensor' may
# already be wrapped.
for consumer in tensor.consumers():
if (consumer.type == "OptionalFromValue" and
consumer.outputs[0] in self._forward_graph.outputs):
optional = consumer.outputs[0]
break
else:
# 'tensor' hasn't been wrapped, do it now.
with self._forward_graph.as_default():
optional = gen_dataset_ops.optional_from_value([tensor])
self.op_needs_rewrite = True
self._wrapped_intermediates[tensor_id] = optional
optional = self._wrapped_intermediates[tensor_id]
captured_optional = super(_CondGradFuncGraph,
self)._capture_helper(optional, name)
captured_tensor = gen_dataset_ops.optional_get_value(
captured_optional, [tensor.dtype], [tensor.shape])[0]
self._indirect_captures[tensor_id] = captured_tensor
return captured_tensor
def _capture_helper(self, tensor, name):
if (tensor.graph is not self._forward_graph or
tensor in self._forward_graph.inputs or
tensor in self._forward_graph.outputs):
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
if control_flow_util.GraphOrParentsInXlaContext(ops.get_default_graph()):
# XLA does not yet support optionals, so capture intermediates directly.
# TODO(skyewm,jpienaar): can XLA support optionals?
if tensor not in self.captures:
self.xla_intermediates.append(tensor)
self.if_op_needs_rewrite = True
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# 'tensor' is an uncaptured intermediate in the forward graph.
# If it is not a resource, we wrap it in an optional in the forward graph
# and capture the optional normally. We then unwrap the captured optional
# value in the gradient graph to get the raw intermediate value.
# If it is a resource, we trace the resource upto the input in the forward
# graph and capture that.
if tensor.dtype == dtypes.resource:
# Index of the forward graph input corresponding to the resource tensor.
index = util.resource_input_index(
tensor.name, [t.name for t in self._forward_graph.inputs],
{op.name: op.node_def for op in self._forward_graph.get_operations()},
self._forward_graph._functions)
# This gets mapped to the corresponding If op input in
# `_resolve_grad_inputs`.
captured_tensor = super(_CondGradFuncGraph, self)._capture_helper(
self._forward_graph.inputs[index], name)
else:
if tensor not in self._wrapped_intermediates:
# If the gradient has already been computed for this If op, 'tensor' may
# already be wrapped.
for consumer in tensor.consumers():
if (consumer.type == "OptionalFromValue" and
consumer.outputs[0] in self._forward_graph.outputs):
optional = consumer.outputs[0]
break
else:
# 'tensor' hasn't been wrapped, do it now.
with self._forward_graph.as_default():
optional = gen_dataset_ops.optional_from_value([tensor])
self.if_op_needs_rewrite = True
self._wrapped_intermediates[tensor] = optional
optional = self._wrapped_intermediates[tensor]
captured_optional = super(_CondGradFuncGraph,
self)._capture_helper(optional, name)
captured_tensor = gen_dataset_ops.optional_get_value(
captured_optional, [tensor.dtype], [tensor.shape])[0]
self._indirect_captures[tensor] = captured_tensor
return captured_tensor
def _capture_helper(self, tensor, name):
if (tensor.graph is not self._forward_graph
or tensor in self._forward_graph.inputs
or tensor in self._forward_graph.outputs):
return super(_CondGradFuncGraph,
self)._capture_helper(tensor, name)
if control_flow_util.InXlaContext(ops.get_default_graph()):
# XLA does not yet support optionals, so capture intermediates directly.
# TODO(skyewm,jpienaar): can XLA support optionals?
if tensor not in self.captures:
self.xla_intermediates.append(tensor)
self.if_op_needs_rewrite = True
return super(_CondGradFuncGraph,
self)._capture_helper(tensor, name)
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# 'tensor' is an uncaptured intermediate in the forward graph. We wrap it in
# an optional in the forward graph and capture the optional normally. We
# then unwrap the captured optional value in the gradient graph to get the
# raw intermediate value.
if tensor not in self._wrapped_intermediates:
# If the gradient has already been computed for this If op, 'tensor' may
# already be wrapped.
for consumer in tensor.consumers():
if (consumer.type == "OptionalFromValue" and
consumer.outputs[0] in self._forward_graph.outputs):
optional = consumer.outputs[0]
break
else:
