def _build_cond(pred,
true_graph,
false_graph,
true_inputs,
false_inputs,
building_gradient,
name=None):
"""Creates an If op from the specified predicate, branch functions and inputs.
Note that this modifies true_graph and false_graph to make the inputs match,
and to output all intermediates values so they're available for the gradient
computation.
true_graph and false_graph need not have the same input types, but they must
have the same outpute types.
Args:
pred: boolean Tensor
true_graph: FuncGraph
false_graph: FuncGraph
true_inputs: a list of Tensors to be passed to true_graph as input.
false_inputs: a list of Tensors to be passed to false_graph as input.
building_gradient: Whether this is a gradient If op.
name: the name for the If op.
Returns:
A list of Tensors which are the outputs of the If op. Does not include added
intermediate outputs.
"""
_make_indexed_slices_indices_types_match(_COND, [true_graph, false_graph])
_check_same_outputs(_COND, [true_graph, false_graph])
# Add inputs to true_graph and false_graph to make them match. Note that
# this modifies true_graph and false_graph.
cond_inputs = _make_inputs_match([true_graph, false_graph],
[true_inputs, false_inputs])
# Save the original number of outputs to return to the caller.
num_cond_outputs = len(true_graph.outputs)
# We do not output intermediates of the gradient If op since this is just
# for backwards compatibility with existing code.
if not building_gradient and util.output_all_intermediates():
# Add all intermediate tensors as function outputs so they're available for
# the gradient computation. Since the outputs of the two functions must
# match, we wrap all the intermediates in optionals. Each intermediate
# output will have a value iff its corresponding branch is taken.
true_intermediates = _get_intermediates(true_graph)
false_intermediates = _get_intermediates(false_graph)
# Wrap intermediates in optionals.
wrapped_true_intermediates = _wrap_intermediates(
true_graph, true_intermediates)
wrapped_false_intermediates = _wrap_intermediates(
false_graph, false_intermediates)
# Make outputs match by adding none optionals.
extra_true_outputs, extra_false_outputs = _make_intermediates_match( # pylint: disable=unbalanced-tuple-unpacking
[true_graph, false_graph],
[wrapped_true_intermediates, wrapped_false_intermediates])
true_graph.outputs.extend(extra_true_outputs)
false_graph.outputs.extend(extra_false_outputs)
_check_same_outputs(_COND, [true_graph, false_graph])
# Create the If op.
with ops.control_dependencies(
list(true_graph.control_captures) +
list(false_graph.control_captures)):
true_stateful_ops = [
op for op in true_graph.get_operations() if op._is_stateful
]
false_stateful_ops = [
op for op in false_graph.get_operations() if op._is_stateful
]
if (true_stateful_ops or false_stateful_ops):
op_fn = gen_functional_ops._if
else:
op_fn = gen_functional_ops.stateless_if
tensors = op_fn(pred,
cond_inputs, [t.dtype for t in true_graph.outputs],
util.create_new_tf_function(true_graph),
util.create_new_tf_function(false_graph),
output_shapes=_get_output_shapes(
true_graph.outputs, false_graph.outputs),
name=name)
# TODO(b/110167197) this approach requires cond_v2 to have at least 1 output
if_op = tensors[0].op
if_op._true_graph = true_graph
if_op._false_graph = false_graph
util.maybe_set_lowering_attr(if_op)
util.maybe_propagate_compile_time_consts_in_xla(if_op)
# Return identities for each output of the If op, rather than the output of
# the If op directly. This makes pruning work if the output of cond() is
# fetched: the lowering pass converts the If outputs into IdentityN outputs,
# which if fetched will cause all ops in the taken branch to be run (since
# it takes all merge ops as input). After lowering, each output identity op
# will end up with only the appropriate merge op as input.
# TODO(b/79984175): this doesn't have to be a tuple once we covert to the
# correct output structure
tensors = [array_ops.identity(t) for t in tensors]
# Prevent fetching since the variant outputs can't be fetched directly.
if_op.graph.prevent_fetching(if_op)
return func_graph_module.pack_sequence_as(true_graph.structured_outputs,
tensors[:num_cond_outputs])
def while_loop(cond,
body,
loop_vars,
shape_invariants=None,
parallel_iterations=10,
maximum_iterations=None,
name=None,
return_same_structure=True,
back_prop=True):
"""Like tf.while_loop, except emits a single While op."""
# Keep the original loop_vars around to know which args were TensorArrays.
orig_loop_vars = loop_vars
# Cache its length since we use it at multiple places below.
len_orig_loop_vars = len(orig_loop_vars)
# Convert TensorArrays to their flow variables. These get converted back to
# TensorArrays before calling `cond` and `body`. See `wrapped_cond` and
# `wrapped_body` below.
loop_vars = list(_tensor_array_to_flow(orig_loop_vars))
loop_vars = nest.map_structure(
ops.internal_convert_to_tensor_or_indexed_slices, loop_vars,
expand_composites=True)
if shape_invariants is not None:
nest.assert_same_structure(orig_loop_vars, shape_invariants,
expand_composites=False)
signature = nest.map_structure(
control_flow_ops._shape_invariant_to_type_spec, loop_vars,
list(shape_invariants), expand_composites=False)
shape_invariants = nest.map_structure(
control_flow_ops._get_shape_invariant, loop_vars,
list(shape_invariants), expand_composites=False)
else:
signature = nest.map_structure(
type_spec.type_spec_from_value, loop_vars, expand_composites=False)
shape_invariants = nest.map_structure(
control_flow_ops._get_shape_invariant, loop_vars,
expand_composites=False)
if not name:
name = "while"
with ops.name_scope(name) as scope:
with ops.name_scope(None):
cond_name = util.unique_fn_name(scope, "cond")
body_name = util.unique_fn_name(scope, "body")
maximum_iterations_loop_var = _build_maximum_iterations_loop_var(
maximum_iterations)
loop_counter = constant_op.constant(
0,
dtype=maximum_iterations_loop_var.dtype
if maximum_iterations is not None else None,
name="loop_counter")
# Add loop counter needed for computing gradients.
loop_vars = [loop_counter, maximum_iterations_loop_var] + loop_vars
shape_invariants = [tensor_shape.TensorShape([])] * 2 + shape_invariants
signature = (
[tensor_spec.TensorSpec.from_tensor(loop_counter),
tensor_spec.TensorSpec.from_tensor(maximum_iterations_loop_var)] +
signature)
# Automatic control dependencies are added in defuns, but not in v1
# graphs. Propagate that behavior here.
add_control_dependencies = ops.get_default_graph()._add_control_dependencies
def wrapped_cond(loop_counter, maximum_iterations_arg, *args):
"""Extra `cond` wrapper that can handle the extra counter loop_var."""
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
pred = cond(*_pack_sequence_as(orig_loop_vars, args))
if (tensor_util.is_tensor(pred) and
(pred.shape.dims is None or pred.shape.dims)):
pred = array_ops.squeeze_v2(pred)
if maximum_iterations is None:
return pred
else:
return math_ops.logical_and(
loop_counter < maximum_iterations_arg, pred)
# NOTE(skyewm): we set collections to the outer graph's collections for
# compatibility with TPUEstimator.
cond_graph = func_graph_module.func_graph_from_py_func(
cond_name,
wrapped_cond,
[], # We provide signature instead of args.
{},
signature=signature,
func_graph=util.WhileCondFuncGraph(
cond_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies)
def wrapped_body(loop_counter, maximum_iterations_arg, *args):
"""Loop body augmented with counter update.
Args:
loop_counter: Loop counter which needs to be incremented in the body.
maximum_iterations_arg: Maximum iterations of the loop.
*args: List of args
Returns:
A list of tensors the same length as args.
"""
# Capture the tensors already captured in cond_graph so that they appear
# in the same order in body_graph.external_captures.
for t in cond_graph.external_captures:
ops.get_default_graph().capture(t)
# Convert the flow variables in `args` to TensorArrays. `args` should
# already have the same structure as `orig_loop_vars` but currently there
# is no nest.zip so we call `_pack_sequence_as` which flattens both
# `orig_loop_vars` and `args`, converts flows in `args` to TensorArrays
# and packs it into the structure of `orig_loop_vars`.
outputs = body(*_pack_sequence_as(orig_loop_vars, args))
if not nest.is_sequence_or_composite(outputs):
outputs = [outputs]
# Compare the structure of input and output of body converting the
# top-level tuples to list to be compatible with legacy while_loop.
nest.assert_same_structure(list(outputs), list(orig_loop_vars),
expand_composites=True)
outputs = _tensor_array_to_flow(outputs)
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
return [loop_counter + 1, maximum_iterations_arg] + list(outputs)
body_graph = func_graph_module.func_graph_from_py_func(
body_name,
wrapped_body,
[], # We provide signature instead of args.