# 'tensor' hasn't been wrapped, do it now.
with self._forward_graph.as_default():
optional = gen_dataset_ops.optional_from_value([tensor])
self.if_op_needs_rewrite = True
self._wrapped_intermediates[tensor] = optional
optional = self._wrapped_intermediates[tensor]
captured_optional = super(_CondGradFuncGraph,
self)._capture_helper(optional, name)
captured_tensor = gen_dataset_ops.optional_get_value(
captured_optional, [tensor.dtype], [tensor.shape])[0]
self._indirect_captures[tensor] = captured_tensor
return captured_tensor
def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
Returns:
An `Optional` that wraps `value`.
"""
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value_structure = type_spec.type_spec_from_value(value)
encoded_value = value_structure._to_tensor_list(value) # pylint: disable=protected-access
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
value_structure)
def _capture_helper(self, tensor, name):
if (tensor.graph is not self._forward_graph or
tensor in self._forward_graph.inputs or
tensor in self._forward_graph.outputs):
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
if control_flow_util.InXlaContext(ops.get_default_graph()):
# XLA does not yet support optionals, so capture intermediates directly.
# TODO(skyewm,jpienaar): can XLA support optionals?
if tensor not in self.captures:
self.xla_intermediates.append(tensor)
self.if_op_needs_rewrite = True
return super(_CondGradFuncGraph, self)._capture_helper(tensor, name)
captured_tensor = self._indirect_captures.get(tensor)
if captured_tensor is not None:
return captured_tensor
# 'tensor' is an uncaptured intermediate in the forward graph. We wrap it in
# an optional in the forward graph and capture the optional normally. We
# then unwrap the captured optional value in the gradient graph to get the
# raw intermediate value.
if tensor not in self._wrapped_intermediates:
# If the gradient has already been computed for this If op, 'tensor' may
# already be wrapped.
for consumer in tensor.consumers():
if (consumer.type == "OptionalFromValue"
and consumer.outputs[0] in self._forward_graph.outputs):
optional = consumer.outputs[0]
break
else:
# 'tensor' hasn't been wrapped, do it now.
with self._forward_graph.as_default():
optional = gen_dataset_ops.optional_from_value([tensor])
self.if_op_needs_rewrite = True
self._wrapped_intermediates[tensor] = optional
optional = self._wrapped_intermediates[tensor]
captured_optional = super(_CondGradFuncGraph, self)._capture_helper(
optional, name)
captured_tensor = gen_dataset_ops.optional_get_value(
captured_optional, [tensor.dtype], [tensor.shape])[0]
self._indirect_captures[tensor] = captured_tensor
return captured_tensor
def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
Returns:
An `Optional` that wraps `value`.
"""
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value_structure = structure.Structure.from_value(value)
encoded_value = value_structure._to_tensor_list(value) # pylint: disable=protected-access
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
value_structure)
def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A value to wrap. The value must be convertible to `Tensor` or
`CompositeTensor`.
Returns:
An `Optional` that wraps `value`.
"""
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value_structure = structure.type_spec_from_value(value)
encoded_value = structure.to_tensor_list(value_structure, value)
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
value_structure)
def from_value(value):
"""Returns a `tf.experimental.Optional` that wraps the given value.
>>> optional = tf.experimental.Optional.from_value(42)
>>> print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)
>>> print(optional.get_value())
tf.Tensor(42, shape=(), dtype=int32)
Args:
value: A value to wrap. The value must be convertible to `tf.Tensor` or
`tf.CompositeTensor`.
Returns:
A `tf.experimental.Optional` that wraps `value`.
"""
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
element_spec = structure.type_spec_from_value(value)
encoded_value = structure.to_tensor_list(element_spec, value)
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
element_spec)
def _wrap_intermediates(func_graph, intermediates):
with func_graph.as_default():
return [
gen_dataset_ops.optional_from_value([t]) for t in intermediates
]
def _OptionalGetValueGrad(unused_op, *grads):
return gen_dataset_ops.optional_from_value(grads)
def _OptionalGetValueGrad(unused_op, *grads): return gen_dataset_ops.optional_from_value(grads)
def _wrap_intermediates(func_graph, intermediates):
with func_graph.as_default():
return [gen_dataset_ops.optional_from_value([t]) for t in intermediates]