{},
signature=signature,
func_graph=util.WhileBodyFuncGraph(
body_name, collections=ops.get_default_graph()._collections), # pylint: disable=protected-access
add_control_dependencies=add_control_dependencies)
# Add external captures of body to the list of loop vars.
# Note that external tensors will be treated as loop invariants, i.e.,
# the value of that tensor in each iteration is the same as it was at the
# beginning of the loop execution.
loop_vars = loop_vars + body_graph.external_captures
# TODO(srbs): Update lowering code to create _Enter nodes with
# is_constant=True for inputs that are directly passed to outputs.
body_graph.outputs.extend(body_graph.internal_captures)
# Capture the extra `external_captures` of `body_graph` in `cond_graph` so
# that it expects to receive those as arguments.
with cond_graph.as_default():
num_cond_captures = len(cond_graph.external_captures)
assert (cond_graph.external_captures ==
body_graph.external_captures[:num_cond_captures])
cond_graph_captures = object_identity.ObjectIdentitySet(
cond_graph.external_captures)
for body_capture in body_graph.external_captures[num_cond_captures:]:
assert body_capture not in cond_graph_captures
cond_graph.capture(body_capture)
# Make sure that the shapes of the loop outputs are compatible with the
# shape invariants, or the shapes of the loop vars if the invariants are not
# specified.
num_flattened_outputs = len(nest.flatten(orig_loop_vars,
expand_composites=True))
# First var is loop counter and second var is maximum_iterations.
first_loop_var_index = 2
_check_shapes_compat(
body_graph.outputs[first_loop_var_index:first_loop_var_index +
num_flattened_outputs],
nest.flatten(
shape_invariants[first_loop_var_index:first_loop_var_index +
len_orig_loop_vars], expand_composites=True),
nest.flatten(loop_vars[first_loop_var_index:first_loop_var_index +
len_orig_loop_vars], expand_composites=True))
num_original_outputs = len(body_graph.outputs)
if back_prop and util.output_all_intermediates():
# Export all tensors in the loop body that may be needed for gradient
# computation. We do this by accumulating the intermediate values in
# TensorLists.
intermediate_tensors = _get_intermediates(body_graph)
for intermediate_tensor in intermediate_tensors:
tensor_list = list_ops.empty_tensor_list(
element_dtype=intermediate_tensor.dtype,
element_shape=intermediate_tensor.shape,
max_num_elements=maximum_iterations)
loop_vars.append(tensor_list)
with cond_graph.as_default():
# Add a placeholder to cond_graph's inputs corresponding to the
# tensor_list.
cond_graph.capture(tensor_list)
with body_graph.as_default():
# Push the intermediate tensor to the tensor list. This captures the
# `tensor_list` as well.
appended_tensor_list = list_ops.tensor_list_push_back(
tensor_list, intermediate_tensor)
# Add this modified tensor list to the list of outputs.
body_graph.outputs.append(appended_tensor_list)
flattened_loop_vars = nest.flatten(loop_vars, expand_composites=True)
_check_num_inputs_outputs(cond_graph, body_graph,
len(flattened_loop_vars))
_check_inputs_outputs_types_match(body_graph, flattened_loop_vars)
with ops.control_dependencies(
list(cond_graph.control_captures) + list(body_graph.control_captures)):
output_shapes = [t.shape for t in body_graph.outputs]
orig_loop_vars_range = slice(first_loop_var_index,
first_loop_var_index + num_flattened_outputs)
output_shapes[orig_loop_vars_range] = nest.flatten(
shape_invariants, expand_composites=True)[orig_loop_vars_range]
cond_stateful_ops = [
op for op in cond_graph.get_operations() if op._is_stateful
]
body_stateful_ops = [
op for op in body_graph.get_operations() if op._is_stateful
]
if (cond_stateful_ops or body_stateful_ops):
op_fn = gen_functional_ops._while
else:
op_fn = gen_functional_ops.stateless_while
outputs = op_fn(
flattened_loop_vars,
util.create_new_tf_function(cond_graph),
util.create_new_tf_function(body_graph),
output_shapes=output_shapes,
parallel_iterations=parallel_iterations,
name=scope)
# This is needed so we do not compute derivative wrt these extra outputs.
outputs[0].op._set_attr("_num_original_outputs",
attr_value_pb2.AttrValue(i=num_original_outputs))
_copy_handle_data(body_graph.outputs, outputs)
util.maybe_set_lowering_attr(outputs[0].op)
util.maybe_propagate_compile_time_consts_in_xla(outputs[0].op)
# Return identities for each output of the While op, rather than the output
# of the While op directly. This makes pruning work if the output of
# while_loop() is fetched: the lowering pass converts the While outputs into
# IdentityN outputs, which if fetched will cause all ops in the body to be
# run (since it takes all exit ops as input). After lowering, each output
# identity op will end up with only the appropriate exit op as input.
outputs = tuple(array_ops.identity(t) for t in outputs)
outputs = _pack_sequence_as(
orig_loop_vars, outputs[first_loop_var_index:first_loop_var_index +
num_flattened_outputs])
if return_same_structure:
return outputs
flattened_outputs = nest.flatten(outputs, expand_composites=True)
if len(flattened_outputs) == 1:
return flattened_outputs[0]
else:
return outputs
def _build_cond(pred,
true_graph,
false_graph,
true_inputs,
false_inputs,
building_gradient,
name=None):
"""Creates an If op from the specified predicate, branch functions and inputs.
Note that this modifies true_graph and false_graph to make the inputs match,
and to output all intermediates values so they're available for the gradient
computation.
true_graph and false_graph need not have the same input types, but they must
have the same outpute types.
Args:
pred: boolean Tensor
true_graph: FuncGraph
false_graph: FuncGraph
true_inputs: a list of Tensors to be passed to true_graph as input.
false_inputs: a list of Tensors to be passed to false_graph as input.
building_gradient: Whether this is a gradient If op.
name: the name for the If op.
Returns:
A list of Tensors which are the outputs of the If op. Does not include added
intermediate outputs.
"""
_make_indexed_slices_indices_types_match(_COND, [true_graph, false_graph])
_check_same_outputs(_COND, [true_graph, false_graph])
# Add inputs to true_graph and false_graph to make them match. Note that
# this modifies true_graph and false_graph.
cond_inputs = _make_inputs_match([true_graph, false_graph],
[true_inputs, false_inputs])
# We do not output intermediates of the gradient If op since this is just
# for backwards compatibility with existing code.
if not building_gradient and util.output_all_intermediates():
# Add all intermediate tensors as function outputs so they're available for
# the gradient computation. Since the outputs of the two functions must
# match, we wrap all the intermediates in optionals. Each intermediate
# output will have a value iff its corresponding branch is taken.
true_intermediates = _get_intermediates(true_graph)
false_intermediates = _get_intermediates(false_graph)
# Wrap intermediates in optionals.
wrapped_true_intermediates = _wrap_intermediates(true_graph,
true_intermediates)
wrapped_false_intermediates = _wrap_intermediates(false_graph,
false_intermediates)
# Make outputs match by adding none optionals.
extra_true_outputs, extra_false_outputs = _make_intermediates_match( # pylint: disable=unbalanced-tuple-unpacking
[true_graph, false_graph],
[wrapped_true_intermediates, wrapped_false_intermediates])
true_graph.outputs.extend(extra_true_outputs)
false_graph.outputs.extend(extra_false_outputs)
_check_same_outputs(_COND, [true_graph, false_graph])
# Create the If op.
with ops.control_dependencies(
list(true_graph.control_captures) + list(false_graph.control_captures)):
true_stateful_ops = [
op for op in true_graph.get_operations() if op._is_stateful
]
false_stateful_ops = [
op for op in false_graph.get_operations() if op._is_stateful
]
if (true_stateful_ops or false_stateful_ops):
op_fn = gen_functional_ops._if
else:
op_fn = gen_functional_ops.stateless_if
def _make_op(inputs):
if_op, tensors = util.get_op_and_outputs(op_fn(
pred,
inputs, [t.dtype for t in true_graph.outputs],
util.create_new_tf_function(true_graph),
util.create_new_tf_function(false_graph),
output_shapes=_get_output_shapes(true_graph.outputs,
false_graph.outputs),
name=name))
_copy_handle_data(tensors, true_graph.outputs, false_graph.outputs)
# `if_op` is None if this is a `StatelessIf` op with no outputs.
if if_op is not None:
# The true and false graphs have already been created, and we need that
# to happen before we know which tensors will be captured and so whether
# to wrap the cond in a tf.function. Post-hoc mutation of the branch
# `outer_graph` properties seems like the only option if we want to
# conditionally wrap in a function.
true_graph.outer_graph = ops.get_default_graph()
false_graph.outer_graph = ops.get_default_graph()
if_op._true_graph = true_graph
if_op._false_graph = false_graph
util.maybe_set_lowering_attr(if_op)
util.maybe_propagate_compile_time_consts_in_xla(if_op)
_set_read_only_resource_inputs_attr(if_op, [true_graph, false_graph])
# Prevent fetching since the variant outputs can't be fetched directly.
if_op.graph.prevent_fetching(if_op)
return tensors
tensors = util.run_as_function_for_tape_gradients(_make_op, cond_inputs)
# Return identities for each output of the If op, rather than the output of
# the If op directly. This makes pruning work if the output of cond() is
# fetched: the lowering pass converts the If outputs into IdentityN outputs,
# which if fetched will cause all ops in the taken branch to be run (since
# it takes all merge ops as input). After lowering, each output identity op
# will end up with only the appropriate merge op as input.
# TODO(b/79984175): this doesn't have to be a tuple once we covert to the
# correct output structure
tensors = [array_ops.identity(t) for t in tensors]
return _pack_sequence_as(true_graph.structured_outputs, tensors)
